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Preface
This Doctoral thesis work has carried out in the Department of Communica-
tion and Networking at Aalto University School of Electrical Engineering, Es-
poo, Finland. The Academy of Finland, Ericsson, and Cassidian have funded
this research work.
First, I would like to highly appreciate and express my sincere and utmost
gratitude to my supervisor, Prof. Jyri Hämäläinen for his all-time generous
unceasing support and continous encouragements throughout my research
activities in his group. I would like to mention that Prof. Jyri Hämäläinen is
the one who open the door of research world for me and due to his honest
guidance; I was able to achieve this big milestone of my life.
I would like to say my special thanks to my thesis instructor, Dr. Edward
Mutafungwa for his all-time available support and help during my doctoral
research work, during which we had done several informative discussions as
well as help me in writing/publication of all my research manuscripts. His
expertise enable an opportunity to polish my technical writing and presenta-
tion skills to present research contributions.
I am thankful to Dr. Zhong Zheng for his utmost support and patience who
provide me a continous guidance in solving problems at all phases of my re-
search work in master and doctoral studies.
I am thankful to Dr. Alexis Dowhuszko for his cooperative approach and
very informative feedback on various research issues throughout my research
studies, even in his busy schedules.
I would like to say my special gratitude to Dr. David González González
for his politeness, humbleness and being continously helpful at every phase
of my research work.
I extend my thanks to Udesh Oruthota, Haile Beneyam, Konstantinos Koufos,
Aamir Mahmood, and Umar Saeed for their tips and suggestions throughout
my research phases.
i
Preface
I would like to acknowledge and highly appreciate the efforts and help of the
support team during my whole work duration in Aalto University. I am grate-
ful and thankful to Viktor Nässi for his all-time any-time welcoming smile and
cooperation. I am really thankful to Marja Leppäharju, Kati Voutilainen, Mari
Paloheimo, Liukko Heli, Haaranen Sirpa, Rinne Essi, Patana Sanna, Kiveliö
Sari, Hietala Juhapekka, Laaksonen Joni and Lehtola Timi for their utmost
and all time support during my stay in Aalto University.
Espoo, Finland, June 7, 2018,
Inam Ullah
ii
Contents
Preface i
Contents iii
List of Abbreviations and Symbols v
1. Introduction 1
1.1 Motivation and problem definition . . . . . . . . . . . . . . . . 1
1.2 Scope of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Summary of Thesis Contributions and Publications . . . . . . 4
1.4 Author’s Contributions . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . 6
2. Relaying in LTE-Advanced Systems and Beyond 7
2.1 3GPP LTE, LTE-Advanced and LTE-A Pro Systems . . . . . . . 9
2.2 Relaying Principles and Classifications . . . . . . . . . . . . . . 15
2.3 LTE-A Relaying System . . . . . . . . . . . . . . . . . . . . . . . 21
2.4 Relaying beyond LTE-A . . . . . . . . . . . . . . . . . . . . . . 25
3. Resource Optimal Relaying 31
3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2 Previous work and Contributions . . . . . . . . . . . . . . . . . 31
3.3 System Model and Resource Allocation Schemes . . . . . . . . 34
3.4 Performance analysis . . . . . . . . . . . . . . . . . . . . . . . . 40
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4. Practical Interference Mitigation for the Relay Backhaul Link 57
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2 Previous Work and Contributions . . . . . . . . . . . . . . . . . 58
4.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
iii
Contents
4.4 Analysis of SINR and e2e Outage Rate . . . . . . . . . . . . . . 66
4.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . 72
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5. Rapidly Deployable Relays for Indoor Environments 81
5.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . 81
5.2 Previous work and Contributions . . . . . . . . . . . . . . . . . 82
5.3 Description of the relay deployment cases . . . . . . . . . . . . 85
5.4 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.5 Performance Evaluation and Simulations . . . . . . . . . . . . 92
5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6. Conclusions and future work 101
6.1 Analysis of Optimal Resource Sharing . . . . . . . . . . . . . . 101
6.2 Interference Mitigation for the Relay Backhaul Link . . . . . . 102
6.3 Rapidly Deployable Relays for Outdoor-to-indoor Coverage . 103
References 105
iv
List of Abbreviations and Symbols
Abbreviations
1G First Generation
2G Second Generation
3G Third Generation
3D Three Dimensional
3GPP Third Generation Partnership Project
4G Fourth Generation
5G Fifth Generation
Ant Antenna
AF Amplify and Forward
AL Access Link
AP Access Point
AAS Active Antenna Systems
AS Antenna Selection
BER Bit Error Ratio
BF Beamforming
BS Base Station
BW Bandwidth
CA Carrier Aggregation
CAGR Compounded Annual Growth Rate
CDF Cumulative Distribution Function
CN Core Network
COST COopération européenne dans le domaine de la recherche Sci-
entifique et Technique
CoMP Coordinated Multipoint Transmission and Reception
CP Cyclic Prefix
CQI Channel Quality Information
v
List of Abbreviations and Symbols
CSI Channel State Information
D2D Device-to-Device
DAS Distributed Antenna Systems
DC Dual Connectivity
DF Decode and Forward
DeNB Donor Evolved Node-B
DLBF Dual-layer Beamforming
DL Downlink
DRX Discontinuous Reception
dB decibel
EBF Elevation Beamforming
EDGE Enhanced Data rates for GSM Evolution
EE Energy Efficiency
EH Energy Harvest
e2e End-to-End
eIMTA Enhanced International Mobile Telecommunication Ad-
vanced
eMBMS Evolved Multimedia broadcast Multicast Service
eNB Evolved Node-B
FBMC Filter Bank Multicarrier
FDD Frequency Division Duplex
FD Full Duplex
FD-MIMO Full-dimension MIMO
GPRS General Packet Radio Service
GSM Global System for Mobile Telephony
Gbps Giga bits per second
HD Half Duplex
Hetnet Heterogeneous Networks
HSPA High Speed Packet Access
Hz Hertz
ICIC Inter-Cell Interference Coordination
ICPWF Iterative Co-phasing Waterfilling
IL Implementation Loss
IM Interference Mitigation
IMT-2000 International Mobile Telecommunications-2000
IMT-A International Mobile Telecommunications-Advanced
IoT Internet of Things
IP Internet Protocol
vi
List of Abbreviations and Symbols
IRT Intelligent Ray Tracing
ISD Inter-Site Distance
ISI Inter-Symbol Interference
ITS Intelligent Transportation System
ITU-R International Telecommunication Union-Radio
kHz kilo Hertz
L1 Layer 1
L2 Layer 2
L3 Layer 3
LA Link Adaptation
LAA License Assisted Access
LoS Line-of-Sight
LPN Low Power Nodes
LTE-A Long Term Evolution-Advanced
LTE Long Term Evolution
LTE-A Pro Long Term Evolution-Advanced Pro
LWA LTE-WLAN Aggregation
MAC Media Access Control
MBS Macro Base Station
MBMS Multimedia Broadcast Multicast Services
MBSFN Multimedia Broadcast over Single Frequency Network
MHz Mega Hertz
MIMO Multiple-Input Multiple-Output
MME Mobility Management Entity
MMF Max-Min Fairness
MNO Mobile Network Operator
MRN Moving Relaying Node
MTC Massive Machine-Type Communication
MUE Macro User Equipment
MUST Multi-User Superposition Transmission
Mbps Megabits per second
mmW Millimeter Wave
msec Millisecond
NB-IoT Narrow-band Internet-of-Things
NLoS Non-Line-of-Sight
NOMA Non-Orthogonal Multiple Access
OFDM Orthogonal Frequency Division Modulation
OFDMA Orthogonal Frequency Division Multiple Access
vii
List of Abbreviations and Symbols
QCP Quantized Co-phasing
PAPR Peak to Average Power Ratio
PDF Probability Distribution Function
PHY Physical layer
PL Path Loss
PLMN Public Land Mobile Networks
PLR Packet Loss Ratio
PRB Physical Resource Block
ProSe Proximity Service
RAN Radio Access Network
REC Relay Enhanced Cellular
RF Radio Frequency
RL Relay Link
RN Relay Node
RRH Remote Radio Head
RRM Radio Resource Management
RR Round Robin
RS Receiver Selection
RUE Relay User Equipment
Rx Receive
S-GW Serving Gateway
SNR Signal-to-Noise-Ratio
SINR Signal-to-Interference-and-Noise-Ratio
SON Self Organizing Networks
TDD Time Division Duplex
TDMA Time Division Multiple Access
TP Throughput
TTI Transmit Time Interval
Tx Transmit
UDN Ultra Dense Networks
UE User Equipment
UL Uplink
UMTS Universal Mobile Telecommunications System
V2I Vehicle-to-Infrastructure
V2N Vehicle-to-Network
V2P Vehicle-to-Pedestrian
V2V Vehicle-to-Vehicle
V2X Vehicle-to-any-thing
viii
List of Abbreviations and Symbols
WG Working Groups
WI Walfisch Ikegami
WLAN Wireless Local Area Networks
ix
List of Abbreviations and Symbols
Symbols
2σ2 Power of the fading component
A(·) Antenna transmission pattern
Am Antenna’s front-to-back ratio
a Indoor propagation loss (dB/m)
Beff Bandwidth efficiency
BPRB Bandwidth of a PRB
Cav Average capacity
D Perpendicular distance between external antenna and external
wall
Da Amount of data for all UEs on AL
Da,u Amount of data for uth UE on AL
Dmin Minimum data rate requirement
Dr Amount of data on RL
Ei(·) Exponential integral function
E(·) Expectation
F (·) Cumulative distribution function
f(·) Probability distribution function
f1 Frequency 1
f2 Frequency 2
fc Carrier frequency
h(1)q Channel coefficient at best receive antenna for a qth transmit
antenna of eNB
h(2)q Channel coefficient at worst receive antenna for a qth transmit
antenna of eNB
hjlq Channel coefficient at the lth receive antenna of the RN from
the qth transmit antenna of the jth eNB
Irestn,k Rest of interfering eNBs signals
Kd Rician K factor for desired DeNB
Ki Rician K factor for interfering eNB
k RN of the nth cell
L Number of hops
Nd Number of phase bits in the dedicated link
Ni Number of phase bits in the interfering link
Na Number of PRBs allocated for AL
NASF Number of subframe allocated for AL in 10 msec frame
Nmax Maximum number of PRBs
x
List of Abbreviations and Symbols
NMBSFN Number of MBSFN subframes in a 10 msec frame
NPRB Number of PRBs in a subframe
NPRB,u Numbers of PRBs allocated to uth UE
Pn Noise power
P out Outage probability
PRx,DeNB Power received by RN from DeNB
PRx,Other eNBs Power received by RN from neighboring eNBs
Pt Transmission power
Q3 3rd Quarter
Re2e e2e rate
Ra Instantaneous rates on AL
Rmin Minimum rate
Rout Outage rate
Routa Outage rate on AL
Rr Instantaneous rates on RL
r1 Target relay node served by DeNB
r2 Relay node served by interfering eNB
S Distance between transmitter and receiver
Sa,u Spectral efficiency of AL
Sr Spectral efficiency of RL
T Total duration of TTI
Ta AL portion of TTI
Tr RL portion of TTI
τa Relative transmission time allocated for AL
τr Relative transmission time allocated for RL
τmaxr Relative transmission time threshold for RL
U Number of UEs
Wa AL bandwidth
Wr RL bandwidth
W Precoding complex weight vector
wjq Precoding weight applied at the qth transmit antenna of the
jth eNB
γa Instantaneous AL SNR
γa Mean AL SNR
γd Mean power from dedicated signal on RL from desired DeNB
γi Mean power of interfering signal from interfering eNB
γr Mean RL SNR
γn,n,k Mean power of the desired DeNB on RL
xi
List of Abbreviations and Symbols
γm,n,k Mean power of the interfering eNB on RL
γn,n,k Mean power of the dominant interfering signal on RL
ν2 Power of the static signal component
θ Angle of direction of main beam of antenna from UE towards
transmitter
θ3dB Angle of antenna beam which is 3dB lower than the main
beam of directional antenna
ΥB RL SINR for baseline scheme
ΥBF RL SINR for beamforming schemes
Υa,u SINR level for uth UE on AL
Υeff SINR efficiency
Υr SINR level on RL
xii
1. Introduction
1.1 Motivation and problem definition
1.1.1 Development towards small cell systems
Mobile communication has become an integral part of our everyday life. Dur-
ing the last two decades drivers for the evolution, has been the increasing
number of mobile broadband subscribers and their growing demand for larger
capacities due to increasing number of smart-phones and other data con-
sumption devices [1]. For example, there was almost 7.6 billion mobile sub-
scriptions globally at first quarter (Q1) of 2017, there are 2.1 billion Long Term
Evolution (LTE) subscriptions and the number is expected to reach 5 billion by
year 2022 [2].
To that end, the standardization organizations such as Third Generation Part-
nership Project (3GPP) and International Telecommunication Union Radio commu-
nication (ITU-R) are continuously specifying new technological solutions to
meet the future services demands. These technologies include the widely-
deployed fourth generation (4G) and evolved 4G technology enhancements
that support services ranging from mobile broadband connectivity (with peak
throughput up to 1 Gbps) to human-to-machine and machine-to-machine-
type communication. Moreover, it is envisioned that the future fifth gener-
ation (5G) technologies will contribute towards a Networked Society, whereby,
all kind of services will be provided through wireless connectivity [1, 3]. Ac-
cordingly, it is expected that the number of 5G subscription will exceed half
a billion by 2022 [2]. To that enhanced use cases enabled by extended mo-
bile broadband connectivity (eMBB), massive Internet-of-Things (IoT) (also
referred to massive machine-type communications, mMTC) and ultra-reliable
low-latency communications (URLLC), will cater to the exploding demands
1
Introduction
for capacity along with other growing range of vertical applications and busi-
ness models [4].
The service, capacity and ubiquitous coverage targets of future wireless sys-
tems can be in part fulfilled by increasing the available spectrum and im-
proving the spectral efficiency. Yet, even more substantial growth of net-
work capacity can be achieved by reusing spectrum through network den-
sification [5, 6]. The commonly considered network densification is obtained
through the heterogeneous network (HetNet) deployment where Low Power
Nodes (LPNs) with less than 10 W transmission power complement the tradi-
tional high-power (20 W or higher transmission power) macro-cell sites [6,7].
Term LPN is used here to draw a distinction between the complementary
small nodes and the legacy macro-cell sites. A vast range of LPNs exists de-
pending on the use scenario attributes (area size, indoor or outdoor location,
etc.). The LPNs include small nodes such as relays, pico/micro nodes, remote
radio heads and distributed antenna systems. It is estimated by Cisco that 60%
of the global cellular traffic will be offloaded to various types of LPNs in near
future [8].
1.1.2 Wireless LPN: Relaying
For LPN network extensions the connectivity to the core network is a crucial
problem and a number of LPN backhauling solutions has been identified de-
pending on the deployment scenario. For indoor LPNs there is the possibility
to leverage in-building wireline infrastructure such as fiber and twisted cop-
per pairs (digital subscriber lines), to provide backhaul. For outdoor LPNs sit-
uation is more complicated since access to backhaul lines might not be possi-
ble without new cable installations. Therefore wireless backhauling solutions
such as fixed microwave or millimeter wave radio links and even satellite links
have been considered as backhaul options for the outdoor LPNs. Yet, these
backhauling alternatives are usually far too expensive when compared to the
LPN traffic volumes.
To that end, the LTE-Advanced (LTE-A) relaying specification provides an
interesting backhauling scenario [9]. The proposed LTE-A relays are self-
backhauled towards the serving Donor evolved Node B (DeNB) via a stan-
dardized radio (Un) interface. The relay deployment scenarios considered in
3GPP standardization discussion include the indoor coverage enhancement,
outdoor coverage extension and temporarily deployed (nomadic) relays. An
important and technically challenging class of LTE-A relays are the inband
dual-hop Decode-and-Forward (DF) Relay Nodes (RNs). The self-backhauling
2
Introduction
ability of dual-hop DF inband relaying protocol provides an opportunity to
efficiently utilize existing radio resources by exploiting the same frequency
band for both Relay Link (RL) between DeNB and RN, and Access Link (AL)
between RN and UE.
However, self-backhauling through inband radio resources may cause a ca-
pacity bottleneck on the RL which obviously affects the End-to-End (e2e) link
performance between UE and DeNB. To compensate the scarce radio resources
in RL efficient Resource Allocation (RA) schemes are needed to optimally
share the radio resources between the RL and AL. Moreover, the inband na-
ture of relaying in frequency reuse 1 systems like 4G may lead to serious co-
channel interference challenges. The interference on RL is crucial since it will
impact to the service of all users connected to the RN. If RNs are fixed part of
the network infrastructure, as is the case in 4G, then the interference in RL is
not temporary in nature but may occur almost continuously. Fortunately, the
interference between the transmitter and receiver with fixed locations can be
effectively mitigated through e.g. beamforming techniques.
The self-backhauling property of relays also provides some new application
options. In e.g. an emergency event, unexpected and high traffic demands
may occur locally in the network. Especially indoor coverage and capacity
may be unacceptable low when the emergency responders and public author-
ities in place need it most. For such situations nomadic RN provides a flexible
solution that can be used to increase local capacity. Actually, a nomadic RN
can be installed to a vehicle and switched on in the place of emergency. The
outdoor location on top of a vehicle (or even on top of a telescope mast) sup-
port good RL while AL indoor coverage can be provided by placing the RN
close to the building wall.
1.2 Scope of the Thesis
The specific focus of the thesis is on the dual-hop inband decode-and-forward
relaying. This type of relaying is part of 4G LTE standards and currently con-
sidered for 5G as well. While mathematical analysis plays an important role in
the performance evaluation, all analytical results have been verified through
simulations. Although most of the results are quite generic in nature, they
are practically viable and can be used in the development of future relaying
systems for 5G. We also note that underlying assumption is that relays are
overlaid by a macro-cellular network.
As discussed briefly in the previous section, the main challenges of the in-
3
Introduction
band relaying are the scarcity of radio resources and the co-channel interfer-
ence. These challenges have been main drivers of the thesis. Accordingly, the-
sis considers the RA between the RL and AL for inband relaying with aim to
maximize the end-to-end (e2e) performance. Since results from the resource
allocation study show that the RL easily becomes the e2e performance bottle-
neck, the practical interference mitigation in the RL has been the second main
subject for the thesis. Finally, a case study is provided to investigate a rapidly
deployable outdoor RN to improve the local in-building coverage and capac-
ity in case of e.g. emergency events. In addition, in this case the co-channel
interference and resource allocation for relaying plays an important role.
1.3 Summary of Thesis Contributions and Publications
The main contributions of this thesis are briefly described below.
• Contribution 1
The RA between RL and AL can be carried out by many different ap-
proaches. In literature fixed RA between RL and AL has been widely de-
ployed while studies on resource optimal RA are rare. In this approach in-
stantaneous RA between RL and AL is deployed that maximizes the e2e
throughput. In this thesis we present a comparative analysis of RA schemes
including the fixed RA with and without buffer, and the resource optimal
RA. For that purpose, we deduce closed-form expressions for the mean and
outage e2e rate. In case of resource optimal RA the mean rate attains an
expression in terms of an integral that does not admit a closed-form solu-
tion. Therefore, we derive a tight lower bound that accurately approximates
the mean e2e data rate performance. In addition, we deduce closed-form ex-
pressions of e2e rate for the case where DL and UL communication between
source and destination is decoupled. Main results have been summarized
in [10] and [11].
• Contribution 2
As discussed, the co-channel interference in RL can become crucial since
resources in RL are scarce. Accordingly, we study the interference mitiga-
tion in RL based on Channel State Information (CSI) fed back from RN to
the both serving and interfering base stations. The main emphasis is on
the analytical investigation but again, all results have been verified through
simulations. We derive analytical expressions for the RL SINR distribution
4
Introduction
assuming the Rice and Rayleigh fading combinations on the RL and the in-
terfering links. Based on the results also e2e outage rate is analytically for-
mulated. These contributions have been summarized in [12] while the work
in [13] served as a starting point for the study.
• Contribution 3
Rapidly deployable RN is an interesting concept where a nomadic out-
door RN can be located e.g. within the proximity of a building with an
emergency and used to provide an in-building coverage for emergency re-
sponders. To study the impact of RN location, overlay network interference
and RA in such scenario three different indoor environments has been con-
sidered. Namely we assume a 3GPP 5×5 grid, 3GPP dual strip model and
a more realistic deployment case based on ray tracing propagation model-
ing. Performance is evaluated in terms of e2e throughput. Results have been
previously presented in [14].
1.4 Author’s Contributions
The author of this thesis was in the leading role in the research reported in
publications [10], [13], [14] and in the submitted manuscript [12]. Dr. Alexis
Dowhuszko has been leading the research reported in the manuscript [11].
In [10], [12], [13] and [14] author of this thesis carried out performance simu-
lations, participated actively in the mathematical analysis by deducing part of
the formulas and was the main author while writing the manuscript. Dr. Ed-
ward Mutafungwa (in [12], [13], [14]), Dr. David Gonzalez G.(in [10], [13]), Dr.
Alexis Dowhuszko (in [10]), Dr. Zhong Zheng (in [10], [14]) and Dr. Beneyam
Haile (in [13]) have given support in the mathematical analysis, writing and
in the proof-reading phase while supervising professor Jyri Hämäläinen has
been helping to identify problems and to model the system. He has been also
guiding the publication work.
The author of this thesis also actively contributed to the manuscript [11],
where his focus was especially in the simulations and paper writing.
5
Introduction
1.5 Structure of the Thesis
Chapter 2 provides a brief overview of 3GPP LTE system covering the prin-
ciples of the basic physical layer features like orthogonal frequency division
multiple access (OFDMA), radio resource management and inter-cell interfer-
ence coordination. Discussion also includes LTE-A radio features and basics
of the relaying therein. To that end, relaying modes and protocols are be-
ing explained in a general level while particular focus is placed on the LTE-A
Type 1 inband relaying scheme. Furthermore, resource scheduling mecha-
nism in relaying and benefits of relaying are briefly discussed.
Chapter 3 presents a comparative study of different RA schemes employed
to distribute the radio resource between RL and AL. Main focus is on the per-
formance analysis of the resource optimal RA that is compared to fixed RA
with and without buffer. The rate distributions and mean rate formulas are
presented to support the analysis of the relaying e2e data rate performance.
In addition, this chapter also contains the derivation of the closed-form ex-
pression for the e2e data rate in case where the DL and UL transmissions are
decoupled. Material presented in this chapter is based on the publication [10]
and the manuscript [11].
Chapter 4 investigates the mitigation of the RL interference through simple
transmit beamforming techniques. Chapter is started by a literature survey
of the field and followed by the problem modeling and analysis that provides
derivation of analytical formulas for the SINR and the outage probability in
the RL by assuming the Rice and Rayleigh fading combinations for the RL
and the interfering links. Analytical study is complemented by simulations to
verify the analytical formulas and provide further insights to the system per-
formance. Material presented in this chapter is based on the publication [12]
while the work in [13] complements the study.
Chapter 5 presents a performance evaluation for a nomadic RN concept
from the perspective of outdoor-to-indoor coverage in different building sce-
narios. The simulation campaign provides the e2e throughput performance
experienced by indoor UE in different building and RN deployment situa-
tions. Material presented in this chapter is based on the publication [14].
Finally, Chapter 6 summarizes the contributions of this thesis.
6
2. Relaying in LTE-Advanced Systemsand Beyond
This chapter focuses on the relaying systems specified in LTE-A standards,
as well as, on the development of relaying technologies thereof. First, we
introduce the baseline LTE system with special emphasis on relaying. This
background discussion includes a thorough introduction of the 3GPP LTE
including the architecture and specifications emerging from the ITU-R ini-
tiative International Mobile Telecommunication-2000 (IMT-2000). Although, the
LTE technology is already deployed, its evolution continues as can be seen
from LTE-A and its future releases (e.g., Release 14) [1,15]. While current LTE
systems support various mobile applications, the customer expectations for
high data rates with low latency are constantly increasing. Accordingly, both
ITU and 3GPP are working on upcoming fifth generation (5G) technologies
while evolving the current LTE releases (to Release 14) in parallel with de-
velopment of new radio access technologies. The LTE Release 14 and beyond
are required to support the upcoming 5G requirements [15–17]. Figure 2.1
shows a schematic diagram of the evolution phases occurred in the mobile
communication technologies including the LTE standard releases.
GSM
1990 2000 2008 2011 2015
GPRSEDGE
UMTSHSPA
HSPA+
LTELTE-A
Voice-centric 1G/2G Mobile broadband 3G/4G
5G
~2020
Networked society
LTE-A Pro
Figure 2.1. Evolution time-line of the mobile wireless communication technologies
7
Relaying in LTE-Advanced Systems and Beyond
Relaying
Relay Backhaul Enhance-
ments Issues
• Network Planning & Op-
timization
� [18–24]
• RRM Schemes
� [20, 22, 25–29]
• MIMO, CoMP & Interfer-
ence Mitigation
� [12, 13, 30–35]
• Path Loss & Antenna
Elevation
� [36–39]
Mobile System Enhance-
ments
• Coverage Extension
� [18, 23, 27, 33, 40–51]
• Capacity Enhancements
� [13, 14, 19, 21, 24, 27, 33, 42,
43, 46, 48, 49, 52–58]
• Energy Efficiency
� [59–66]
• Performance comparison
Studies
� [13, 14, 43, 44, 46, 48, 50, 52,
54, 61, 67–71]
Future Research
• Open Issues & Future
Challenges
� [5, 13, 35, 38, 72–84]
PHY/MAC Layers
• Channel Waveforms
� [76, 82, 83, 85–92]
• Massive MIMO
� [92–100]
• Relaying with Network
Coding
� [101–109]
• Cooperative Communica-
tion & Relay Selection
� [78, 110–120]
• Channel Access
� [117, 118, 120–123]
Classification of Relays
based on
• Resource Allocation
� [1, 124, 125]
• Duplexing Modes
� [126–128]
• Relaying Processing
� [9, 21, 45, 47, 52, 68, 129–
138]
• Deployment Modes
� [3, 139–141]
Deployment & Usage sce-
narios
• UDN & Hetnets
� [3, 6, 17, 71, 142]
• D2D Communication
� [1, 80, 81, 143–147]
• M2M/IoT
� [3, 148–150]
• Moving Networks
� [140, 141, 147, 151, 151–
157]
• Rapid Deployments
� [14, 81, 144, 145, 155, 158–
160]
Figure 2.2. Overall Structure of our literature survey of Relaying systems.
Figure 2.2 presents the overall structure of our literature survey on relaying
in LTE-A systems and beyond. We have used the mentioned references in the
forthcoming discussion. Yet, due to practical reasons we are not following in
the following sections exactly the same categorization as in Figure 2.2.
The rest of the chapter is organized as follows. Section 2.1 gives the overall
summary of LTE releases including the system requirements, architecture as
well as highlights enhanced physical layer technologies introduced in each re-
lease. Section 2.2 explains the relaying principle and classification of several
8
Relaying in LTE-Advanced Systems and Beyond
relaying modes employed in wireless communication. Furthermore, the em-
ployment of relaying in LTE-A system are being briefly described in section 2.3
with special focus on inband relaying. Moreover, this section also highlights
the relaying performance gains obtained in terms of coverage extension and
capacity enhancements. Finally, section 2.4 presents the overall literature sur-
vey on relaying beyond LTE-A systems including the open issues and future
challenges.
2.1 3GPP LTE, LTE-Advanced and LTE-A Pro Systems
2.1.1 Baseline LTE System
System Requirements and Architecture
The LTE standards were driven by the need for a long term efficient network
coverage and capacity solution [161]. The LTE standards initially targeted sys-
tem improvements over the preceding High-Speed Packet Access (HSPA) Re-
lease 6 standards. These initial targets include an improved instantaneous
peak data rates of 50 Mbps and 100 Mbps in uplink (UL) and downlink (DL),
respectively, a flexible operating bandwidths from 1.4 MHz up to 20 MHz and
less than 10 milliseconds (msec) round-trip delay over the LTE air interface.
Other targets include improvements in battery efficiency for LTE UE, reduced
cost per transmitted bit, support for high-speed mobility and compatibility
with legacy network architectures. LTE systems may operate on both paired
and unpaired spectrum, i.e. Frequency Division Duplexing (FDD) and Time
Division Duplexing (TDD). Moreover, the system can be operated on differ-
ent frequency bandwidths: 1.25 MHz, 1.6 MHz, 2.5 MHz, 5 MHz, 10 MHz,
15 MHz, 20 MHz.
Unlike the preceding 3GPP architecture, LTE systems are designed to en-
able a seamless Internet Protocol (IP) based connectivity between User Equip-
ment (UE) and Core Network (CN). This leads to a reduced number of net-
work interfaces. For example, radio base station (called as eNB in 3GPP termi-
nology) is the only intermediate node between the UE and CN. It is noted that
LTE eNB uses Uu to serve its UEs. This flat architecture decreases the amount
of signaling and jitters. The main components of the LTE system architec-
ture are shown in Figure 2.3. As already mentioned, the LTE Radio Access
Network (RAN) enables a flat system architecture where eNB is responsible
for all the radio related functionalities of the network. We note that eNB em-
9
Relaying in LTE-Advanced Systems and Beyond
ploys the so-called X2 interface to communicate with its neighbouring eNBs,
in order to enable a seamless active mode mobility in the network [1].
eNB1
eNB3
eNB2UE
UE
UEX2
X2 X2
S1 S1
S1
S1
MME/S-GWMME/S-GW
Internet/PLMN
EPC
E-UTRAN
SGi SGi
Figure 2.3. LTE network architecture.
LTE Radio Interface
The LTE system applies a multi-carrier transmission scheme known as Or-
thogonal Frequency-Division Multiplexing (OFDM) in order to enable broad-
band services. In OFDM, data symbols are modulated over orthogonal
narrow-band subcarriers with subcarrier spacing of 15kHz. This minimizes
the selectivity of wide band channels which may cause the loss of data sym-
bols. Moreover, OFDM scheme also minimizes the Inter-Symbol Interfer-
ence (ISI) through the use of Cyclic Prefix (CP) technique, enabling a simple
receiver design .
The temporal resources are segmented into radio frames comprising of 10 sub-
frames with duration of 10 msec. Each sub-frame is formed by two slots of
duration of 0.5 msec. Each slot consists of 6 or 7 OFDM symbols. The Physical
Resource Block (PRB) is the smallest element used in the transmission resource
allocation with bandwidth of 180 kHz. Each PRB contains 12 consecutive sub-
carriers for one 0.5 msec slot. One or more contiguous resource blocks can be
allocated to a physical channel.
Multi-antenna transmission techniques hold a key role in LTE technology.
To that end, even the first LTE eNBs and UEs employ dual antennas in or-
der to improve the downlink performance via Multiple Input Multiple Out-
put (MIMO) methods. Multiple antennas can be used to increase data rates
10
Relaying in LTE-Advanced Systems and Beyond
and/or suppress the interference [1].
LTE systems also exploit the rapid channel variations through dynamic
channel-dependent scheduling. Here, the time-frequency resources are dy-
namically assigned to UEs per Transmission Time Interval (TTI). Due to multi-
path fading, the UE radio link may experience rapid instantaneous variations
in both time and frequency domains [1]. This channel condition information
is known as Channel-State Information (CSI) and it is estimated using the refer-
ence signals in the DL direction. Once CSI is conveyed to eNB it can be applied
in the scheduler while scheduling the resources to UEs.
To increase the spectrum efficiency, LTE employs the frequency reuse-one.
Thus, all the neighbouring transmission points can use the same spectrum.
In DL this leads to a notable interference between the eNBs. To that end, co-
ordinated transmission between eNBs has been introduced. This technique
is known as Inter-Cell Interference Coordination (ICIC). Hence, eNBs exploit the
X2 interface to exchange the control messages with neighbouring eNBs to co-
ordinate the overlapping transmissions [1].
2.1.2 LTE-Advanced System
The baseline LTE system was defined in standard Releases 8 and 9. Though,
there was a need to further develop the system in order to meet the rapidly in-
creasing service demands. To that end, LTE-Advanced (Release 10) represents
the evolution of LTE that is designed to achieve IMT-Advanced requirements.
LTE-A system aims to achieve peak data rates of up to 1 Gbps (for low mobil-
ity users) and 500 Mbps in DL and UL, respectively. The target peak spectral
efficiencies are 30 bps/Hz and 15bps/Hz in DL and UL, respectively. LTE-A
enhances the cell edge user throughput (5th%-ile user throughput) in order to
achieve a more homogeneous user experience in the cell [162].
In addition to these requirements, 3GPP also demanded the backward-
compatibility with LTE systems and seamless support for LTE Release 8 UEs.
To that end, LTE-A standards introduced a number of enhancements which
are depicted in Figure 2.4 and described briefly thereafter [1, 124, 163].
11
Relaying in LTE-Advanced Systems and Beyond
LTE-
Adv
ance
d
Release – 10/11
Downlink (8x8) / Uplink (4x4) MIMO Coordinated Multipoint (CoMP)Heterogeneous networks (Hetnet)Carrier Aggregation (CA)Relaying
Release – 12
Enhanced Downlink MIMOSmall Cell ON/OFFDual ConnectivityMachine Type Communication (MTC)Proximity Service (ProSe) or Device-to-Device (D2D) CommunicationSelf Optimizing Networks (SON)Hetnet MobilityMultimedia Broadcast/Multicast ServiceEnhanced International Mobile Telecommunication Advanced (eIMTA)Frequency Division Duplex – Time Division Duplex Carrier Aggregation (FDD-TDD CA)
Figure 2.4. LTE-A communication technologies.
Carrier Aggregation
LTE release 10 introduces a new bandwidth extension technique known as
Carrier Aggregation (CA), where multiple frequency components, each with
different bandwidths are aggregated. CA enables a wider bandwidth for
transmission purposes both in DL and UL. In principle this allows the ag-
gregation of up to five component carriers and transmission at maximum on
100 MHz aggregated band [1,124]. The number of component carriers in CA
is extended in later releases up to 32.
Multi-Antenna Enhancements
In LTE releases, various multi-antenna techniques have been introduced. To
that end, Release 10 introduced the enhanced DL MIMO and UL MIMO
schemes. The DL MIMO transmission may apply up to 8 transmission layers
while in UL spatial multiplexing of up to 4 transmission layers can be used [1].
Coordinated Multi-Point
LTE Release 11 introduced the so-called Coordinated Multipoint Transmis-
sion (CoMP). In CoMP, different transmission points (in same or different cell
sites) coordinate their transmission and reception to create less interference
for their users and to enhance the performance. In CoMP, network may uti-
lizes dynamic point selection such that the transmission can be switched be-
tween involved transmission points. Alternatively network can utilize the
joint transmission/reception, where the transmitted/received data signals
12
Relaying in LTE-Advanced Systems and Beyond
are jointly processed to enhance the transmission/reception performance [1].
Heterogeneous Deployments
In Heterogeneous Network (HetNet), the mixture of access nodes with
different transmission powers are deployed under the macro-overlaid net-
work (usually include high power macro node with transmission antenna el-
evation above the rooftop level). In HetNet low-power nodes (usually pico
nodes) are overlaid by macro-cell coverage area with purpose to match the
service provision with the non-uniform demand. In LTE Release 10 new ways
were introduced to coordinate the interference among different layers of Het-
Net with different transmission powers.
Furthermore, the Hetnet concept considered in Release 12 enable a dense de-
ployment of small cells to achieve high network throughput. In dense de-
ployment, there is a high likelihood that device is in the service area of many
small cells. Such overlapping downlink transmissions certainly create heavy
interference. Accordingly, LTE Release 12 introduced a technique of turning
ON/OFF the small cells, so as to minimize the inter-cell interference as well
as to improve the energy efficiency [1].
Machine Type Communication
Machine Type Communication (MTC) applies wireless connectivity for ma-
chine communication under the control of an access node. MTC can be cate-
gorized into two major groups namely, to massive MTC and to critical MTC.
The former type comprise less critical, less complex and low power consump-
tion wireless devices, such as, sensor, actuator, etc. These type of devices have
long battery life even with span of 10 years [3]. The latter type represents a sta-
ble, always-on and reliable wireless connection. Related devices are used in
mission critical scenarios such as traffic control, industrial applications, smart
grid and so on [1, 3].
Device-to-Device Communication
In LTE Release 12, a new non network-centric communication mechanism
was introduced known as Device-to-Device (D2D) communication. In D2D,
the communication devices establish a direct communication link with each
other rather then connect via cellular base station. To that end, two usage
scenarios are public safety communication and local commercial usage. In
the former case, the D2D development includes the in-coverage and out-of-
coverage communication while in the latter case, each device is required to
discover its neighbouring devices [1].
13
Relaying in LTE-Advanced Systems and Beyond
Relaying
Relaying deploys a low power node known as Relay Node (RN) which acts
as an intermediate node between user and network elements. Specifically,
RN utilizes wireless self-backhaul link towards the eNB known as a Donor
eNB (DeNB). The RN deployment reduces the UE-Infrastructure distance and
eventually, minimizes the experienced path loss of signals between UE-DeNB.
In the following, the backhaul link between RN and DeNB is named as Relay
Link (RL) which carries both the UE data traffic as well as the control signaling
for RNs. The RL have normal LTE air interface characteristics [164]. Similarly,
the Direct Link and Access Link (AL) refer to the DeNB-UE link and the RN-UE
link, respectively. Figure 2.5 shows the schematic diagram of REC network.
Section 2.3 presents in more detail the LTE-A relaying system.
DeNB
RN
UE
Figure 2.5. LTE-A relay enhanced cellular network.
2.1.3 3GPP LTE-Advanced Pro
The LTE system evolution has been further extended by two releases namely,
by Release 13 (closed in March, 2016) and Release 14, also known as LTE-A
Pro. The newly introduced concepts are summarized in Figure 2.6.
In Release 13, the MTC performance was further improved by introducing
enhancement techniques. For example, the MTC enabled devices can be op-
erated at frequency spectrum of 1.4 MHz with transmission power 20 dBm
and battery life upto 10 years [3]. This not only reduce the device cost but
also improves the energy efficiency. Another LTE-based track is known as
Narrow-band Internet-of-Things (NB-IoT) further evolving the LTE-A systems
by exploiting the 180 kHz carrier. Moreover, the 3GPP communication tech-
nologies are envisioned to play a significant role in the deployment of Intel-
ligent Transportation System (ITS) by enabling the wireless connectivity be-
tween vehicles and any thing around it, in order, e.g., to ensure the public
safety of transportation system. To that end, the LTE enabled Vehicle-to-any-
thing (LTE-V2X) deployment scenarios. This X-any-thing could either be a
14
Relaying in LTE-Advanced Systems and Beyond
vehicle (V2V), pedestrian (V2P), network (V2N) or Infrastructure (V2I) across
the road side [15, 165].
LTE-
A P
ro
Release – 13Active Antenna Systems (AAS)Elevation Beamforming (EBF) and FD-MIMOEnhanced Signaling for CoMPEnhanced D2D/ProSeEnhancement for MTC & Narrow-band Internet-of-Things (NB-IoT)Indoor Positioning EnhancementEnhancement in CA/License-assisted Access (LAA)DL Multi-User Superposition Transmission (MUST)RAN sharing EnhancementDual Connectivity (DC)LTE-WLAN Aggregation (LWA)RAN enhancements for extended Discontinuous Reception (DRX) in LTE
Release – 14Enhanced FD-MIMO with 32 Tx AntennasLAA in Uplink TransmissionReduction in Latency timeIntelligent Transportation System via LTE-V2X: Vehicle-to-Vehicle (V2V), Vehicle-to-Infra (V2I), & Vehicle-to-Pedestrian (V2P) Enhanced operation in Unlicensed SpectrumEnhanced Positioning/Network assistance aspectsEvolved Multimedia broadcast Multicast Service (eMBMS)Superposition Coding for Enhanced DL Multiuser Transmission
Figure 2.6. LTE-A Pro communication technologies.
In LTE Release 13, the efficient utilization of available spectrum is further de-
veloped by employing the CA over the licensed and unlicensed frequency
spectrum (usually 5 GHz license free frequency is used). This approach is
known as License-Assisted Access (LAA) [1,124]. Furthermore, Release 13 in-
troduced the Dual Connectivity (DC) framework that enables a wireless de-
vice to simultaneously communicate with two eNBs. This provides an oppor-
tunity to aggregate the user-planes and a wireless device can communicate
with the multiple eNBs both in UL and DL in order to obtain improved per-
formance. Moreover, this also add robustness to the network when device is
controlled by one eNB while transmitting the user data to other eNBs. In DC,
UE can select the best eNB for UL and DL communication [1].
2.2 Relaying Principles and Classifications
Many types of RNs have been proposed since the earliest RN were first intro-
duced [3, 166]. RNs can be classified according to various criteria, such as,
15
Relaying in LTE-Advanced Systems and Beyond
(a) Classification based on Resource Allocation
(b) Duplexing-Based Classification
(c) Classification based on Relay Processing
(d) Classification based on Deployment Modes
In this section, we recall briefly the RN technologies under each category.
2.2.1 Classification based on Resource Allocation Strategies
Relaying can be classified according to how the RN resources are being shared
between RL and AL. In relay communication, four different unidirectional
communication links are required to enable communication between UE and
eNB both in UL and DL as shown in Figure 2.5. Different relaying protocols
are explained below.
Inband Relaying
In inband relaying, RL and AL utilize the same frequency spectrum, though,
links are separated via time division multiplexing on subframe basis as shown
in Figure 2.12. This type of relaying is spectral efficient due to usage of the
same frequency channel for both RL and AL operations. To minimize the in-
terference between the two links, RL and AL should be properly separated in
a time domain. This time division multiplexing of both links certainly affect
the system capacity as well as add some delay because simultaneous trans-
missions on RL and AL are not possible [1, 124, 125].
Outband Relaying
In outband relaying, RL and AL utilize separate frequency bands as shown in
Figure 2.12. In this type of relaying, RL and AL transmissions are not affect-
ing each other due to sufficient separation in frequency domain. This type of
relaying improves the system capacity due to availability of large spectrum
for each individual link but at the cost of inefficient use of frequency spec-
trum [1,124,125].
2.2.2 Duplexing-based Classification
Relaying can apply one of the given two duplexing modes:
16
Relaying in LTE-Advanced Systems and Beyond
Half-Duplex Relaying
In half duplex (HD) relaying, RN enables the transmissions (to DeNB in UL /
to UE in DL) and reception (from UE in UL / from DeNB in DL) on the same
frequency at different time instants. This requires suitable resource schedul-
ing in order to efficiently distribute the RN resources for UL and DL trans-
missions and receptions. In HD relaying, there is no co-channel interference
between RL and AL due to use of orthogonal time slots. Schematic diagram
of half-duplex relaying is shown by Figure 2.7.
DeNB
RN
UE DeNB
RN
UE
First Phase Second Phase
Figure 2.7. Half-duplex relaying mode.
Full-Duplex Relaying
In full duplex relaying (FDR), RN is able to carry out transmissions (to DeNB
in UL / to UE in DL) and reception (from UE in UL / from DeNB in DL), si-
multaneously either on the different or even the same frequency channel. The
former FDR scheme is known as out-band FDR [167] and the latter relaying
scheme is known as inband FDR [168], see Figure 2.8.
DeNB
RN
UE DeNB
RN
UE
Outband FDR Inband FDR
f 1f 1f 1f 2
Figure 2.8. Outband and Inband full-duplex relaying.
If RN exploit the same frequency spectrum for RL and AL transmissions
and receptions, then HD RN may suffer from the self-interference. That
is, the RN transmission interferes the RN receiving antenna However, this
self-interference can be mitigated by enabling enough spatial isolation be-
tween RN transmit and receive antennas. In case of out-band FDR, this
self-interference can be avoided by using large separation among the fre-
quency bands used for RN transmission and receptions [126]. The inband
17
Relaying in LTE-Advanced Systems and Beyond
FDR is required to employ the advanced interference mitigation schemes
as well as to isolate the transmit/receive antennas for combating the self-
interference [127,128].
2.2.3 Classification based on Relay Processing
Amplify-and-forward Relaying
Amplify-and-Forward (AF) RN, also known as repeater, amplify the analog
signals received from the source and retransmit it towards the destination as
shown in Figure 2.9 (a). During the AF relaying, AF also do amplify the in-
terference and noise in addition to the desired signal. This deteriorates the
overall system SINR level and limits the system throughput. Hence, these
RNs are more beneficial in the high SNR region. AF RNs are transparent to
both the source and destination and that is why they can be easily deployed in
the existing networks [68,129]. AF relaying can be categorized into two types
in terms of relay gain i.e., Variable Gain (VG) and Fixed Gain (FG) RNs. The
former protocol adjust its amplification factor according to the instantaneous
channel state while the latter protocol keeps a constant amplification factor
that is set by exploiting the statistical channel state information [130–132]. The
AF RN can also be considered as layer 1 RN [47, 134]. In WiMAX standard-
ization, this type of relaying are being proposed with the name of transparent
relaying [136].
Decode-and-forward Relaying
In DF relaying, RN first receives the entire signal from the source and then
decode and retransmit it to the destination as shown in Figure 2.9 (b). In DF
relaying, RN removes the interference and noise from the received signal af-
ter the first hop. Thus, this type of RN enables a good performance also in
low SNR environments [1]. Due to decoding and retransmitting of signals, it
causes time delay and increases the system complexity. In dual-hop DF relay-
ing system, throughput can be maximized on RL and AL, if both links enables
equal amount of data rate [21, 45, 133]. The DF RN can also be considered as
layer 2 RN [47]. Moreover, Layer 3 type RN, in addition to the Layer 3 func-
tionalities, comprises all base station functionalities like L2 RN. In WiMAX
this type of relaying is known as non-transparent relaying [136].
18
Relaying in LTE-Advanced Systems and Beyond
DeNB RN
(a) AF Relaying
UE
RF Signal Received
RF SignalRe-transmitted
Received SignalAmplified
DeNB RN
(b) DF Relaying
UE
RF Signal Received
RF SignalRe-transmitted
Received SignalDecodedEncodedAmplified
Figure 2.9. (a) Amplify-and-forward relaying and (b) Decode-and-forward relaying
Compress-and-forward Relaying
In Compress-and-Forward (CF) relaying (also known as estimate-and-
forward or quantize-and-forward), the RN operations fall in between the
AF and DF processing. Unlike the AF and DF relaying, where RN just re-
transmits the copy of what it receive from the source, the CF RN retransmits
the quantized form of the received message towards the destination. Hence,
the destination node uses the quantized message as well as the message re-
ceived from the source for decoding the source data. RN is not necessarily
required to decode the message received from the source but rather it needs
to extract the information which could be useful for the decoding purposes in
destination. Comparatively, CF relaying may outperform DF relaying when
the RL experience poor quality and vice versa [137,138].
2.2.4 Classification Based on the Deployment Mode
RNs can also be differentiated from the movement ability perspective. Mobil-
ity provides an additional characteristic to RN for enabling need-based cov-
erage in a target geographic location. Below, we give three different relaying
modes namely, fixed (or infrastructure), nomadic and mobile relaying [139].
Fixed Relaying
Fixed RN are usually permanently deployed at a fixed geographic location
with a purpose of providing coverage in a given (small) area. Conventionally
this type of RNs are deployed to enhance the service provision by improving
the UE SINR experienced at macro cell coverage holes and urban locations
19
Relaying in LTE-Advanced Systems and Beyond
affected by the shadowing. Fixed RNs can be also deployed at the macro
cell edge to systematically extend the network coverage/capacity. RNs can be
mounted in the lampposts or building walls or roof tops. Usually, the RN an-
tenna heights are lower than DeNB antenna heights in order to avoid mutual
interference between AL transmissions. In the network planning, RN deploy-
ment flexibility can be exploited to achieve a LOS conditions for RL [3, 139].
Schematic diagram of fixed relaying is shown by Figure 2.10.
DeNB
RN
UE
Coverage extension
Coverage holeShadowing
Urbanenvironment
Figure 2.10. Fixed (infrastructure) relaying scenarios.
Nomadic Relaying
Nomadic relaying is characterized by a semi-static RN deployment. RN can be
temporarily deployed in a certain location, where the cellular coverage is poor
and there is a temporary need for high capacity. These RNs are designed so
that they can be transported to the desired location and start operations with-
out complex configuration efforts. For example, nomadic relaying is attractive
solution in emergency/disaster situations where the emergency responders
and authorities are unable to (locally) communicate due to the unavailability
or congestion in the network. The UE served by a nomadic RN may experi-
ence both LoS and NLoS wireless channel condition on the AL. The nomadic
RN should be light and power efficient [3, 139].
Mobile Relaying
In mobile relaying, the RN is mounted on a moving vehicle (e.g., Train, Bus,
Tram), to enable on-board cellular services with good quality. A mobile RN is
connected with DeNB via mobile link, while it may have a static AL connec-
tivity to UEs located inside the vehicle. The mobile RL will comparatively add
more complexity in systems due to its entry and exit in/out of cell coverage
areas. The antenna height of the mobile RN would be relatively low because
of vehicle restrictions and other operational safety scenarios. Further details
20
Relaying in LTE-Advanced Systems and Beyond
on mobile relaying can be found from [3,139].
2.3 LTE-A Relaying System
2.3.1 Architecture of Relay Enhanced Cellular Network
Relaying is an add-on type of amendment in LTE, introduced first time in
LTE-A Release 10. To ensure the backward compatibility with earlier LTE re-
leases, RN acts like a normal eNB from the UE perspective. Similarly, core net-
work considers RN as an additional sector in DeNB coverage domain which
makes RN transparent for the CN as well. From the neighbouring eNB per-
spective, RN is also transparent and it assumes that UE is served by DeNB.
Moreover, in UL the RN utilizes the DeNB to forward the UE data towards
CN and neighbouring eNBs via standard interfaces (i.e., via S1 and X2) [124].
More information about the 3GPP relaying can be found from 3GPP standard-
ization technical reports [9, 100, 169–173].
eNBDeNB
UEUE
X2
S1S1
MME/S-GW
Internet/PLMN
REC Network
RNUE
X2 S1
Figure 2.11. LTE-A relay enhanced cellular network architecture.
In 3GPP LTE-A standards, relaying is differentiated as Type 1 and Type 2 re-
laying due to its capability of operation within the LTE-A network [9].
Type 1 Relay
In Type 1 relaying, RN is capable of controlling its own cell and all its RRM
functionalities. RN utilizes the local UE reporting to enable resource schedul-
ing among its own UEs. It sends its own physical cell identity with all con-
trol/synchronization channels and reference signals to UE. Type 1 RN acts as
21
Relaying in LTE-Advanced Systems and Beyond
a normal eNB to all type of UEs including the LTE Release 8 UEs. However,
RN is required to connect to CN via DeNB. Moreover, UE also treat RN as
normal eNB and send all the channel related information to RN. The L3 RN
can be considered as Type 1 RN. This type can be further sub-divided into
Type 1a and Type 1b RNs. The difference between Type 1a and Type 1b is
that Type 1a RN utilizes the outband relaying characteristics enabling RL and
AL transmissions on different frequency carriers. While the Type 1b utilizes
the inband relaying characteristics provided that RN transmit and receive an-
tennas, are sufficiently isolated [9]. Figure 2.12 presents the different LTE-A
relaying types.
DeNBRN
Out-band Relaying Type 1a
DeNBRN
Inband Relaying Type 1
DeNBRN
Inband Relaying Type 1b
Relay Backhaul Antenna
Figure 2.12. LTE-A relaying types.
Type 2 Relay
Type 2 RN utilizes the inband relaying characteristics for its operation in the
cellular network. This RN type operates in a transparent mode within the
existing donor cell, meaning that UEs are unaware of Type 2 RN in the cell.
That is, RN doesn’t create its own serving cell and does not have the physical
cell identity. These RNs are deployed to improve the SINR level and through-
put within the donor cell. Examples are the smart repeaters, DF RN and L2
RN [9]. The L2 relaying is not supported in LTE standards.
2.3.2 Radio Frame Structure in LTE relaying
This section describes the resource allocation when inband Type 1 RN is de-
ployed. Resource allocation scheme is required to share the available radio
resources among Direct link, RL and AL. The RL competes for the resources
with UEs served directly by DeNB on the direct link. Moreover, the RL and
AL utilize the same frequency bandwidth in inband relaying. To that end,
a resource allocation is required to isolate the transmission of both links, for
example by multiplexing the transmission in time domain. This multiplexing
22
Relaying in LTE-Advanced Systems and Beyond
should ensure that RN can carry out transmission on AL at a time, when the
RL transmission is on hold and the other way around. Figure 2.13 shows the
schematic diagram of the LTE downlink radio frame for the inband relaying
case.
As it can be seen in Figure 2.13, DeNB allocates (in this example case) three
subframes for RL among the ten subframes in a radio frame. The RN is hold-
ing its transmission on AL during these three subframes to avoid the self-
interference. In UL, it is possible for RN to ignore the transmission coming
from UEs on AL while communicating with DeNB.
Since LTE-A RN is backward compatible with the Release 8, it sends a cell-
specific reference and control signals in all DL subframes by utilizing the so-
called MBSFN capability of LTE technology [174]. More precisely, RN trans-
mits all cell-related information in the first one or two OFDM symbols of a
MBSFN subframe in DL. Accordingly, UEs are not in MBSFN mode and thus
they ignore MBSFN subframes where RL is carried out. By this, RN can quit
the transmission towards UEs and start receiving transmission from DeNB.
The remaining seven subframes in Figure 2.13 are mutually shared by the
DeNB and RN to schedule their own corresponding UEs on direct link and
AL, respectively. This however, creates interference among the overlapping
eNB and RN layers [1, 124, 175].
MUE
DeNB
RN
RUE
0 1 3 4 5 6 7 8 92
DeNB transmission to MUEs
DeNB backhaul transmission to RN on MBSFN subframe
RN reception of DeNB backhaul transmission on MBSFN subframe
RN access link transmission to RUEs
Blank subframes due to MBSFN DeNB interference to RUEs
Figure 2.13. Radio resource splitting among the direct link, RL and AL at DeNB
It is noted that the RN also receives co-channel interference from the sur-
rounding eNBs when they are serving their own RNs on the same RL sub-
frames.
23
Relaying in LTE-Advanced Systems and Beyond
2.3.3 Benefits of LTE-A Relaying
Relaying provides benefits in terms of coverage extension and capacity en-
hancement as compared to legacy cellular networks. Below Table 2.1 indicates
several studies on advantages of relaying. Moreover, this section also summa-
rizes briefly the literature regarding the studies done on various enhancement
techniques.
Table 2.1. References describing the LTE-A relaying benefits
References Scenarios Methodology Benefits
[27, 42, 44,
46, 51, 52,
61, 68, 69]
Macro and/or
Pico Only & REC
networks
SimulationsCoverage extension &
capacity gains
[13, 14, 19,
21, 23, 47,
48, 56, 58,
62, 71, 176]
Macro Only &
REC networks
Analytical
and/or
Simulations
Capacity
enhancements
[43, 45, 49,
54, 67, 177]
Macro Only &
REC networksSimulations Coverage extension
[50, 70, 71]Macro Only &
REC networksSimulations
Interference mitigation
& Throughput gains
[37, 40, 41]Macro Only &
REC networks
Field Tests &
measurements
Coverage extension in
indoor/coverage holes
[57, 59–66]
Macro Only
and/or REC
networks
Analytical
and/or
Simulations
Energy Efficiency
Relaying provides advantages when compared to legacy macro/pico base sta-
tions due to wireless backhaul option [44]. Relaying can be also used to boost
the overall network throughput (e.g., urban and suburban scenarios) via effi-
cient utilization of radio resources [20, 21, 27, 33, 55, 70, 70]. The studies done
in [27, 42, 45, 49, 67, 155] show that RN deployment enables macro cell cover-
age extension at the cell edge. It is shown via system level simulations and
field tests [40, 41] that relay UE (RUE) may also experience good throughput
in indoor environments [13, 14, 40, 41, 49].
Furthermore, nomadic relaying is a viable solution for enabling temporary
cellular coverage in special events (e.g., game events, conferences, festivals,
etc.) and in emergency situations (e.g., natural disasters, earthquakes, floods,
telemedicine scenarios, etc.) [14, 140, 141, 151, 156]. To that end, contributions
24
Relaying in LTE-Advanced Systems and Beyond
in [13, 14] show that relaying provides system performance gains in terms of
better coverage and capacity in outdoor-to-indoor scenarios. It is shown that
UEs experience less competition for radio resource on AL which leads to good
throughput gains, even though, RUEs may experience high interference from
eNBs.
It is also shown in [67] that relaying enables cost efficient deployment solu-
tion to cellular network operators, simplify site location planning and can be
easy to deploy in massive numbers on street lamp posts. Simulation results
indicate that operators can save more than 30% of deployment cost by using
RNs in coverage limited LTE-A network [67]. In addition, relaying enables
notable performance gains in terms of SNR and SINR in both interference-
limited and noise-limited cases, provided that RN deployment is done via
proper site planning [18,19, 23, 24, 37, 141].
2.4 Relaying beyond LTE-A
2.4.1 PHY/MAC Layer development
We start by recalling that the PHY layer is defined as different transmission
techniques to send the information bits over the air interface. The MAC layer,
on the other hand, describes different flow and multiplexing mechanisms.
In order to satisfy the future requirements of data hungry wireless applica-
tions and services, several efficient physical layer techniques especially from
the relaying aspect, are being proposed for the future mobile systems. For ex-
ample, the wireless transmission in millimeter wave (mmW) frequency bands
is proposed [82,83, 89–92]. Furthermore, other multicarrier techniques in ad-
dition to OFDM are discussed including the Filter Bank Multicarrier (FBMC)
technique [85–88]. The Massive MIMO scheme [92–100] enables enhanced
spectral efficiency and improved coverage. Inband full duplex relaying is also
one of the relaying flavors. There the wireless nodes are able to execute the
transmission and reception operations at the same time and frequency [126].
Finally, several channel sensing and access schemes in combination with re-
laying are being investigated in [117,118,121,122].
Relaying can be utilized in a cooperative fashion to enable additional com-
munication routes for the data between source and destination. This may
take place through e.g. cooperative diversity or network coding [104, 178].
In cooperative diversity, the data may be exchanged between BS and UE via
25
Relaying in LTE-Advanced Systems and Beyond
two paths, i.e., through the direct link and via the RN [129]. Moreover, the
relay selection mechanisms of opportunistic cooperation also increase the at-
tractiveness of RN employment in the future wireless technologies [179,180].
Opportunistic cooperation enables an option to select a RN which provides
the best communication link between the source and destination [110–120].
Similarly, two-way relaying combined with the wireless network coding can
be exploited in future systems to efficiently utilize the available spectrum
[101–109].
2.4.2 Deployment and Usage Scenarios
The inband relaying as compared to out-of-band relaying, is considered to be
spectral efficient due to self-backhauling capability of RL. Self-backhauling
ability provides advantages for the relaying in upcoming future wireless com-
munication technologies. For example, the RL may employ UDN infrastruc-
ture [6]. In UDN, a large number of small cells are deployed in a given geo-
graphic area (e.g., > 103small cells/km2 [181]). To that end, the wireless back-
hauling (especially inband relaying) is an efficient and rapid option in terms
of deployment cost as well as efficient single frequency utilization [142].
Relaying as a part of UDN deployments can exploit the mmW frequency spec-
trum due to the availability of contiguous large blocks of frequency band in
ranges of 30-300 GHz. The usage of mmW for the small cell operation is feasi-
ble but challenging due to the fact that propagation at high frequency is highly
affected by the physical obstruction. Small cell operation on mmW enhances
the system efficiency as well as increases the capacity in a local coverage area.
The self-backhauling capability also makes relaying a good candidate in small
cell deployment scenarios to enable outdoor-to-indoor coverage in dense ur-
ban area [3].
Mobile and nomadic RNs may make the future communication networks
highly dynamic and make it possible to provide better on-board cellular ser-
vices [151, 152]. To that end, the moving/nomadic relaying, even though
adding system complexity and limitations, are envisioned to be a promising
notion in future. Due to usage of wireless backhaul link, moving RNs in e.g.,
buses and trams, can provide wireless connectivity to UEs in close proximity
as well as enhance the data rate experience of UEs. Moreover, it can also mini-
mizes the penetration losses when the RN enables outdoor-to-indoor cellular
coverage. The nomadic nature of relaying can also provide rapid deployment
flexibilities to network operators on demand basis [153] and is envisioned to
26
Relaying in LTE-Advanced Systems and Beyond
play an important role in future communication technologies [141].
Very recently a drone-based relaying mechanism have been proposed in [154]
to mitigate the traffic congestion in a cellular network as well as enhance the
cellular user performance experience. This work proposes a mechanism to
employ the drone RN in hot spots of 5G cellular networks where the network
is unable to support sudden large traffic volume. Hence, RN are envisioned
to be in nomadic mode in the air, providing the cellular services to users on
behalf of macro cell with improved radio conditions as well as minimum in-
terference. This type of relaying can be also efficiently utilized in scenarios of
emergency events or public safety cases.
The D2D communication may also support relaying to enable the wireless
connectivity between two devices rather than connecting them via infrastruc-
ture BS. This helps the network to separate the local traffic operation from the
global traffic and it allows a direct communication between the devices [143].
This approach may facilitates higher spectral and power efficiency and poten-
tial decreases the system cost and latency [1]. Relaying can be also utilized in
D2D [143] assisted communication with purpose of coverage extension espe-
cially in public safety scenarios [81,144,145]. In D2D relaying, a mobile device
can act as a RN to enable transmission towards mobile devices in the outage
areas [143]. In [146], D2D relaying was used to improve performance in terms
of enhancing the cell edge UE throughput.
Relaying node also bears a potential role in MTC. Here, the BS is controlling a
large number of wireless sensors/devices. Accordingly, congestion problems
on the AL may occur. Hence, RN deployed between the BS and sensors can
decrease the access signaling burden on the BS. To that end, RN aggregates
the traffic received from the sensor devices located in its coverage area and
forward it to the controlling BS [3]. Similarly, the utilization of machine-type
UE relaying extends the coverage area and enable communication between
devices and BS [148,149]. It also increases the battery life of the devices [150].
2.4.3 Relay Backhaul Enhancement Issues
The wireless backhaul plays a major role in the REC network. In literature,
different approaches have been investigated to enhance the backhaul per-
formance. To that end, the RN site planning enables the RN to connect to
the eNB with the best RL and/or smallest shadow fading. The RL limi-
tations are being investigated from the network planning and optimization
perspective in [14, 18–20, 22–24, 28, 33, 37, 70, 140, 141]. Moreover, RL limita-
27
Relaying in LTE-Advanced Systems and Beyond
tions are further relaxed from the perspective of different RRM related issues
in [20, 21, 25–27, 29, 55, 70, 70]. The MIMO/Beamforming/Interference miti-
gation aspects are considered in [12, 13, 30–33], elevation of RL antenna and
optimal path loss models are discussed in [36–39]. Moreover, the RL limita-
tions are further relaxed via CoMP Scheme in [34, 35] and some other initia-
tives based on the flexible backhaul are provided in [182]. The contribution
of [12, 13] analyze the backhaul RL limitations due to interference received
from the neighbouring eNBs. To that end, the performance of transmit beam-
forming and interference mitigation schemes to relax the RL limitation by
minimizing the interference from dominant interfering eNB is investigated.
Results show enhanced system performance on RL in terms of SINR per PRB
and e2e UE throughput. Furthermore, the UL performance of a LTE REC net-
work have been examined in [33, 53, 54, 58, 70, 71].
Table 2.2. Relay Backhaul Link Enhancements in the Literature
References Strategies Study Methods
[14, 18–20,22–24,28, 33,
37, 70, 140,141, 182]
Network
Planning/Optimization
& Nomadic
Analytical and/or
Simulations
[20, 21, 25–27,29, 55, 70,
70]RRM Schemes Simulations
[12,13,30–32,35,35,183] CoMP & Beamforming Simulations
[36, 38, 39] RL Path Loss ModelingSimulations & Field
measurements
2.4.4 Future Research Directions
This section briefly describes some of the open issues and future challenges
occurring in the relay systems. The work in [73] highlights the RRM issues
in multi-hop relaying especially from the call admission control and QoS per-
spective. In [74], authors suggested to address the issue of the enhancements
of self-interference cancellation, reduction of Bit Error Ratio (BER) and Packet
Loss Ratio (PLR) in Full Duplex relaying. In addition to self-interference can-
cellation ability, authors in [76] analyzed the FD relaying in mm-Wave fre-
quency ranges from Energy Efficiency (EE) perspective. The introduction of
novel multi carrier modulation technique known as Index Modulation for
OFDM subcarriers, is one of the future aspects. The aim being to make In-
dex Modulation compatible with the cooperative relay communication sys-
28
Relaying in LTE-Advanced Systems and Beyond
tems [77]. In [75], self-sustainable relaying is envisioned to be one of the ini-
tiatives of the future green wireless communication technologies. The author
in [78] evaluates different buffer-aided RN selection algorithms and draws a
broad picture of future challenges to be addressed from the perspective of
buffer-aided relaying in 5G communications. A survey done in [79] discusses
on the role of different relaying strategies in future LTE including Type-1,
Type-2, and moving RN. Figure 2.2 presents the references regarding the fu-
ture challenges of the relaying system.
29
3. Resource Optimal Relaying
3.1 Background
In this chapter, we analyse the performance of dual-hop DF relaying in terms
of e2e data rates when the radio resources are shared between RL and AL
using different Resource Allocation (RA) schemes. While the focus is on the
conventional case where DL and UL are carried out with the same DeNB,
we also briefly consider the impact of different RA schemes in the context of
relaying systems where UEs can decouple DL and UL connections. In what
follows, we apply a simple resource allocation model where only a single user
is considered. We note that this model simplifies the analytical study but prin-
ciples of the proposed resource allocations can be extended to the multi-user
scenarios. Such extension would, however, lead to unnecessary complex anal-
ysis and is not considered in this thesis.
3.2 Previous work and Contributions
3.2.1 Previous work
Traditionally, the performance analysis of dual-hop DF relaying has been con-
ducted in terms of ergodic capacity [184–188], outage capacity [189,190], out-
age probability [191–193], and the bit error probability [193]. In [184], author
studied the ergodic capacity of DF relaying in the presence of Rician fading
and, based on the obtained results, concluded that the combination of the
optimal power control and rate adaptation provides a notable positive effect
on the overall system performance. In [185] and [186], closed form expres-
sions were derived for the ergodic capacity of DF relaying assuming different
31
Resource Optimal Relaying
approaches. For example, Taylor’s series expansions were used in [185] to
determinel the lower and upper bounds for the ergodic capacity, while the
study carried out in [186] focused on the ergodic capacity when indepen-
dent non-identically distributed composite inverse Gaussian/Nakagami-m
random variables were used to model the wireless channel. The research work
done in [189] evaluated the benefits of power allocation on the outage capac-
ity of dual-hop DF relaying. Authors showed that the gain of optimal power
allocation is more pronounced when channel power gain difference grows
between RL and AL. In [190], the outage capacity formula for a dual-hop DF
relaying system was derived when different generalized fast fading channel
models were used.
In [187, 191–193], the performance gain that dual-hop DF relaying provides
when communication takes place in parallel with the direct communication
between eNB and UE is characterized. Authors of [187] studied the ergodic
capacity of DF relaying in presence of independent but non-necessarily iden-
tically distributed Nakagami-m fading. Similarly, the average symbol error
probability and outage probability was calculated in [191] by studying the in-
stantaneous received e2e SNR of dual-hop DF relaying in case of Nakagami-m
fading. Moreover, the authors of [192, 193] obtained closed form expressions
for the outage and bit error probability of DF relaying in presence of dis-
similar Rayleigh fading channel models. The ergodic capacity analysis was
extended in [188] for the multi-hop DF relaying scenario, where two upper
bounds were obtained using the Jensen’s inequality and by computing the
harmonic-geometric means in case of a Rayleigh fading. Finally, the authors
of [131] performed a comparative study between the dual-hop regenerative
(i.e., DF) and the non-regenerative (i.e., AF) relaying systems, by deriving
closed form expressions for the statistics of SNR harmonic means of both
links. Moreover, the impact of buffering on the performance of DF relaying
has been investigated in [194]. Also, different authors have studied the re-
laying performance in terms of enhanced spectral efficiency by proposing the
implementation of rate adaptation and relay selection scheme [195], as well
as adaptive modulation schemes [196] while the performance and energy ef-
ficiency of REC networks has been investigated in e.g. [19, 42, 197–205].
It is worth noticing that in all these previous works, the communication re-
sources of the dual-hop relaying system are equally shared between RL and
AL. Though, some recent work [195, 196] considered the optimal RA scheme
introduced in [45]. This study recall the said optimal RA scheme where the
same amount of data is transferred over both RL and AL at each radio frame.
32
Resource Optimal Relaying
According to our best understanding, analysis of e2e data rate and the deriva-
tion of closed-form expressions for its distribution and expected value in pres-
ence of optimal resource allocation have not been well addressed before our
publication [10]. Part of the material of this chapter will be also published
in [11].
3.2.2 Contributions
The main contributions of this chapter are the following: First, we deduce
closed-form expressions that describe the performance impact of the resource
allocation on the relaying when the UE is connected to the same RN and DeNB
in DL and UL. Second, we consider the case where the DL/UL communica-
tion between the source and destination via RN, is decoupled, meaning that
the source (destination) node of a DL (UL) transmission can be any macro
eNB that has the target UE in range (via an intermediate RN).
To that end, the first case includes the comparative analysis of several RA
schemes: that is, conventional RA (where the RL and AL equally share the
time resources), fixed RA (where the RL and AL resources are proportionally
allocated according to their corresponding mean data rates), and optimal RA
(where the RL and AL resources are allocated according to the instantaneous
data rates in each link). Moreover, we also derive a closed-form expressions
for the probability distribution function for the mean and outage rate of the
dual-hop DF relaying system, by assuming the optimal RA scheme. While the
mean e2e data rate attains an expression in terms of an integral which does
not admit closed-form solution, so we can deduce a tight lower bound expres-
sion which approximates accurately the mean e2e data rate. Results are being
published in [10].
In case of DL/UL decoupling, we also analyze the e2e data rate performance
when different RNs, while possibly connected to different macro eNBs, are
being employed to retransmit the information in both DL (eNB-RN-UE) and
UL (UE-RN-eNB) direction of communication. Study assumes the adaptive
modulation and coding in both RL and AL, in order to obtain improved spec-
tral efficiency for the given SNR value. The work includes the derivation of
closed form expressions of e2e data rate for both DL and UL. Results will be
published in [11].
Chapter is organized as follows: Section 3.3.1 present the system model of
both coupling and decoupling cases and gives the channel modeling assump-
tions. Section 3.3.2 explains the different RA schemes focusing on the differ-
ences between mentioned conventional RA, fixed RA with/without buffering,
33
Resource Optimal Relaying
and optimal RA. Moreover, Section 3.4.1 provides the performance analysis of
the studied RA schemes. Finally, conclusions are drawn in Section 3.5.
3.3 System Model and Resource Allocation Schemes
3.3.1 System Models
DL/UL coupled scenario
The considered dual-hop relay system is shown in Figure 3.1. We assume that
RN is acting as an intermediate node between UE and eNB and the communi-
cation between both will always be taken via RN. We denote the radio frame
duration by T and assume the radio resources can be shared in time between
RL and AL assuming an infinitesimal granularity. We employ the HD relay-
ing, where RN cannot transmit and receive information simultaneously. We
denote T = Tr + Ta, where Tr and Ta refer to transmission times allocated for
RL and AL, respectively. Moreover, the Rr and Ra denote the instantaneous
data rates on RL and AL in bits per second. We note that block fading model
is assumed where the channel remains constant over each TTI but change be-
tween every TTIs depending upon the channel statistics. Furthermore, the
RL and AL data rates are dependent on the SNR levels and the bandwidths
allocation to both RL and AL. Hence, the achievable data rates during a TTI
on RL and AL are given by Shannon’s formula as
Rr = Wr log2(1 + γr), and Ra = Wa log2(1 + γa), (3.1)
where Wr and Wa refer to the bandwidths allocated to RL and AL, respec-
tively, while γr and γa represent the instantaneous SNR values experienced on
RL and AL, respectively. In fixed relaying, RN is part of the network and RN
locations are defined in the network planning process, so that a very strong
Line-of-Sight (LoS) maybe experienced on RL towards the DeNB. We note
that in 3GPP system scenarios channel models for the RL experience LoS as
well [9]. Due to LoS, there is high likelihood that RL SNR admit only small
variations and hence, Ricean distribution can be applied to model the fading
in RL [206, 207]. On the other hand, a Non-Line-of-Sight (NLoS) condition is
assumed in AL because, a UE typically changing its location due to mobility.
Thus, we can apply the Rayleigh distribution in order to model the fast fading
component of the channel in the AL, as shown in Figure 3.1.
34
Resource Optimal Relaying
RL(Ricean fading)
DonoreNB
RN UE
AL(Rayleigh fading)
Figure 3.1. System model for the fixed relaying system consisting of a eNB, RN, and UE. Fast
fading component of the wireless channel in the RL and AL are modeled according
to a Ricean (LoS) and Rayleigh (NLoS) distribution.
DL/UL Decoupling scenario
In this scenario, we consider a two-cell system model of Figure 3.2, where
RNs are associated to different macro eNBs and serve a common UE located
at the cell edge of both cells. It is possible to enhance the performance in both
communication directions by applying the UL and DL decoupling whenever
it is suitable from the e2e data rate perspective. We assume Frequency Di-
vision Duplexing (FDD) to enable the simultaneous transmission in both UL
and DL directions. Moreover, RNs are HD and the temporal resources are or-
thogonally shared between AL and RL in both directions. No power control
is applied in DL but in UL direction UE uses an open-loop power control al-
gorithm with target received power p(ul)0 [146]. The channels are modelled as
in the aforementioned coupled case.
Donor eNB 1
RN 1
UE
DownlinkServing Cell
Donor eNB 2
RN 2
UplinkServing Cell
Uplink Power Control (P0)
Figure 3.2. System model for a two-cell DF relaying system with UL and DL decoupling. No
power control is implemented in DL. Open-loop power control with target received
power p(ul)0 is used in UL to control the interference.
35
Resource Optimal Relaying
3.3.2 Resource Allocation Schemes
We consider four different RA schemes: Conventional RA, Fixed RA
with/without buffer, and Optimal RA. The conventional RA and fixed RA
represent benchmark for the optimal RA scheme. The reference RA schemes
allocate the communication resources to AL and RL with fixed Ta and Tr.
Conventional RA. In a conventional RA scheme [208, 209], the time resources
are equally divided between AL and RL, i.e., Ta = Tr = 0.5 · T . Thus, the
conventional e2e data rate per unit bandwidth (Rconve2e ) for this RA scheme at a
given fixed transmission power, will be as
Rconve2e =
1
2min{Rr , Ra }. (3.2)
Moreover, the e2e performance depends on the RL and AL SNRs while the
link with lowest SNR will become a bottleneck for the overall e2e data rate.
Here, the factor 1/2 represents the half-duplex constraint.
Fixed RA without buffer. If there is no buffer, then the values of Ta and Tr are
chosen proportional to the mean data rate of each link, i.e., E{Rr} and E{Ra}.
Hence, we write
T Rwobe2e = Ta E{Ra} = Tr E{Rr}. (3.3)
From (3.3), it can be seen that
Ta =Tr E{Rr}E{Ra} , Tr =
Ta E{Ra}E{Rr} . (3.4)
In general E{Rr} �= E{Ra}, and thus the time allocations for each link will
be different i.e., Tr �= Ta verifying Ta + Tr = T . The overall payload data
transmitted over the e2e link is given by
P = min {TaRa, TrRr} . (3.5)
Using (3.5), the e2e data rate can be written as follows:
Rwobe2e =
P
T=
min {TaRa, TrRr}T
,
=1
Tmin
{Tr E{Rr}E{Ra} Ra, TrRr
},
=1
Tr +Tr E{Rr}E{Ra}
min
{Tr E{Rr}E{Ra} Ra, TrRr
}
(3.6)
36
Resource Optimal Relaying
Here, we can apply the formula min{X,Y } = 1/2(X + Y − |Y −X|) in (3.6)
and obtain
Rwobe2e =
E{Ra}Tr (E{Ra}+ E{Rr})
1
2
[Tr E{Rr}E{Ra} Ra + TrRr −
∣∣∣∣Tr E{Rr}E{Ra} Ra − TrRr
∣∣∣∣]
=1
2
E{Ra}E{Ra}+ E{Rr}
[E{Rr}E{Ra}Ra +Rr −
∣∣∣∣E{Rr}E{Ra}Ra −Rr
∣∣∣∣].
(3.7)
From (3.7), three different e2e data rate formulas are obtained by solving the
modulus operator, i.e.,
Rwobe2e =
⎧⎪⎪⎪⎪⎨⎪⎪⎪⎪⎩
Rr E{Ra}E{Rr}+E{Ra} ,
E{Rr}E{Ra}Ra > Rr,
Ra E{Rr}E{Rr}+E{Ra} ,
E{Rr}E{Ra}Ra < Rr,
12Rr E{Ra}+Ra E{Rr}
E{Rr}+E{Ra} , E{Rr}E{Ra}Ra = Rr.
(3.8)
Fixed RA with buffer. In fixed RA scheme with buffer, RNs have large buffers
which avoid data overflow during the transmissions. In buffer-aided relay-
ing, RN can better use the CSI while selecting between reception in RL and
transmission in AL. RN can also utilize the buffer effectively in scheduling.
In buffer-aided relaying, the eNB can continue the transmission on the RL
without taking into account of the AL channel quality. When the AL is in
deep fade, the data can be stored at the RN’s buffer until the AL channel con-
dition becomes suitable for transmission again. Then, the RN can forward
the previously buffered data to UE, provided that the AL channel conditions
are favorable [3, 78, 84]. We denote the e2e data rate by Rwbe2e for this relaying
setup. Though the same amount of data is transferred over both RL and AL
in long-term, the relation
T · E{Rwbe2e} = Ta · E{Ra} = Tr · E{Rr} (3.9)
should hold. Then, we find that
E{Rwbe2e} =
Tr · E{Rr}Tr + Ta
=E{Rr}
1 + Ta/Tr
=E{Rr}E{Ra}
E{Rr}+ E{Ra} .(3.10)
Optimal RA. The optimal resource sharing notion for the L-hops connections
has been considered previously in [45]. In optimal RA scheme, the e2e data
rate Ropte2e is maximized, which happens when the same amount of data is
transferred over each link, i.e., RL and AL, at each TTI. This is equivalent to
T Ropte2e = TrRr = TaRa. (3.11)
37
Resource Optimal Relaying
To obtain the condition (3.11), it is required to properly allocate the radio re-
sources among RL and AL. We note that the selection of Tr, Ta, Wr, and Wa
depends on the instantaneous SNR of each link. To that end, one degree of
freedom is to adjust the transmission power of RN. However, this study con-
siders the LTE-A style fixed relaying, where the transmission power of eNBs
and RNs in DL, is kept constant in order to prevent the potential harmful co-
channel interferences toward the adjacent cells. Hence, we are left with time
and bandwidth resources to obtain (3.11). Implementation of resource opti-
mal relaying (such as Type 2 DF relaying in LTE-A and Mobile WiMAX [210])
require flexibility to allocate the radio resources in time-frequency domain,
so that optimal resourcing can be achieved, or at least, well approximated.
Using (3.1) and (3.11), the expression for the optimal e2e data rate can be ob-
tained as follows:
Ropte2e =
Tr Rr
Tr + Ta=
Rr
1 + Ta/Tr=
Rr
1 +Rr/Ra=
Rr Ra
Rr +Ra. (3.12)
Performance comparison of the different RA schemes. While, comparing the effi-
ciencies of the mentioned RA schemes, we find that
Rmax ≥ E{Rwbe2e} ≥ E{Ropt
e2e} ≥ E{Rwobe2e } ≥ E{Rconv
e2e } (3.13)
It is known that in conventional RA scenario, RN does not have a buffering
capability and thus, it always equally divides the communication resource
between RL and AL. Obviously, performance of conventional RA is clearly
inferior to the rest of the RA strategies. Still, the conventional RA is the sim-
plest scheme to distribute the communication resources in practice. Fixed RA
without buffer allocates the time resources of the radio frames to fit with the
long-term (expected) data rates of each individual link i.e., RL and AL, as
given by (3.3). Though, the e2e data rate of this relaying RA scheme (having
no buffering capability) is inferior to the fixed RA scheme with buffer as well
as to the optimal RA scheme that adjusts the transmission times and data
rates in RL and AL without delay, see (3.11). Furthermore, it is noted that
the relaying system with buffer ability, outperforms the resource optimal RA
scheme as given in (3.12) when expected e2e data rates are compared. While it
can be Ropte2e > Rwb
e2e for instantaneous data rates, in fixed RA with buffer it is
possible to set Rr = E{Rr} and Ra = E{Ra}. Note also that Jensen’s inequal-
ity verifies E{Rwbe2e} ≥ E{Ropt
e2e}, see (3.12) and (3.13). However, it is worth to
mention that the buffer usage leads to additional latency as well as additional
hardware costs, as the data blocks transported over the RL and AL of a radio
frame do not usually match over the same TTI.
38
Resource Optimal Relaying
Figure 3.3. Cumulative Distribution Function of the instantaneous gains that resource optimal
RA provides with respect to the other RA schemes in a dual-hop DF relaying. Pa-
rameters: Wr = Wa = 180 kHz, E{γr} = γr = 15dB (Ricean K-factor = 12 dB),
and E{γa} = γa = 5dB.
Figure 3.3 shows the CDF of the gains that can be achieved by means of
the resource optimal RA with respect to the above mentioned baseline RA
schemes (i.e., conventional RA and fixed RA with and without buffer). It is
observed that the Optimal RA always enables significant performance gains
when compared to conventional and fixed RA without using buffer. We recall
that in case of the fixed RA with buffer, it is possible to set Rr = E{Rr} and
Ra = E{Ra}; hence, sometimes Ropte2e < Rwb
e2e, as shown in Figure 3.3. Yet, Ropte2e
represents the rate-optimal RA scheme provided that there is no buffer at RN.
3.3.3 Role of the Channel State Information
We assume that the DL receiver has always short-term CSI which is obtained
from pilot signals through a suitable channel estimation process, while, trans-
mitter may have either long-term or short-term CSI only. Hence, the receiver
enables the CSI via a certain feedback mechanism. Feedback is fast for short-
term CSI and slow for long-term CSI. Here, ’fast’ and ’slow’ denote the speed
of the feedback provision when compared to the channel coherence time.
It is assumed that a RA scheme applying instantaneous rates (i.e. Rr and Ra)
should have short-term CSI of both links, i.e., RL and AL, in order to take the
RA decisions per TTI. This short-term CSI is needed in the resource optimal
RA scenario. Here, the RN should request CSI report from the UE before per-
forming the allocation of resources (jointly with DeNB) on RL and AL. More-
over, in case of fixed RA without buffer, the communication time resources,
Tr and Ta are chosen proportional to the mean data rate of RL and AL. Thus,
there is only need of long-term CSI to allocate the communication resources
which, after being determined, are kept fixed for all TTI. In the same way,
fixed RA with buffer also require only long-term CSI. Finally, we recall that
39
Resource Optimal Relaying
if fast radio layer scheduling is applied in a radio link, then short-term CSI is
always needed.
3.4 Performance analysis
3.4.1 DL/UL coupled scenario
In the following, the CDF of the e2e data rate is obtained and, then, closed
form expressions are deduced for both the mean and the outage rates.
SNR distribution in the Relay Link
In case of resource optimal RA, the e2e data rate is obtained by combin-
ing (3.1) and (3.12) as follows:
Ropte2e =
Wr log2(1 + γr)Wa log2(1 + γa)
Wr, log2(1 + γr) +Wa log2(1 + γa), (3.14)
where γr and γa are the instantaneous SNRs of the RL and AL, respectively.
If the fast fading components of these links admit Ricean (LoS) and Rayleigh
(NLoS) fading distributions, then the Probability Density Functions (PDFs)
for γr and γa are given by [207]:
fγr(γ) =K + 1
γre−Kγr+(K+1)γ
γr I0
(2
√K(K + 1)γ
γr
)(3.15)
and
fγa(γ) =1
γae− γ
γa . (3.16)
Here γr and γa refer to the mean SNR in RL and AL. Moreover, in (3.15),
I0 is the zero order modified Bessel function of the first kind [211], which
makes the mathematical analysis of (3.14) challenging. Nevertheless, in case
of strong LoS scenarios, the Ricean K-factor can be large, as noted in [206],
where typical value was of the order 12dB. Presuming a high K-factor, the RL
SNR is almost constant in LoS condition. Although results of [206] enables a
good justification for this assumption, we have also verified it via simulations:
40
Resource Optimal Relaying
Figure 3.4. Accuracy of the constant SNR assumption on RL. The CDF of the Re2e is presented
here for different combinations of mean SNR in both RL (γr) and AL (γa).
The CDF of Ropte2e in (3.14) is presented by Figure 3.4, where the AL SNR is
considered to always distributed according to (3.16). Furthermore, we com-
pare two scenarios: First (solid curves), it is assumed that fast fading in the
RL is distributed according to (3.15). Second (dashed curves), we assume a
constant RL SNR, i.e., (γr = γr).
Figure 3.4 presents the CDF of Ropte2e for different values of γr and γa, with
K = 12dB in all cases. It is observed that the assumption of constant SNR is
valid not only in terms of the resulting distributions, but also in terms of the
deviation of the average Ropte2e , where for values K > 12 dB it was verified that
the approximation error is always smaller than 1%. We also note that using
the Jensen’s inequality we obtain
E{Rr} = E{log2(1 + γr)} ≤ log2(1 + E{γr}), (3.17)
where the lower bound for the mean e2e data rate becomes tight as the K-
factor of the Ricean distribution grows.
Probability distribution for the e2e data rate
This subsection presents the e2e data rate distributions, that is CDF FRopte2e
and PDF fRopte2e
, when resource optimal RA scheme is employed. We note
that the SNR on the RL is assumed to be constant while the AL SNR - de-
noted by γa - admit uncorrelated values from TTI to TTI. From (3.12), the
CDF FRopte2e
can be deduced using the standard mapping procedure between
random variables [212]. First, we set re2e = g1(Ra) = Rr · Ra/(Rr + Ra) and
after solving the instantaneous AL data rate Ra in terms of re2e, we obtain
g−11 (re2e) = re2e ·Rr/(Rr− re2e). The CDF of Ropt
e2e admits the following formu-
lation:
FRopte2e
(re2e) = FRa(g−11 (re2e)) = FRa
(re2e ·Rr
Rr − re2e
). (3.18)
41
Resource Optimal Relaying
Second, the SNR in the AL can be expressed as a function of γa as follows;
γa = g−12 (ra) = e(
ln 2Wa
·ra) − 1. Then, it is possible to write the CDF of Ra as:
FRa(ra) = Fγa(g−12 (ra)) = Fγa
(e
ln 2Wa
·ra − 1). (3.19)
After combining (3.12), (3.18) and (3.19), FRopte2e
is given by
FRopte2e
(re2e) = Fγa
(e
ln 2Wa
re2eRr(Rr−re2e) − 1
), (3.20)
where FRopte2e
(re2e) = 0 if re2e < 0 and FRopte2e
(re2e) = 1 if re2e ≥ Rr. Thus,
using (3.16), FRopte2e
becomes
FRopte2e
(re2e) = 1− e− 1
γa
(eln 2Wa
re2eRr(Rr−re2e)−1
). (3.21)
Using (3.20) we can deduce CDF for e2e data rate Re2e also in case AL SNR
admit e.g. Nagakami or Weibull distributions. The probability density fR(r)
of e2e data rate can be obtained by taking the derivative of CDF (3.21):
fRopte2e
(re2e) =ln 2
γaWaexp
⎛⎝−
exp(
ln 2Rr re2eWa (Rr−re2e)
)− 1
γa
⎞⎠
× R2r
(Rr − re2e)2 exp
(ln 2Rr re2e
Wa (Rr − re2e)
).
(3.22)
The e2e data rate PDF fRopte2e
and CDFFRopte2e
are illustrated by Figure 3.5 and 3.6,
respectively. Here, in Figure 3.6, the mean SNR on the RL (γr) is assumed to
be 20 dB. The dashed vertical line on the right-hand side of the figure denotes
the upper limit for the e2e data rate. This upper limit is obtained if the SNR
on AL grows very large (i.e., upper bound for Ropte2e). It also includes the CDF
curves for AL SNR of 0 dB (black curves), 10 dB (orange curves) and 20 dB
(green curves).
Results show the impact of bandwidth allocation between RL and AL. By as-
suming Wa/Wr = 1 (dotted curves), and Wa/Wr = 2 (dashed curves) and
Wa/Wr = 4 (solid curves), we find that the distribution of re2e tends asymp-
totically to Rr with increasing values of γa and Wa/Wr. It can be seen from
results that the imbalance between the mean SNR on the RL and AL signif-
icantly deteriorate the e2e data rate performance even though the resource
optimal RA scheme is applied. If the mean SNR in AL is considerably lower
than the mean SNR in RL, then this imbalance can be compensated by allo-
cating more frequency resources to the AL (see black curves).
42
Resource Optimal Relaying
0 2 4 6 8 10
105
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 10-5
Figure 3.5. PDF of e2e data rate when SNR γr on the RL is 20 dB and Wr=180 kHz.
0 2 4 6 8 10
105
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 3.6. CDF of e2e data rate when SNR γr on the RL is 20 dB and Wr=180 kHz.
It is noticed that for Wa/Wr = 4 the system gains for γa=10 dB and γa=20 dB
as compared to γa=0 dB at 10%-ile, 50%-ile, 90%-ile are 383%, 107%, 45%, and
750%, 153% and 62%, respectively. Moreover, in case of low mean SNR in
AL, the allocation of additional frequency resources to AL doesn’t lead to any
notable enhancement in the e2e data rate (see solid black curve).
From Figure 3.6 we find that if the mean SNRs in RL and AL are the same (see
green curves), then the e2e data rate is significantly enhanced in both low
and high probability regions when increasing the frequency resources in AL.
For γa = 20 dB, the system performance gain for Wa/Wr = 2 and Wa/Wr = 4
compared to Wa/Wr = 1 at 10%-ile, 50%-ile, 90%-ile are 49%, 35%, 29%, and
43
Resource Optimal Relaying
96%, 63% and 56%, respectively. Hence, it can be said that increasing the
resources in AL, will results a positive impact on e2e performance when the
RL and AL SNR imbalance is considerably small.
It is observed that even though the AL resources are four times larger than in
RL the e2e data rate is not very close to the performance upper bound. The
e2e data rate of the resource optimal RA scheme asymptotically approaches
the upper limit when Wa/Wr increases. Yet the increased e2e data rate effi-
ciency from additional frequency resources in AL decreases when the Wa/Wr
becomes large.
Mean e2e data rate for the resource optimal RA scheme
This subsection presents the mean (average) data rate Cav for the resource
optimal data rate adaptation over the e2e link as given by computing the ex-
pected value of Ropte2e :
Cav � E{Ropte2e} =
∫ ∞
0r fRe2e(r) dr. (3.23)
We note that this definition of mean data rate is well in line with the con-
ventional definition of mean data rate for a single-hop link as given in [213].
Equation (3.23) can be written in the form
Cav =
∫ ∞
0
Rr · raRr + ra
fRa(ra)dra =
∫ ∞
0
Rr ·Wa log2(1 + γa)
Rr +Wa log2(1 + γa)fγa(γa) dγa. (3.24)
Thus mean data rateCav is computed for fixed AL bandwidthWa and RL data
rate Rr.
At this stage we note that since τr = Tr/T = Ropte2e/Rr, the expected time shar-
ing for RL is given by E{τr} = Cav/Rr. This value can be used if time sharing
between RL and AL needs to be set. In the following we consider the com-
putation of Cav in the case of exponentially distributed AL SNR. Since CDF
of Ropte2e is easier to handle than the PDF, we use integration by parts to obtain
the form:
Cav =
∫ ∞
0
(1− FRopt
e2e(r)
)dr.
Then, we apply (3.21) and the following substitution:
r =Rr · Wa
ln 2 ln t
Rr +Waln 2 ln t
, dr =R2
r · Waln 2 · dt
t(Rr +
Waln 2 ln t
)2 .Since limr→0 t(r) = 1 and t(r) → ∞ when r → Rr, Cav can be re-written as
follows:
Cav = R2r ·
Wa
ln 2
∫ ∞
1
e−(t−1)/γadt
t(Rr +
Waln 2 ln t
)2 . (3.25)
After integration by parts, we can write (3.25) in the form
Cav = Rr
(1− 1
γae1/γa
∫ ∞
1
e−t/γadt
1 + WaRr ln 2 ln t
). (3.26)
44
Resource Optimal Relaying
Since closed form expression for the integral in (3.26) does not exist, we will
later deduce a tight lower bound for the Cav which can serve also as a good
approximation. Let us consider next the distribution for the relative time
(τr = Tr/T ) to determine the time resource allocation for the RL transmission.
According to (3.11), we have τr = R/Rr and
Fτr(τ) =1
RrFR
(τ
Rr
). (3.27)
The time sharing τa = Ta/T for AL can be computed from the equality τa =
1−τr. The CDF presented in (3.27) can be used in the network planning phase
for the dimensioning. This requires that the relative transmission time used
in the RL is under some predefined threshold τmaxr with a given probability
Pmaxr . This requirement will help to save some communication resources in
RL, in order to enable service to those UEs that are directly connected to DeNB
on the direct link.
It is noted that (3.27) also indicates the relative data rate loss when compared
to the ideal RL. The loss in the e2e data rate when compared to the ideal RL,
is Ra − R, and the relative loss is δR = (Ra − R)/Ra = R/Rr. This relative
data rate loss can be utilized as a parameter to compare the data rate perfor-
mance between RN (with data rate limitation in the RL) and pico base station
(without data rate limitation in the backhaul).
Outage rate for the resource optimal RA scheme
The outage rate is given as a largest data rate Rout such that P (R < Rout) =
P out is verified, where P out is defined as the outage probability [213]. In order
to derive a mathematical expression for the data rate Rout, we write
P out = P (Ropte2e < Rout) = P
(Ra ·Rr
Ra +Rr< Rout
)
= P
(Ra <
Rr ·Rout
Rr −Rout
)= Fγa
(e
ln 2Wa
· Rr·Rout
Rr−Rout − 1
),
(3.28)
where the latter equality follows from (3.20). The outage rate Rout can be
computed from (3.28) analytically, provided that the inverse of Fγa(γ) admit
closed-form expression. If AL SNR follows the exponential distribution, then
we find that
Rout =Rout
a ·Rr
Routa +Rr
=Waln 2 ln
(1− γa ln
(1− P out
)) ·Rr
Waln 2 ln (1− γa ln (1− P out)) +Rr
,
(3.29)
where Routa is the outage rate obtained from
Routa = Wa · log2(1 + γout
a ), Fγa(γouta ) = P out. (3.30)
45
Resource Optimal Relaying
Hence, from (3.29) and (3.30), we see that e2e data rate formula (3.12) defines
a direct relation between the outage rate in AL and e2e link. Here we recall
the discussion on the time sharing between RL and AL. It is noted that using
the distribution (3.27), we can similarly with (3.29) compute the time sharing
threshold τmaxr for a given excess probability Pmax
r .
Figures 3.7 and 3.8 present the mean e2e data rate Cav and outage rate Rout,
respectively as a function of mean AL SNR (γa). Here, the mean SNR in RL
is 20 dB. The black horizontal dotted line denotes the upper limit for Cav and
Rout. It is shown that both parameters increase with the increase of mean AL
SNR γa.
-5 0 5 10 15 20 25 30 35 40 45 50γa [dB]
0
2
4
6
8
10
12
Average
e2erate
Cav
[bps]
×105
Wa= Wr
Wa= 2Wr
Wa= 4Wr
Rr
Figure 3.7. Mean e2e data rate as a function of AL SNR γa when SNR γr on the RL is 20 dB and
Wr=180 kHz.
It is observed from Figure 3.7 that the mean e2e data rate with resource op-
timal RA scheme is significantly enhanced when employing additional fre-
quency resources in the AL. We consider two example cases: Cav = 0.25Mbps
and Cav = 0.75Mbps. We note that Cav = 0.25Mbps is obtained when
γa = −1dB for Wa/Wr = 4, and γa = 7dB for Wa/Wr = 1. However, if
we target to the mean data rate of 0.75Mbps, then the mean SNR in the AL
should increase to γa = 12dB for Wa/Wr = 4, and γa = 40dB for Wa/Wr = 1.
46
Resource Optimal Relaying
-5 0 5 10 15 20 25 30 35 40 45 50γa [dB]
0
2
4
6
8
10
12
Outage
e2erate
Rout[bps]
×105
P out = 5%, Wa= Wr
P out = 0.5%, Wa= Wr
P out = 5%, Wa= 2Wr
P out = 0.5%, Wa= 2Wr
P out = 5%, Wa= 4Wr
P out = 0.5%, Wa= 4Wr
Rr
Figure 3.8. Outage e2e data rate as a function of AL SNR γa when SNR γr on the RL is 20dB
and Wr=180 kHz.
Similarly, Figure 3.8 presents the outage rate for the outage probability 5%
(solid curves) and 0.5% (dashed curves), respectively. As it is expected, the
outage data rate Rout decreases when decreasing the outage probability. It
can be also seen that as the mean SNR in the AL increases, the dashed and
solid curves with the same color come closer to each other. By this, it can be
concluded that the performance loss for guaranteeing a better e2e radio link
reliability (lower outage probability) can be partly compensated by allocating
more resources to AL. It is also observed that the impact of a larger bandwidth
allocation is more prominent when the AL SNR is low.
Lower bound for the average e2e data rate
The closed-form expression for (3.26) does not exist. In order to obtain the
computable expression, the integration domain in (3.26) is divided into three
sub-intervals such that
I =
∫ ∞
1
e−t/γadt
1 + WaRr ln 2 ln t
= I1 + I2 + I3,
where the corresponding integration domains are (1,mγa), (mγa,Mγa) and
(Mγa,∞), m < 1 < M . The rationale behind this approach is that each inte-
gral Ix with x = {1, 2, 3} admit a closed form bound. Exact expressions for
bounds are derived below.
For the first integral we have
I1 <N∑
n=0
(−1)n
n!
(1
γa
)n ∫ mγa
1
tn
1 + WaRr ln 2 ln t
dt, (3.31)
where N is odd. Thus, we replace in integral of (3.26) the exponential term by
47
Resource Optimal Relaying
its Taylor expansion. We note that (3.31) provides an upper bound only when
the number of terms in the sum is odd. The right side function in (3.31) can
be integrated to obtain a closed-form expression as given by (3.33). We start
computation by writing∫ mγa
1
tn
1 + b ln tdt =
∫ mγa
0
tn
1 + b ln tdt−
∫ 1
0
tn
1 + b ln tdt. (3.32)
In order to find the expression in terms of exponential integral function, we
substitute s = tmγa
and dt = mγads. Note that s = 0mγa
= 0 for t = 0 and
s = mγamγa
= 1 for t = mγa. Hence, integrals can be rewritten as
∫ 1
0
(mγas)n
1 + b ln(mγas)mγa ds−
∫ 1
0
tn
1 + b ln tdt
=(mγa)
n+1
b
∫ 1
0
sn
(1b + ln(mγa)) + ln sds−
∫ 1
0
tn
1 + b ln tdt
To further simplify the above expressions, we apply∫ 10
xp−1
q + lnxdx =
e−pqEi(pq) given in [211, Prop. 4.281.3]. Then, after tedious but straightfor-
ward computation we obtain
I1 <N∑
n=0
(−1)n
n!
(1
γa
)n e−nb
b
(Ei(n ln(e
1bmγa))− Ei(
n
b)). (3.33)
The upper bound for the integral I2 can be calculated by assuming the lin-
ear interpolation Ψ(t) on interval (mγa,Mγa) such that Ψ(mγa) = ln(mγa),
Ψ(Mγa) = ln(Mγa). Then Ψ(t) < ln t on (mγa,Mγa) and we have
I2 <
∫ Mγa
mγa
e−t/γadt
1 + bΨ(t)=
∫ Mγa
mγa
e−t/γadt
1 + b(pt+ q), (3.34)
where
p =ln(M/m)
(M −m)γa, q = ln(mγa)−
(m
M −m
)ln(M/m).
Exploiting p, q and the exponential integral function from [214] as given∫ eCZ
AZ +B= 1
AEi
(C(BA + Z)
)e−
BCA , integral in (3.34) can be rewritten in
the form
I2 =
(k2 γab k1
)· e
⎛⎝k2 + bM ln(mγa)− bm ln(Mγa)
b k1
⎞⎠
×(E1
(k2
1 + b ln(mγa)
b k1
)− E1
(k2
1 + b ln(Mγa)
b k1
)).
where k1 = ln (M/m) and k2 = M −m.
Finally, we use a simple bound in the interval (M γa,∞) as given:
I3 <
∫ ∞
M γa
e−t/γa dt
1 + WaRr ln 2 ln(Mγa)
=γa e
−M
1 + WaRr ln 2 ln(M γa)
. (3.35)
48
Resource Optimal Relaying
This formula is accurate when Mγa >> 1. Combining the above results we
obtain a lower bound for Cav:
Cav > CLB = Rr ·(1− 1
γae1/γa(I1 + I2 + I3)
). (3.36)
0 2 4 6 8 10 12 14 16 18 200.2
0.4
0.6
0.8
1
0 2 4 6 8 10 12 14 16 18 200
5
10
15
Figure 3.9. Upper: Mean e2e data rate as a function of the SNR in the AL and the lower-bound
given by (3.36). Lower: Accuracy of (3.36), error = 100 Cav−CLBCav
. Optimization for
γa ∈ [0, 20]dB: m = 0.995 and M ∈ [2.42, 2.61].
Figure 3.9 shows that the accuracy of CLB increases rapidly with γa, with an
error smaller than 2% for γa > 2dB, which is the range of interest from a
practical point of view. The applied m and M has been derived via numerical
methods.
3.4.2 DL/UL decoupling scenario
Here we analyse the e2e data rate of a dual-hop relaying for the case when DL
and UL are decoupled. This discussion is based on the work in [11]. Like in
the coupled case, we assume a block fading model and recall the (3.5) which
gives the e2e data rate for the dual-hop relaying system:
Re2e T = min{Ra Ta, Rr Tr}. (3.37)
We consider two RA schemes to distribute the communication resources be-
tween RL and AL. First, Fixed long-term RA scheme scheme allocates resources
in each direction on long-term basis such that mean e2e data rates in both RL
49
Resource Optimal Relaying
and AL are identical. Then we have
T fixa
T=
E{Rr}E{Ra}+ E{Rr} ,
T fixr
T=
E{Ra}E{Ra}+ E{Rr} . (3.38)
If we use (3.38) in (3.37), then the instantaneous e2e data rate that the dual-hop
relaying system is able to support is given by
Rfixe2e = min
{Ra E{Rr}
E{Ra}+ E{Rr} ,Rr E{Ra}
E{Ra}+ E{Rr}}. (3.39)
As a second scheme we apply Resource Optimal RA scheme. We recall from
section 3.3.2 that the resource optimal RA scheme allocates resources on
short-term basis such that Ra Ta = Rr Tr is valid in each TTI. Then we have
T opta
Ttot=
Rr
Ra +Rr,
T optr
Ttot=
Ra
Ra +Rr(3.40)
and there holds
Ropte2e =
RaRr
Ra +Rr. (3.41)
These formulas are valid for UL and DL.
Let us next consider the following formulation of the instantaneous received
SNR:
γa = γa|ha|2, γa =(Ptx,a/La)
PN,a, (3.42)
where |ha|2 refers to the channel power gain which is exponentially dis-
tributed with unitary mean. Moreover, Ptx,a, PN,a, and La denote the trans-
mission power, noise power, and mean path loss attenuation, respectively. If
a strong LoS condition is assumed in the RL (Ricean fading with large K-
factor), then the instantaneous received SNR remains close to its mean value
and approximately there holds γr =(Ptx,r/Lr)
PN,r.
We recall that for the data rates on AL and RL we use formulas
ra = Wa log2(1 + γa), Rr = Wr log2(1 + γr). (3.43)
It is noted that the macro eNB use orthogonal resources in RL to communi-
cate with the RNs in its coverage area. This condition does not hold for AL,
where the whole communication bandwidth can be re-utilized by each RN to
provide services to its associated UEs. We also note that Wa is affected by the
parameters of the open-loop power control mechanism in UL. In practice, the
value that AL bandwidth Wa may take for a given UE depends on the (max-
imum) transmission power p(ul)max, the UE-eNB distance deNB,ue, and the target
received signal power per PRB p(ul)0 which needs to be guaranteed [146].
50
Resource Optimal Relaying
Fixed (long-term) RA scheme
If time slots allocated to RL and AL, are defined by (3.38), then the mean e2e
data rate is given as
Rfixe2e = E
{Rfix
e2e
}=
Rr
Rr + E{ra}E{min
(ra,E{ra}
)}, (3.44)
where the mean data rate for the AL obtains expression
E{ra}=∫ ∞
0Wa log2(1 + γ)
e−γ/γa
γadγ=
Wa
loge(2)e1/γaE1(1/γa). (3.45)
Let us define the SNR threshold γth by
γth = exp(e1/γa E1(1/γa)
)− 1. (3.46)
This definition makes the instantaneous data rate of AL shown in (3.43) be
equal to its mean value given in (3.45). Then, we find that
E
{min
(ra,E{ra}
)}=
∫ γth
0Wa log2(1 + γ)
e−γ/γa
γadγ
+Wa log2(1+γth)
∫ ∞
γth
e−γ/γa
γadγ
=Wa
log2(e)
[− e1/γaE1
(1+x
γa
)−e−x/γa loge(1+x)
]∣∣∣∣∣γth
0
+Wa log2(1 + γth) e−γth/γa . (3.47)
Finally, plugging (3.46) into (3.47), and the resulting formula into (3.44), we
obtain
Rfixe2e =
[RrWa
loge(2) e−1/γaRr +Wa E1(1/γa)
]
×[E1(1/γa)− E1
(exp (e1/γaE1(1/γa)
)γa
)]. (3.48)
It is noted that this RA scheme requires the long-term statistics of both AL
and RL CQI (i.e., γa and Rr).
Resource Optimal (Short-term) RA scheme
If the time resources allocated to RL and AL are updated on instantaneous
basis, then the mean e2e data rate is obtained from (3.41) as
Ropte2e = E
{raRr
ra +Rr
}= Rr
[1− E
{1
1 + ra/Rr
}]. (3.49)
Closed-form expression for the expectation in RHS of (3.49) does not exist.
Hence, we compute for it an upper bound expression that will be further uti-
lized in order to obtain a tight lower bound for the corresponding e2e data
rate.
51
Resource Optimal Relaying
The RHS expectation of (3.49) can be written as
E
{[1+
raRr
]−1}= E
{[1+
Wa
loge(2)Rrloge(1+γa)
]−1}(3.50)
=
∫ ∞
0
[1+
Wa
loge(2)Rrloge(1+γ)
]−1 e−γ/γa
γadγ (3.51)
=
∫ 1
0
[1+
Wa
loge(2)Rrloge
(1−γa loge(u)
)]−1du, (3.52)
where (3.51) follows after replacing the expectation operator with its corre-
sponding integral form, while (3.52) is obtained after substitution u = e−γ/γa .
Let us use the first term of the series expansion for the logarithm presented
in (4.1.27) of [215], i.e.,
loge(z)=2
[(z−1
z+1
)+
1
3
(z−1
z+1
)3
+ . . .
]�{z} > 0. (3.53)
This formula provides a good upper bound for logarithm in 0 ≤ �{z} ≤ 1.
Then, after replacing the logarithmic function in (3.52) with first term of (3.53),
we obtain
E
{[1+
raRr
]−1}≤∫ 1
0
[1+
Wa
loge(2)Rrloge
(1−γa
2(u−1)
(u+1)
)]−1du
=
∫ 1
0
[1+
Wa
loge(2)Rrloge
(u(1−2γa)+(1+2γa)
(u+ 1)
)]−1du
=
∫ 1
0
[C2 +C1 loge
(Mu+ 1
u+ 1
)]−1du. (3.54)
For C1, C2 and M, there holds
C1 =Wa
loge(2)Rr, C2 = 1 + C1 loge(1 + 2γa), (3.55)
M =(1− 2γa
)/(1 + 2γa
). (3.56)
These are constant parameters which depend on the long-term statistics and
communication resources allocated to RL and AL.
Let v = (Mu+1)/(u+1). Then, applying this substitution in (3.54) and, after
that, using the Schwarz’s inequality we find that
E
{[1+
raRr
]−1}≤∫ 1
(M+1)/2
[C2 +C1 loge(v)
]−1 (1−M)
(v−M)2dv
≤ (1−M)
C1
{∫ 1
(M+1)/2
[C2
C1+ loge(v)
]−2
dv
} 12
×{∫ 1
(M+1)/2
[1
(v −M)2
]2dv
} 12
. (3.57)
It is possible to show that the definite integrals in (3.57) attain the following
52
Resource Optimal Relaying
closed form solutions [211]:∫ 1
u
1
[α+ loge(x)]2dx = e−α
[E1
(− α− loge(u))− E1
(− α)]
−[ 1α− u
α+ loge(u)
], (3.58)∫ 1
u
1
[x− α]4dx =
1
3
[ 1
(u− α)3− 1
(1− α)3
]. (3.59)
After applying (3.58)-(3.59) in (3.57) and plugging the resulting expression
in (3.49), the following lower bound expression is obtained:
Ropte2e ≥ Rr
[1−
{ 7
12γaC1
} 12{1− (1 + 2γa)
1 + C1 loge(1 + 2γa)
+e−1/C1
C1
[E1
(− 1
C1
)−E1
(− 1
C1−loge(1+2γa)
)]} 12
]. (3.60)
Finally, using the asymptotic expansion presented in [211]
E1(z) ∼ e−z
z
{1− 1
z+
2
z2− 6
z3+ . . .
}, (3.61)
it is possible to show that when γa grows, then the approximation
Ropte2e ∼ Rr
[1− 1
C1 loge (2γa)
]γa 1 (3.62)
becomes increasingly tight. The latter formula shows that the RL data rate
always becomes a bottleneck of the dual-hop relaying system when SNR in
the AL is large.
Simulation results
The following we present results for different RA schemes assuming certain
mean SNRs in RL and AL. It is noted that the given results are valid for both
UL and DL directions.
Figure 3.10 presents the mean e2e data rate when the macro eNBs shares
10MHz bandwidth among 10 RNs. By employing even shares, each RN has
1MHz (10MHz) to communicate in the RL (AL). Results of Figure 3.10 are
computed from the derived analytic expressions.
53
Resource Optimal Relaying
0 5 10 15 20 25 301
2
3
4
5
6
7
8
Figure 3.10. Mean e2e data rate for a dual-hop relaying system (Wr = 1MHz; Wa = 10MHz).
Red lines: γr = 5dB. Green lines: γr = 15dB. Blue lines: γr = 25dB. Dashed
lines with circles: (Long-term) fixed RA. Solid lines with squares: (Short-term)
resource optimal RA. Point values (′∗′) were simulated.
As it was expected, the resource optimal RA scheme performs better that the
fixed RA scheme and performance difference grows as the mean SNR of the
RL increases. If the RL SNR is kept constant, then the data rate gain of resource
optimal RA reduces when the mean SNR of the AL grows. In this situation
RL becomes a bottleneck for the e2e data rate. It is noted that the point wise
simulation results denoted by (′∗′) are included to validate the accuracy of the
derived closed form expressions in all cases.
Figure 3.11 and Figure 3.12 show the mean e2e data rates for UL and DL,
respectively. When compared to a RN, UE usually applies less power when
sending signals in UL. Therefore, if UL open-loop power control is applied,
then the bandwidth that a UE can utilize in AL (Wa) is usually smaller than
the bandwidth that the RN uses in RL (Wr).
54
Resource Optimal Relaying
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Figure 3.11. Mean e2e data rate for a dual-hop relaying system (Wr = 1MHz) for Uplink di-
rection (γr = 5dB). Red lines: γa = 10dB. Green lines: γa = 20dB. Blue lines:
γa = 30dB. Dashed lines with circles: (Long-term) fixed RA. Solid lines with
squares: (Short-term) resource optimal RA.
1 2 3 4 5 6 7 8 9 101
2
3
4
5
6
7
Figure 3.12. Mean e2e data rate for a dual-hop relaying system (Wr = 1MHz) for Downlink
direction (γr = 20dB). Red lines: γa = 10dB. Green lines: γa = 20dB. Blue
lines: γa = 30dB. Dashed lines with circles: (Long-term) fixed RA. Solid lines
with squares: (Short-term) resource optimal RA.
In DL the macro eNB transmits at constant power towards multiple RNs.
Then, the ratio between Wa and Wr, is expected to be smaller (larger) than 1
for the UL (DL) communication. For the DL direction, the performance gain
55
Resource Optimal Relaying
of resource optimal RA over fixed RA scheme vanishes when Wa/Wr → 1.
However, in the UL direction, the performance is maximized in this situation.
Hence, it can be concluded that the AL represents the bottleneck in the UL
communication, whereas, RL is the limiting factor in the DL communication.
3.5 Conclusions
We analyzed resource allocation schemes (i.e., conventional RA, fixed RA with
and without buffer, and the resource optimal RA) in a two-hop relaying. It
was observed that the resource optimal RA provides notable performance
gains when compared to other RA schemes, where radio resources are not
allocated in short-term. The main contributions of this chapter include the
derivation of closed-form expressions of the CDF and PDF of the e2e data
rate, as well as expressions for the average and outage data rates. Moreover,
an accurate lower bound for the mean data rate was also deduced.
The derivation of closed-form expressions for e2e data rates was repeated for
the DL/UL decoupled scenario to study the obtained e2e data rates when the
fixed (long-term) and resource optimal (short-term) RA schemes are applied
in UL and DL directions. The e2e data rate depends not only on the SNR
that both RL and AL experience, but it also depends on the amount of radio
resources that are allocated to each link. If the UL and DL decoupling is em-
ployed, then, the derived formulas determine the most optimum UE to RN
association (and RN association to given DeNB) for the communications in
each direction.
Overall, it was observed that the resource optimal RA scheme can have signif-
icant performance enhancement due to the imbalance in resourcing between
RL and AL. The use of resource optimal RA schemes improve the RL effi-
ciency, which indirectly leads to a positive impact on the e2e data rate perfor-
mance. The obtained results shed light into the complex relationship between
RL and AL design parameters and system variables, and hence, they poten-
tially support the dimensioning of the infrastructure relaying system.
56
4. Practical Interference Mitigation forthe Relay Backhaul Link
4.1 Introduction
The LTE-A Relay Node employs self-backhauling towards the serving donor
eNode B (DeNB) via the Un radio interface making relaying an attractive so-
lution for the operators [216]. However, Relay Link may also represent a ca-
pacity bottleneck [20]. In LTE-A, the inband relaying utilizes a time-based
resource scheduling between RL and AL. In addition, RN competes on the
radio resources with UEs directly served by the DeNB via a direct link. More-
over, the competition for radio resources further increases when the DeNB is
serving more than one RN. As a result, scarce radio resources are heavily com-
peted and RL may become congested. We also note that RL is experiencing
interference from the neighbouring eNBs, especially when the RN is deployed
at the cell edge. At the same time all RNs can serve their UEs by utilizing the
same radio resource pool in the AL. Then, the spatial radio reuse is much more
effective for the AL resources than it is for the RL resources. The resulting im-
balance between RL and AL can be compensated e.g. by advanced antenna
technologies, coordinated communication and/or by carefully planning the
relay locations [19,24,217–219]. These methods are effective but will increase
the cost and implementation complexity of the relaying.
As mentioned, there is interference from adjacent eNBs on the RL. In some
locations RL may even experience a Line of Sight (LoS) or a dominant signal
direction towards the interfering eNB. In these cases the interference easily
becomes crucial [37, 220, 221]. To that end, this chapter includes a study of a
practical method that can be used on RL to improve the desired signal strength
from DeNB and/or to mitigate the interference from the interfering eNB. We
present an approach where a few bit channel feedback from the RN to both the
DeNB and the interfering eNBs is employed. We note that a simple version of
57
Practical Interference Mitigation for the Relay Backhaul Link
the proposed feedback method has been already standardized in 3G and 4G.
Obtained results has been summarized in our previous publications [12, 13].
The rest of chapter is organized as follows. A brief literature survey is pro-
vided in Section 4.2. Section 4.3 presents the considered system model to ad-
dress the RL limitations. Moreover, Section 4.4 provides the analysis of the
SINR and e2e outage rate obtained on RL. Furthermore, mathematical equa-
tions are validated via simulations in Section 4.5. Work is summarized in
Section 4.6.
4.2 Previous Work and Contributions
Several studies have proposed various improvements for the RL performance.
To that end, Bulakci et al. [18] and Saleh et al. in [19] proposed network plan-
ning techniques namely location selection and cell selection for the improved
RL performance. The former technique enables RNs to be deployed in less
shadowed location while in the latter the RN is served by the best available
eNB. However, the good location options for the RN deployment are limited
especially in dense urban areas due to high building obstruction. In [30],
Park et al. evaluated the relaying performance by employing a dual-layer
BF (DLBF) on RL assuming that the resources allocation is optimized. In ad-
dition, higher order MIMO was employed on the AL of spatially multiplexed
RNs. Results show that the proposed scheme enables significant system gains
of upto 241% as compared to 2×2 MIMO in the RL. However, this work only
considers the transmission of DeNB towards the serving the RNs by employ-
ing MIMO antennas, while not addressing the interference from the neigh-
bouring eNBs.
Yi et al. in [25] present a two-step resource allocation scheme to enhance the
RL performance. The proposed resource assignment model take into account
the link quality of RL, AL and macro UEs. Result shows that the proposed
scheme enhance the average cell performance and the performance of the cell-
edge UE. In [26], a downlink resource scheduling scheme was proposed for
the TD-LTE relaying network. The proposed scheme allocate dynamically the
frequency resources on direct and AL on need basis. The resources for RL are
allocated using the user queue information.
Furthermore, Li et al. in [31] examine the utilization of virtual MIMO in or-
der to enhance the RL performance in dense relaying networks. This work
employs the MIMO technique on cluster of RNs such that all RNs form a vir-
tual MIMO on RL towards the base station. This work mainly focus on the
58
Practical Interference Mitigation for the Relay Backhaul Link
out-band relaying where the DeNB and RN operate on exclusive frequency
bandwidths. This not only leads to spectrum scarcity due to exclusive allo-
cation of spectrum for RL but also increase the system complexity and con-
trol signaling among the RNs in the cluster. Luo et al. in [32] proposed an
iterative Co-Phasing Water-filling (ICPWF) scheme in order to enable the op-
timal power solutions in order to improve the RL performance. Here, two
base stations simultaneously carry out coordinated transmissions to a RN
deployed at the cell edge. Moreover, Haile et al. in [34, 35] employs the
coordinated multi-point transmission in order to relax the self-backhauling
bottlenecks of RL. Results show improved system performance in terms of
e2e rates. However, the method induce additional control signaling as well
as increase system complexity by engaging several eNBs. In addition to
the mentioned works, there is a large number of studies in the field, see
e.g. [131, 184, 187, 188, 192, 199, 222, 223]. Yet, to the best of our knowledge,
our simple, practical and standard compliant method has not been studied by
other authors.
The contributions of this chapter are the following: We derive analytical ex-
pressions for the RL SINR distributions assuming Rice and Rayleigh fading
combinations on the dedicated and interfering links, and a certain interfer-
ence mitigation method based on limited feedback. In addition to SINR dis-
tributions, we also deduce the e2e outage probabilities for the assumed in-
terference and feedback cases. Analytical results are verified via numerical
simulations.
4.3 System Model
We focus on the downlink of a REC network with a dual-hop relaying system
as shown in Figure 4.1.
4.3.1 System Assumptions and Definitions
Fast and Slow Feedback
If the Channel Quality Information (CQI) feedback, sent by the receiver, ar-
rives to the transmitter and is applied within the duration of the channel co-
herence time, then feedback is said to be ’fast’. It is noted that the channel
coherence time is inversely related to the transmitter/receiver motion. In con-
trast to fast feedback, if the CQI feedback requires clearly more time than the
channel coherence time, then it is said to be ’slow’. This long-term feedback
59
Practical Interference Mitigation for the Relay Backhaul Link
Serving NeighborRelaynode
Rice/Rayleigh block fading channel
UE
Rr , Wr , d i
Ra , Wa , a
Fast/slow feedback Fast/slow feedback
Strongest interferer
Irest
DeNB eNB
Other eNBs
Figure 4.1. System model.
may describe e.g. channel mean, variance, or correlations between component
channels. To obtain a reliable long-term information about the channel, some
filtering is usually needed. Furthermore, this filtering should be used over
several channel coherence times in order to mitigate the impact of short-term
channel variations.
Channel Models
Unless otherwise stated, we assume that RN and eNB locations are fixed. To
that end eNB antennas are elevated above the street level where the surround-
ing environment around the eNB remains unchanged. RN is located closer to
the street level but it may experience a LoS towards the DeNB due to e.g., loca-
tion planning. Yet, the moving objects around the RN within the first Fresnel
zone may create a fast fading on RL. Then, the Rice fading provides a suitable
option for modeling the radio channel.
If there is no LoS on RL, then RN receives large part of signal power from
several directions rather than from a single direction. Such a channel envi-
ronment can be described using the Rayleigh channel model. Other options
might be Nakagami-m model [224], [225] or the so-called general fading mod-
els like Stacy model [226], [227]. In case of these general channel models a
problem arise, when values for numerous parameters are chosen to define the
fading. Thus, we have chosen an approach where results are deduced for the
Rice and Rayleigh models. We note that these models provide a reasonable
basis for the link and system performance analysis.
In case of Rayleigh model, the channel components hm of the vector h are i.i.d.
complex zero-mean Gaussian random variables, meaning that the |hm| fol-
lows the Rayleigh distribution. The channel feedback for the case of Rayleigh
model will be fast. In Rician model, the channel components of the vector h
60
Practical Interference Mitigation for the Relay Backhaul Link
are i.i.d. complex Gaussian with the same mean |E{hm}| = ν ∈ R+, which
means that the |hm| follows the Rice distribution. In the following, we use
normalized channels so that E{|hm|2} = 2σ2+ν2 = 1, where 2σ2 is the power
of the fading component and ν2 is the power of the static signal component.
Here, the RN will employ the slow feedback approach on RL to provide CQI
to eNB.
System Assumptions
1) We assume the block fading model on both RL and AL.
2) We employ the AWGN channel capacity formula in order to compute the
instantaneous rates on the RL (Rr) and the AL (Ra):
Rr = Wr · log2(1 + Υr), Ra = Wa · log2(1 + Υa), (4.1)
where Wr and Wa are the transmission bandwidths and Υr and Υa are the
SINR’s on RL and AL, respectively. We need the distributions of Υr and Υa in
order to analyze the e2e performance.
3) It is noted that the experienced SINR’s Υr and Υa on RL and AL, respec-
tively, are independent and hence, the experienced data rates Rr and Ra on
corresponding links are also independent.
4.3.2 End-to-end outage rate
We recall the conventional e2e rate (Re2e) formula to initiate the mathematical
treatment of the relaying system;
Re2e =1
2min{Rr, Ra}. (4.2)
To that end, we compute the outage rate to examine the e2e rate for a relaying
system;
Pout = P (Re2e < Rmin) = FRe2e(Rmin). (4.3)
Here Rmin is the minimum rate requirement that can be used to control the
e2e performance. That is, link is considered to be in outage if Re2e < Rmin.
Evidently, the outage probability Pout for such an event should be small. To
compute the outage rate we need the CDF of the e2e rate, denoted in (4.3) by
FRe2e . While assuming that instantaneous rates in RL and AL are independent
random variables, we have
FRe2e(R) = 1− P (min{Rr, Ra} > 2R)
= 1− P (Rr > 2R)P (Ra > 2R)
= 1− (1− FRr(2R))(1− FRa(2R)),
(4.4)
61
Practical Interference Mitigation for the Relay Backhaul Link
where FRr and FRa denote the CDF’s of RL and AL rates, respectively. Thus,
we need distributions of the Rr and Ra in order to deduce the e2e rate distri-
bution. To that end, the expression in (4.1) can be used to obtain the RL rate
distribution as;
FRr(R) = P (Rr < R) = P (Wr · log2(1 + Υr) < R)
= P (Υr < 2R/Wr − 1) = FΥr(2R/Wr − 1).
(4.5)
In the same way, we can also deduce the AL distribution as FRa(R) =
FΥa(2R/Wa − 1). Hence, in order to compute the outage rate, we need the
SINR CDF’s of RL and AL.
It is noted that the fixed RN deployment is usually employed at the cell edge to
improve the cell coverage [6,67]. Then, the macro DeNB will obviously create
co-channel interference to a large area and consume scarce time/frequency
radio resources for RL transmission. While there is need to minimize the uti-
lization of radio resources on RL, it may easily make RL as a bottleneck in the
e2e performance especially if the network is interference limited. In such sce-
nario each RN will provide channel feedback to its interfering eNB in order to
mitigate the interference from the interfering eNB through precoding. To that
end, it is assumed that the serving and the interfering eNBs are synchronized
and utilize the same time frequency resource pool for RN transmissions.
The following sections will present the considered precoding methods. These
methods will be used to mitigate the interference from dominant interfering
eNB and to improve the RL quality from the dedicated DeNB.
4.3.3 RL SINR Model
Let us consider the SINR in the RN as;
Υn,k =γn,n,k|wn · hn,n,k|2
1 +∑
m�=n γm,n,k|wm · hm,n,k|2 . (4.6)
Here indices m and n refer to eNBs in mth and nth cells, respectively, while k
refers to the RN of the nth cell and wm is the normalized precoding weight
vector used in the cell m. It is noted that hm,n,k represents the normalized
vector channel between mth eNB and kth RN of the nth cell. We also note that
the dimensions of the weight and channel vectors are defined by the number
of parallel transmission chains in the eNB. We haveE{|wn ·hn,n,k|2} = 1 due to
normalizations, if precoding vector is randomly selected. Furthermore, γm,n,k
denotes the average received SNR in the RN. The equation (4.6) can be written
as follows:
Υn,k =γn,n,k|wn · hn,n,k|2
1 + γn,n,k|wn · hn,n,k|2 + Irestn,k
, (4.7)
62
Practical Interference Mitigation for the Relay Backhaul Link
where γn,n,k = maxm�=n{γm,n,k} denotes the mean power of the
strongest/dominant interfering signal and Irestn,k includes the rest of the
interfering signals,
Irestn,k =
∑m�=n,n
γm,n,k|wm · hm,n,k|2. (4.8)
As can be seen from formulae (4.7)–(4.8), the eNB multiantenna resources can
employed as follows:
1) We can enhance signal strength transmitted from the DeNB towards its own
RN by selecting the precoding weight such that |wn ·hn,n,k|2 is maximized, i.e.
|wn · hn,n,k|2 = max{|w · hn,n,k|2 : w ∈ W}, (4.9)
whereas W denotes the precoding codebook. This is a baseline scenario where
RN provides CQI feedback to the DeNB only.
2) If there is a dominant interfering eNB such that γn,n,k >> γm,n,k, m �= n,
then RN SINR performance will be enhanced if the dominant interfering eNB
n employs its antenna resources to mitigate the interference towards RN as
γn,n,k|wn ·hn,n,k|2 in (4.7) by e.g. selecting the precoding weight from the code-
book such that
|wn · hn,n,k|2 = min{|w · hn,n,k|2 : w ∈ W}. (4.10)
It is obvious that such action will diminish the BF gain in RL between the
dominant interfering eNB n and its own RN. Though, this may not be nec-
essarily a problem if the RL between the dominant interfering eNB n and its
own RN is good or precoding weight that maximize the own RL and minimize
the interference is the same. Furthermore, if there is a strong LoS component
on RL, then there is some directivity by nature and due to fixed location of
RN, the LoS component is quite static. Hence, in this scenario, the RL radio
resources of different eNBs can be jointly scheduled in order to minimize the
interference towards adjacent cells RN.
In the following we study the performance when eNB applies the so-called al-
truistic precoding to mitigate the interference towards the RL of the adjacent
macro eNBs. The altruistic transmit BF has been previously investigated
in [228, 229], though, those studies mainly focus on femtocells in NLoS sce-
nario with Rayleigh fading channel. We fit some results of [228, 229] in our
context for comparison purposes, but our study mainly focus on Rice fading
experienced by a fixed RN on the RL.
63
Practical Interference Mitigation for the Relay Backhaul Link
Before considering the precoding in more details we write (4.7) in the form
Υn,k =γn,n,k|wn · hn,n,k|2
1 + γn,n,k|wn · hn,n,k|2 + Irestn,k
=
γn,n,k1+Irest
n,k|wn · hn,n,k|2
1 +γn,n,k1+Irest
n,k|wn · hn,n,k|2
=Ψ
(1)n,kγn,n,k|wn · hn,n,k|2
1 + Ψ(1)n,kγn,n,k|wn · hn,n,k|2
,
(4.11)
where factor Ψ(1)n,k = 1/(1 + Irest
n,k ) reflects the structure of the interference re-
ceived in kth RN of nth macrocell. Here the superscript ’1’ indicates that the
strongest interfering signal has not been taken into account. We note that
0 < Ψ(1)n,k < 1 and the closer Ψ
(1)n,k is to one, the more dominant the strongest
interferer is. If interference mitigation is applied only for the strongest inter-
fering signal, then we may approximate
Irestn,k ≈ E{Irest
n,k } =∑
m�=n,n
γm,n,kE{|wm · hm,n,k|2}
=∑
m�=n,n
γm,n,k = Irestn,k ,
Ψ(1)n,k ≈ 1/(1 + Irest
n,k ),
(4.12)
whereas E{·} denotes the expectation over fast fading. This approximation is
most valid when the strongest interfering eNB is clearly dominant.
We are left with two random variables after approximation (4.12), including
the gain factor for the desired signal from DeNB: |wn · hn,n,k|2; and the gain
factor for the interfering signal from dominant interfering eNB: |wn · hn,n,k|2.We analyze the maximum achievable performance gain through use of some
simple and practical precoding methods by applying (4.9) and (4.10) when se-
lecting precoding weights for the desired DeNB and the dominant interfering
eNB. Now, we re-write SINR (4.6) in the form
Υr ≈ Ψ(1)γd|w · hd|21 + Ψ(1)γi|w · hi|2
, (4.13)
where equality has been replaced by approximation due to (4.12), subscript
′d′ refers to desired signal and subscript ′i′ refers to the interfering signal. The
SINR model (4.13) provides our starting point for the analytical study in Sec-
tion 4.4.
Fast and Slow Quantized Co-Phasing (QCP)
Before further mathematical analysis, we discuss briefly about the applied
precoding schemes. In the previous literature, the use of precoding for the
64
Practical Interference Mitigation for the Relay Backhaul Link
purpose of interference mitigation has been examined, e.g., in [229–231].
In the current study, we assume a simple method with two antennas, see
also [232]. While approach is simplistic, it is very practical and tractable math-
ematical analysis can be used to provide insight to the link level performance.
The applied precoding methods are known as Fast QCP (FQCP) and Slow
QCP (SQCP).
In FQCP scheme, we assume two-antenna transmitter and a single antenna
receiver. We also assume N bits of phase information available at the trans-
mitter. Then w = (w1, w2) = (1, ejϕ)/√2 for FQCP is defined by
|w · h| = maxn
{|(h1 + ejϕnh2)|/√2}, (4.14)
where ϕn = 2π(n− 1)/2N and n = 1, 2, . . . , 2N .
The work done in [229, 232, 233] already employed FQCP which can be ex-
tended to any number of transmit antennas. Although FQCP is simple and
widely known, its performance on non-Rayleigh channels has not been well
studied. We note that in interference mitigation we simply select w =
(w1, w2) = (1, ejϕ)/√2 such that minimum is obtained in (4.14) instead of
maximum.
We assume a single antenna receiver and two antenna transmitter with N bits
of phase information available for SQCP case. The weight w = (w1, w2) =
(1, ejϕ)/√2 for SQCP is selected as in FQCP but it is based on long-term mean
of the channel:
|w · h| = maxn
{|(E{h1}+ ejϕnE{h2})|/√2}, (4.15)
where ϕn = 2π(n− 1)/2N and n = 1, 2, . . . , 2N . In case of the Ricean channel
model E{h1} = ν1ejψ1 , E{h2} = ν2e
jψ2 and (4.15) becomes
|w · h| = maxn
{|ν1 + ej(ϕn−ψ1+ψ2)ν2|/√2}. (4.16)
If transmitter antennas are grouped in the same mast, then ν1 = ν2 =: ν. Sim-
ilarly as in FQCP, we select w = (w1, w2) = (1, ejϕ)/√2 such that minimum is
obtained in (4.15) instead of maximum for interference mitigation. The main
aim of this interference mitigation is to mitigate the Rice component of the
interfering signal. Moreover, the slow CQI feedback update does not impose
capacity limitations. Finally, we note that the phasing accuracy is not neces-
sarily defined only by N , but also by the accuracy of the phase measurements
in the receiver.
65
Practical Interference Mitigation for the Relay Backhaul Link
4.4 Analysis of SINR and e2e Outage Rate
We can write the SINR Υr of RL in the form of X/(1 + Y ) according to (4.13),
whereas random variables X = Ψ(1)γd|w · hd|2 and Y = Ψ(1)γi|w · hi|2 are
independent. Here γd and γi denote the mean powers of the dedicated RL
channel from desired DeNB and the interfering RL channel from interfering
eNB, respectively, before feedback is used to adjust the channel components.
By [212], we have
FΥr(γ) =
∫ ∞
1FX(γt) fY (t− 1)dt, (4.17)
where fY (y) is the PDF of Y and FX(x) represents the CDF of X . The chal-
lenge here is to find the distributions forX and Y , and then compute the SINR
distribution from (4.17). If there is no interference, then we can write
FΥr(γ) = Fγd|w·hd|2(γ). (4.18)
In the following F cΥr(γ) = 1− FΥr(γ) is the Complementary Cumulative Dis-
tribution Function (CCDF). We consider four scenarios with different fading
model combinations in the dedicated RL and the interfering RL in the coming
analysis. In each fading scenario the channel model for the dedicated RL will
be mentioned first, while the model for the dominant interfering eNB is given
second.
4.4.1 Scenario 1: Rice fading - Rice fading
Relay system framework
If RN experiences a LoS on dedicated RL and in the interfering link, then the
Rice fading model is a suitable option for both to demonstrate the channel
statistics. Although the RN location is fixed, some fading can occur due to
the movement of scatterers (e.g., vehicles, etc) nearby the RN. This scenario
assumes slow feedback scheme (i.e., SQCP) on either/both dedicated RL and
the interfering link.
SINR distribution in the presence of SQCP
In the Rice fading modeling, the component channel related to each transmit
antenna m is of the form hm = αm + νejψm , where αm is complex zero-mean
Gaussian which means that |αm| follows the Rayleigh distribution. The sec-
ond term in the sum (i.e., νejψm) denotes the static channel component, i.e.
ν > 0 is constant and ν2 is the power of the LoS part of the signal. Angles
ψm are assumed to be mutually independent and uniformly distributed on
(−π, π), while it is noted that ψm in each antenna branch can take any value
66
Practical Interference Mitigation for the Relay Backhaul Link
on (−π, π) if antenna branches are not calibrated - as usually is the case in
practice. Yet, the value of ψm is changing only slowly - if at all. We recall that
the K = ν2/2σ2, 2σ2 = E{|αm|2} denotes the so-called Rice factor (K), which
is the ratio between the power of the fixed-path (i.e., ν2) and the power of the
fading part (i.e., 2σ2) of the signal. In the following, the component channels
of h (whether it is desired or interfering vector channel) are normalized such
that
E{|h1|2} = E{|h2|2} = 2σ2 + ν2 = 1 (4.19)
and the Rice factors for the component channels are equal: K = ν2/2σ2. Then,
we can write the powers of fading and static channel parts as
2σ2 =1
1 +Kν2 =
K
1 +K. (4.20)
Let us consider the distribution of |w · hd| when SQCP is applied on the ded-
icated RL. More precisely, we will show the following: If h1 and h2 are i.i.d.
and defined as above, and w is selected using the SQCP method in (4.15), then
|w · hd| follows the Rice distribution with parameters
ν := |E{w · hd}| ≈√
1 + sinc( 1
2Nd
) · νd,K :=
|E{w · hd}|2E{|w · hd − E{w · hd}|2} ≈
(1 + sinc
( 1
2Nd
))·Kd.
(4.21)
Hence, the sum channel will be Rician after using SQCP as defined in (4.15)
over two Rician channels. Furthermore, the parameters ν and K of the sum
channel can be expressed in terms of the component channel parameters νd,
Kd and the number Nd of phase bits used in SQCP.
To prove this result, we assume that w is selected according to (4.15). Then
we can write
w·hd =1√2(α1 + νde
jψ1) +ejϕ√2(α2 + νde
jψ2)
=( 1√
2α1 +
1√2ejϕα2
)+
νd√2
(ejψ1 + ej(ϕ+ψ2)
),
(4.22)
where the first term in the right side is a complex zero-mean Gaussian since
both α1 and α2 are complex zero-mean Gaussian and the phasing is using
only long-term channel information. Moreover, the second sum on the right
side is static - from fast fading perspective - and we find that |w · hd| follows
the Rice distribution. We also note that E{|(α1 + ejϕα2)/√2|2} = 2σ2
d, which
means that the power of the fading part of the signal remains the same after
using the SQCP.
Furthermore, we need to prove (4.21). We take an expectation with respect to
the fast fading over (4.22). Since1√2(α1 + ejϕα2) is a zero-mean variable, we
67
Practical Interference Mitigation for the Relay Backhaul Link
obtain
|E{w · hd}|2 = ν2d2
∣∣∣ejψ1 + ej(ϕ+ψ2)∣∣∣2
=ν2d2|1 + ejΦ|2
= ν2d · (1 + cosΦ),
(4.23)
whereas we haveΦ = ϕ+ψ2−ψ1. Althoughψ1 andψ2 change slowly, they are
random due to non-ideal practical implementation. For example, phase drift
is a well-known phenomenon in practical transmission chains. By applying
the SQCP, we select ϕ based on Nd feedback bits such that Φ = ϕ + ψ2 − ψ1
maximizes the value of cosΦ. Due to quantization of ϕ the phase Φ becomes
uniformly distributed in (−π/2Nd , π/2Nd). Now we can write
|E{w · hd}|2 = ν2d · (1 + EΦ{cosΦ}) + Δ, (4.24)
where Δ = ν2d ·(cosΦ−EΦ{cosΦ}) and EΦ refers to the long term expectation
over Φ. After simple calculations, we find that EΦ{cosΦ} = sinc(1/2Nd). In
case of SQCP, accurate co-phasing is well possible because phasing between
static signal components varies on slowly basis. Then Δ becomes small and
first approximation in (4.21) is valid. To show the second part of (4.21), we
compute
E{|w · hd−E{w · hd}|2}= E{|(α1 + ejϕα2)/
√2|2} = 2σ2
d
(4.25)
and we have
K =ν2
2σ2d
≈(1 + sinc
(1
2Nd
))ν2d
2σ2d
=(1 + sinc
( 1
2Nd
))·Kd.
(4.26)
Hence, it is proved that after applying SQCP, the received signal amplitude,
i.e., |w · hd| follows the Rice distribution with parameters (4.21). The CDF of
the Rice variable with parameters ν and σd is by [207] of the form
Fγd|w·hd|(γ) = 1−Q1
(ν
σd,
√γ
γdσ2d
), (4.27)
where Q1 is the well-known Marcum Q-function defined as
Q1(A,B) =
∫ ∞
Bte−
t2+A2
2 I0(At)dt (4.28)
and I0 refers to the modified Bessel function of order zero [211]. By (4.20),
(4.21) and (4.27), we obtain the CCDF
F cΥr
(γ) = Q1
(√2(1 + sinc
( 1
2Nd
))Kd,
√2γ
γd(1 +Kd)
). (4.29)
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Practical Interference Mitigation for the Relay Backhaul Link
SINR distribution when SQCP is applied both in the desired RL and in the
interfering link
Here, we discuss the scenario where the interfering eNB is in LoS with the
RN. Though, we may use the SQCP scheme for mitigating the interference
from the interfering eNB. Then, similarly with (4.21) in the previous section
we can deduce the Rice factor of the interfering channel
K =ν2
2σ2i
=(1− sinc
( 1
2Ni
))·Ki, (4.30)
where the 2σ2i andKi denote the power of the fading part and the Rice factor of
the component channels. We obtain K/Ki = 0.026 with just three bit phasing,
which means that with three bit phasing accuracy we may attenuate 16 dB the
Rice factor of the signal from interfering eNB.
Since the Rice factor can be heavily attenuated just with few bit phasing accu-
racy, we can ignore it in the following and assume that after applying SQCP,
the received interference channel from interfering eNB is zero-mean Gaus-
sian. That is, in (4.13) we now haveY = Ψ(1)γi|w·hi|2, whereE{|w·hi|2} ≈ 2σ2i
and we approximate
fY (y) ≈ 1
Ψ(1)γi2σ2i
e−y/Ψ(1)γi2σ2i . (4.31)
Let us compute the CCDF of SINR Υr. Using (4.17), (4.37), (4.27) and (4.31),
we find that
F cΥr(γ) =
e1
Ψ(1)γi2σ2i
Ψ(1)γi2σ2i
∫ ∞
1Q1
(ν
σd,
√γt
Ψ(1)γdσ2d
)e− t
Ψ(1)γi2σ2i dt. (4.32)
Analytic computation of the integral in (4.32) is challenging but fortunately
in [234] a new formula has been deduced for the Marcum Q-function that
enables series presentation for the integral. According to [234] there holds
Qm(A,B) =
(A2
2
)1−m
e−A2+B2
2 Φ3
(1, 2−m;
A2
2,A2B2
4
), (4.33)
where Φ3 is the confluent hypergeometric function of two variables [211]
(9.261.3):
Φ3
(C,D;w, z
)=
∞∑m,n=0
(C)m(D)m+n
zmwn
m!n!. (4.34)
Here (C)m = Γ(m+C)/Γ(C) is the Pochhammer symbol. To compute F cΥr(γ)
we combine (4.33), (4.34), and apply standard integration rules. The resulting
expression is of the form
F cΥ(γ) =
2c
b2e−
a2+b2
2
∞∑m,n=0
1
(m+ n)!
(a22
)m+n en
(b2
2 + c)
(1 + 2c
b2
)n+1 , (4.35)
69
Practical Interference Mitigation for the Relay Backhaul Link
where en(z) =∑n
k=0 zk/k! and
a2 =ν2
σ2d
= 2(1 + sinc
( 1
2Nd
))Kd,
b2 =γ
σ2dΨ
(1)γd=
2γ(1 +Kd)
Ψ(1)γd,
c =1
Ψ(1)γi2σ2i
=1 +Ki
Ψ(1)γi.
(4.36)
4.4.2 Scenario 2: Rayleigh fading - Rayleigh fading
Relay system framework
Generally, the Rayleigh fading model is suitable channel statistics for the RL of
a mobile RN. Though, if RN admit fixed location then we can use the Rayleigh
statistics, if RN is experiencing a NLoS link towards the eNB and there are
moving scatterers in close vicinity of the RN. To that end, we may apply FQCP
in either/both the dedicated RL and the interfering link. Here, we can apply
previous results of [229, 232], where a direct connection between the BS and
the UE was assumed. This scenario will be used as a benchmark in perfor-
mance studies of Section 4.5.
SINR distribution in the presence of FQCP
Now we assume that the components of the vector channels hd and hi are
complex zero-mean Gaussian. Then, an effective chi-square approximations
for X and Y can be used according to [229] as follows;
FX(x) = 1−(1 +
2x
EΨ(1)γd
)e−2x/EΨ(1)γd ,
fY (y) =1
EΨ(1)γie−y/EΨ(1)γi ,
(4.37)
where E := E{|w · hd|2} and E := E{|w · hi|2}. The resulting expressions are
E = 1 +π
4· sinc
( 1
2Nd
), E = 1− π
4· sinc
( 1
2Ni
), (4.38)
where Nd and Ni denotes the numbers of phase bits used for the construc-
tive and the destructive phasing in the dedicated and the interfering links,
respectively.
The distribution of SINR in (4.13) can be computed using (4.17) and approxi-
mations (4.37). Applying the results of [229] we obtain CCDF of the form
F cΥr(γ) =
(2γ · ρ(ρ+2γ
)2 +
(1+ 2γ
EΨ(1)γd
)ρ
ρ+2γ
)e
−2γ
EΨ(1)γd , (4.39)
where
ρ =γdγi
· 1 +π4 · sinc
(1/2Nd
)1− π
4 · sinc(1/2Ni
) . (4.40)
70
Practical Interference Mitigation for the Relay Backhaul Link
4.4.3 Scenario 3: Rice fading - Rayleigh fading
Relay system framework
In REC network planning, the RN location can be selected with aim to expe-
rience a good radio signal towards the DeNB on RL as well as to avoid inter-
ference from the adjacent eNB [19, 24]. Due to the said network planning, a
scenario is typical where RN admit the LoS Rice channel towards the serving
DeNB and NLoS Rayleigh channel towards the (dominant) interfering eNB.
Then, in addition to SQCP in the dedicated RL, the use of FQCP can be con-
sidered in the interfering link.
SINR distribution when SQCP is applied in the desired RL and FQCP is applied in
the interfering link
In this scenario we assume that the interference channel between the inter-
fering eNB and RN is complex zero-mean Gaussian. Now, if a fast feedback
channel between RN and the interfering eNB exists, then FQCP can be ap-
plied for interference mitigation. Thus, we can apply chi-square approxima-
tion as in (4.37) with E = 1− π4 · sinc(2−Ni). On the other hand, if there is no
fast feedback channel, then the interfering signal component channels sum up
randomly and the amplitude of the interference channel follows the standard
Rayleigh distribution, i.e. E = 1 in the latter formula of (4.37).
Here, the CCDF of SINR is obtained from (4.35). Since SQCP is used in the
desired RL the parameters a and b are as in (4.36). Furthermore, since FQCP
is applied in the interfering channel, the parameter c becomes
c =1
Ψ(1)γiE=
1
Ψ(1)γi(1− π
4 · sinc(
12Ni
)) . (4.41)
4.4.4 Scenario 4: Rayleigh fading - Rice fading
Relay system framework
Assume a mobile RN (located e.g. in a bus), where RL between DeNB and
RN can be for a while blocked by e.g., building, while simultaneously a LoS
towards the interfering eNB takes place. It is obvious that such a situation
usually leads to a handover, but it is noted that handover process requires
a time window during which the RN is required to operate in unfavorable
channel conditions. It is not expected that this scenario is common in the
network.
71
Practical Interference Mitigation for the Relay Backhaul Link
SINR distribution when FQCP is applied in the desired RL and SQCP is applied in
the interfering link
Here, the SNR distribution for the desired RL is given by the first formula
in (4.37). After using SQCP, we assume as before that the interference (sum)
channel is complex zero-mean Gaussian and the PDF (4.31) can be applied.
Similarly as in Section 4.4.2, we obtain
F cΥr(γ) =
(2γ · ρ(ρ+2γ
)2 +
(1+ 2γ
EΨ(1)γd
)ρ
ρ+2γ
)e
−2γ
EΨ(1)γd , (4.42)
where E = 1 + π/4 · sinc(1/2Nd) by (4.38) and
ρ =γdγi
(1 +Ki
)(1 +
π
4· sinc
( 1
2Nd
)). (4.43)
4.4.5 End-to-end outage rate
As it was discussed in Section 4.3.2, we need the RL and AL CDF functions
FΥr(γ) and FΥa(γ) for the e2e outage analysis. More precisely, by (4.4) and
(4.5) we have
FRe2e(R) = 1− (1− FRr(2R))(1− FRa(2R))
= 1− F cΥr(22R/Wr − 1) · F c
Υa(22R/Wa − 1).
(4.44)
In previous sections we obtained the FΥr(γ) for different channel, interfer-
ence and feedback scenarios. Since our focus is on the impact of the feedback
and interference on RL, we consider the AL as interference free link. Then
in Rayleigh and Rice fading cases the CDF’s of the AL are respectively of the
form
FΥa(γ) = 1− eγ/γa ,
FΥa(γ) = 1−Q1
(νaσa
,
√γ
γaσ2a
),
(4.45)
where γa is the mean AL SNR and νa, σa refer to the AL Rice parameters.
4.5 Performance Evaluation
This section presents the performance evaluation of the aforementioned sce-
narios. The results include the CDF plots of SINR and e2e outage rate ob-
tained in Section 4.4. All curves are computed using previously deduced
mathematical functions. Table 4.1 summarizes the parameters used in sim-
ulations.
72
Practical Interference Mitigation for the Relay Backhaul Link
Table 4.1. Parameters for the performance experiments.
Parameter γd γi Ψ(1) Nd, Ni Kd, Ki
Value 12 dB 6 dB 0 dB/-7 dB 4 bits 9 dB
It is noted thatΨ(1) becomes 0 dB if Irest = 0, which means that RN experience
interference on RL only from the dominant interfering eNB. In contrast, if the
average powers of the dominant interfering eNB and the rest of the interfer-
ence are equal, then Irest=6 dB and we have Ψ(1) = -7 dB. Moreover, it is also
noted that figures include point values (presented in figures by ’o’, ’�’ and
’�’) which has been simulated. Simulated results are well consistent with the
curves obtained using the equations.
4.5.1 RL SINR Distributions
Scenario 1: Rice fading - Rice fading
Figure 4.2 shows the CDF of SINR on RL when we applied the SQCP and
parameters of Table 4.1. As a performance upper bound we have used the
case where SQCP is applied on desired RL and there is no interference at
all (denoted by dotted curve with ’�’). Curve was generated by using the
equation (4.29).
0 2 4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 4.2. CDF of the SINR of RL when Rice fading occurs in both the desired RL and the
interfering link, and parameters of Table 4.1 are used. Solid curves: Ψ(1) = 0 dB;
dashed curves: Ψ(1) = -7 dB. SQCP in the desired RL, no SQCP in the interfering
link (’�’). SQCP in both the desired RL and the interfering link (’o’). Performance
upper bound (’�’): SQCP in the desired RL, no interference at all.
73
Practical Interference Mitigation for the Relay Backhaul Link
It is observed from figure 4.2 that SQCP provides notable performance gain
on RL. Here, the curves with ’o’ are plotted using the formulae (4.35) and
(4.36). The interference mitigation gain from SQCP depends heavily on the
strength of the dominant interferer eNB with respect to the sum of interfer-
ence from other eNBs. That is, if the interference experienced by RN on RL
is only from the dominant interferer, then gains will be significant especially
in the low SINR region. For instance, when comparing solid curves with ’o’
and ’�’, the gain is 6 dB at CDF level 0.1 for the dominant interferer only case.
This is clearly higher gain than in the case, where the dominant interferer
represents only half of the total interference, i.e., gain drops to 2-3 dB. It is
noted that in the case with Rice interference but without SQCP, curves were
obtained through simulations. The analytical formulae were not deduced for
this scenario because it was used only for the benchmarking purposes.
Scenario 2: Rayleigh fading - Rayleigh fading
Figure 4.3 presents the CDF of RL SINR when utilizing the FQCP and param-
eters of Table 4.1. It is noted that as an upper bound the case is used where
FQCP is applied in the desired RL and there is no interference at all as denoted
by a dotted curve with ’�’, obtained by using equations (4.37) and (4.38).
-5 0 5 10 15 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 4.3. CDF of the SINR of RL when Rayleigh fading occurs in both the desired RL and the
interfering link, and parameters of Table 4.1 are used. Solid curves: Ψ(1) = 0 dB;
dashed curves: Ψ(1) = -7 dB. FQCP in the desired RL, no FQCP in the interfering
link (’�’). FQCP in both the desired RL and the interfering link (’o’). Performance
upper bound (dotted curve with ’�’): FQCP in the desired RL, no interference at
all.
Curves with ’o’ show the performance when FQCP is used in both the desired
74
Practical Interference Mitigation for the Relay Backhaul Link
RL and the interfering link. It is noted that equations (4.39) and (4.40) were
used to generate the curves. Similar to the Scenario 1, it is observed that if
interference experienced on RL is from dominant interferer only, then FQCP
interference mitigation gain is large especially in low CDF range. For exam-
ple, SINR gain is 4 dB or even more for CDF level below 0.3. In addition, if
RN experience same amount of interference from both the dominant inter-
ferer and other interfering eNBs (i.e., Ψ(1) = -7 dB), then the gain from FQCP
interference mitigation is much smaller.
Scenario 3: Rice fading - Rayleigh fading
In this scenario, RN experiences Rice fading in the desired RL and Rayleigh
fading in the interfering links. As before, we apply the upper bound (denoted
by ’�’), where SQCP is employed in the desired RL and there is no interference
at all. It is observed from Figure 4.4, that FQCP provides notable gains while
mitigating the interference on RL.
While the efficiency of SQCP mainly depends on the strength of the static part
in the interference as shown previously in the Figure 4.2, FQCP tracks the fast
fading but needs more feedback. The curves in Figure 4.4 were generated by
using formulae (4.35) and (4.36), where in the parameter c has been computed
from equation (4.41).
0 2 4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 4.4. CDF of the SINR of RL when Rice fading occurs in the desired link and Rayleigh
fading in the interfering link, and parameters of Table 4.1 are used. Solid curves:
Ψ(1) = 0 dB; dashed curves: Ψ(1) = -7 dB. SQCP in the desired RL, no feedback in
the interfering link (’�’). SQCP in the desired RL and the FQCP in the interfering
link (’o’). Performance upper bound (’�’): SQCP in the desired RL, no interference
at all.
75
Practical Interference Mitigation for the Relay Backhaul Link
Scenario 4: Rayleigh fading - Rice fading
It is observed from Figure 4.5 that SQCP effectively mitigates the interference
if there is only dominant interferer on the RL: The gap towards the perfor-
mance upper bound (dotted curve with �) becomes small. We used equa-
tions (4.42)-(4.43) to obtain curves.
-5 0 5 10 15 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 4.5. CDF of the SINR of RL when Rayleigh fading occurs in the desired RL and Rice
fading in the interfering link, and parameters of Table 4.1 are used. Solid curves:
Ψ(1) = 0 dB; dashed curves: Ψ(1) = -7 dB. FQCP in the desired RL, no feedback in
the interfering link (’�’). FQCP in the desired RL and the SQCP in the interfering
link (’o’). Performance upper bound (’�’): FQCP in the desired RL, no interference
at all.
4.5.2 End-to-end Outage Rate
This section presents how the RL gains impact on the e2e data rate perfor-
mance. We have obtained these curves by using formula (4.44) and SINR dis-
tributions from Section 4.4. We assume Rayleigh fading in the AL with mean
power γa = 8 dB in addition to parameters of Table 4.1. Furthermore, we al-
locate to the AL and RL bandwidth with 180kHz granularity as in LTE where
frequency-time PRBs of (180 kHz)x(1 msec) are used.
76
Practical Interference Mitigation for the Relay Backhaul Link
0 0.2 0.4 0.6 0.8 1 1.20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 4.6. The e2e outage probability when Rayleigh fading occurs in the AL (γa = 8 dB),
Rice fading in the RL (γr = 12 dB) with Rice interference (γi = 6 dB, Ψ(1) =0 dB).
Numbers of PRB’s are Nr,PRB = 2 and Na,PRB =2 (’o’), 8 (’�’) . Solid blue curves
refer to the case where SQCP is applied in both the desired RL and the interfering
link, dashed red curves refer to case where SQCP is applied only in desired RL.
Figure 4.6 presents the e2e outage rate when Rice fading occurs on RL and two
PRBs are allocated to the RLNr,PRB = 2, while the number of AL PRBsNa,PRB
varies from 2 PRBs to 8 PRBs. It is noted that the number of PRB’s allocated
to RL is usually limited because DeNB may serve many RNs as well as direct
connections simultaneously. In contrast, RN can utilize more radio resources
on AL because RN serves smaller area than DeNB but operate on the whole
frequency band. Hence, in order to boost the RL performance, we employ the
SQCP in the desired RL and the interfering link.
It is noted in the case Na,PRB = 2, AL is the bottleneck and applying the in-
terference mitigation in RL, will improve e2e rate only if outage probability is
high. Yet, if the AL resources increased (i.e., Na,PRB = 8), then the bottleneck
is RL and mitigating the interference on RL will clearly improve the e2e data
rate. At 10% outage probability level, it is observed that rate increases from
0.3 Mbps to 0.5 Mbps in this example.
Figure 4.7 presents the e2e outage rate for scenario where Rayleigh fading
occurs on both the RL and AL (with Rayleigh fading interference in the RL).
As in case of Rice fading AL is bottleneck when Na,PRB = 2 and the gains
from interference mitigation are small. If Na,PRB = 8 for AL, then mitigating
the RL interference will clearly improve the e2e performance. At 10% outage
probability level rate increases from 0.2 Mbps to just over 0.3 Mbps.
77
Practical Interference Mitigation for the Relay Backhaul Link
0 0.2 0.4 0.6 0.8 1 1.20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 4.7. The e2e outage probability when Rayleigh fading occurs in the AL (γa = 8 dB),
and in the RL (γr = 12 dB) with interference (γi = 6 dB, Ψ(1) =0 dB). Numbers of
PRB’s are Nr,PRB = 2 and Na,PRB =2 (’o’), 8 (’�’) . Solid blue curves refer to the
case where FQCP is applied in both the desired RL and the interfering link, dashed
red curves refer to the case where FQCP is applied only in the desired RL.
4.6 Conclusion
Interference in the RL may severely deteriorate the e2e performance of a prac-
tical dual-hop half duplex DF relaying system. This type of relaying is already
standardized in 4G LTE and will be evidently part of 5G as well. In mobile
systems RNs are preferably employed in the macro-cell edge to improve the
network coverage. Hence, RL can easily become a bottleneck due to interfer-
ence from adjacent macro eNBs. In order to mitigate the impact of interfer-
ence we proposed a simple and practical approach where the signal from the
dominant interfering eNB is mitigated by applying a few bit channel feedback
from the RN to the interfering eNB. The proposed feedback method is already
standardized in 4G to enhance the signal strength in the direct link between
the eNB and UE.
To that end, we derived analytical expressions for the CDF of SINR and the
outage probability on RL by assuming the Rice and Rayleigh fading combina-
tions for the dedicated link and the interfering link. The obtained analytical
results are based on very recent results for Marcum Q-function that enable our
analysis of precoded signal over interfered Rice fading channel. In addition,
we deduced outage probabilities for different interference and feedback sce-
narios using the obtained SINR distributions. Numerical simulations were
78
Practical Interference Mitigation for the Relay Backhaul Link
utilized to verify the analytical results. It was observed that the mitigation
of the dominant interferer on RL enables significant performance gains espe-
cially if the RL creates a bottleneck for the e2e performance. Moreover, for the
Rice fading case with notable K-factor, the required feedback capacity is very
small while gains can be large. The said method is suitable for the infrastruc-
ture relaying where RN can be placed over a rooftop or on a location with LoS
connectivity towards the macro eNBs.
79
5. Rapidly Deployable Relays for IndoorEnvironments
5.1 Background and Motivation
5.1.1 Background and motivation
Early mobile networks were designed for voice services and networks were
- in principle - dimensioned to enable an acceptable indoor voice cover-
age [235–237]. Yet, the importance of indoor coverage and capacity were sig-
nificantly underestimated. Nowadays 85% to 90% of mobile services are spent
indoors [238,239], the role of indoor data services has become crucial [239,240]
and practically everybody use mobile devices (e.g., global mobile subscription
reached 7.5 billion in Q3 2016 [241]).
The indoor cellular coverage degrade due to penetration loss experienced by
radio signals obstructing through concrete building floors, walls and mod-
ern window glasses [237, 242–245]. To that end, a number of solutions are
being explored to address indoor coverage problems. Solutions include high
transmission power macro cells that adjust different parameters (e.g., antenna
elevation and downtilt, carrier frequency, etc.) [245–247]. Similarly, pico cell
deployments [248, 249], distributed antenna system (DAS) [250–252], leaky-
feeder installation in tunnels [253, 254] and small cells/femtocells deploy-
ments [255,256] have been introduced to improve indoor coverage. Some ref-
erences are summarized in Table 5.1.
Table 5.1. Legacy solutions for enhancing the indoor wireless coverage
Technology Macro Cell Pico Cell FemtocellDAS & Leaky
Feeders
References [245–247] [248, 249] [255, 256] [250–254]
While technology for indoor coverage provision has been developing fast,
81
Rapidly Deployable Relays for Indoor Environments
there are some serious challenges especially from public safety perspective.
In many indoor locations mobile system capacity is still low making it diffi-
cult e.g. for emergency responders to use video services indoors. Even voice
calls might be blocked due to increased local network traffic. Also, in case of
various disasters local network (base stations and backbone lines) might be
damaged making services unavailable [257–259]. Yet, for emergency respon-
ders it is critically important [260], to quickly establish the communication
link since it significantly enhances the effectiveness of rescue operations and
enables real time critical information sharing [261–263]. Unfortunately, exist-
ing indoor solutions (mentioned in Table 5.1) may be either unavailable (e.g.
due to damage in the emergency) or incapable of providing the QoS and ro-
bustness required emergency operations.
A rapidly deployable relaying network provides an attractive solution for the
improved coverage in several public safety scenarios [158]. Relaying enables
wireless backhaul, prompt and flexible deployment on demand [153] (i.e., no-
madic/moving RN in dynamic radio access networks [152]) and temporary
coverage solution that can meet the public safety requirements. Moreover, a
rapidly deployable RN with exclusive frequency resources may become use-
ful when there is network congestion due to repeated call attempts by the
affected/concerned individuals.
The remaining part of the chapter is organized as follows. Section 5.2 will
provide a brief background literature review of previous contributions in the
context of rapid deployments. Sections 5.3 and 5.4 explain the system models
and simulation parameters and Section 5.5 presents the performance evalua-
tion along with description of simulation results for each deployment option.
Finally, Section 5.6 concludes the study.
5.2 Previous work and Contributions
In literature, several rapidly deployable network prototypes are being stud-
ied as summarized in Table 5.2. For example, an adhoc network is proposed
in [159] deploying portable RN devices which act as a backbone to extend the
wireless coverage in a given incident area. In [160], an airborne wireless sen-
sor networks with emergency communication network is proposed, capable
of sending the GSM warning text messages to the people located in the dis-
aster areas. Wan-Yi et al. proposed in [264] a WiMAX pico base station based
network which can provide cellular coverage to the emergency responders on
urgent cases. In [255, 256], authors proposed solutions for providing a rapid
82
Rapidly Deployable Relays for Indoor Environments
indoor broadband services by exploiting the existing user-deployed closed ac-
cess residential small cells. The work done in [265] propose an airborne 4G
enabled light weight base station that is sent to high altitudes to provide a 4G
coverage in the emergency area.
In [266], a heterogeneous rapid deployment has been presented: Rapid Emer-
gency Deployment mobile Communication (REDComm). In REDComm sev-
eral communication access technologies are aggregated on same platform
providing communication services to all type of users in disaster areas. Sev-
eral REDComm nodes are connected with each other via a mesh network of
802.11a cognitive radio technology operating in unlicensed TV white space
spectrum. The REDComm deployment is dependent on the satellite for con-
nectivity with the outside world. The investigation done in [155] employs the
static and mobile RN to provide link between disconnected nodes and the
nodes located in coverage area, in order to provide cellular coverage in a dis-
aster area. Mentioned work mainly aims to provide alternate communication
links between the network nodes in order to enhance the network sustainabil-
ity in disaster scenarios.
Authors in [267] developed prototype network: Movable and Deployable Re-
source Unit (MDRU), operating in natural disaster area to provide cellular cov-
erage. This system relies on satellite communication to connect with the core
network. EmergeNet, a rapidly deployable cellular type network proposed
in [268] which enables free voice calling and messaging services to the first
emergency responders in the disaster situations. Moreover, the work in [269]
proposes that a multi-hop wireless networks is extended by mobile devices.
This system is known as on-the- fly establishment of multi-hop wireless access
networks (OEMAN). In OEMAN, several mobile devices extend the cellular
coverage to the people present in the emergency locations.
Table 5.2. Proposed and existing rapidly deployable network solutions
Reference Prototype Network Type
[160] WSN Emergency Communication Network (ECN)
[264] WiMAX Picocell Pico Network
[255,256] Femto Cell Femto Cell Network
[265] Helikites Airborne 4G enabled Light Weight Base Station
[266] REDComm Rapid Heterogeneous type Deployment
[267] MDRU Standalone Network
[268] EmergeNet Cellular type Network
[269] OEMAN Extended Cellular type Network
[155,159] Relay Device Adhoc Network
83
Rapidly Deployable Relays for Indoor Environments
Below are some of the benefits of relaying in this context:
Easy deployment/Low cost
Unlike the wireline nodes (e.g., macro, pico, femtocells, etc) as mentioned in
Table 5.1, RN can be connected to the network in almost all locations to pro-
vide local coverage and improved throughput. RN can be designed to be self-
configurable and if highly directive antennas or high transmission powers are
not needed, then cost of the design can be kept low.
Wireless relay backhauling
Unlike the aforementioned prototypes (summarized in Table 5.2) [266, 267],
RN can exploit different RL options to connect the users with the core net-
work. That is, RL can be carried out via macro base stations, using a satellite
link or even via an airborne relay.
Mobile/Nomadic character
The mobile and nomadic nature of RN [152] can be exploited to enable a tem-
porary coverage. The future relaying technologies are expected to be highly
dynamic, energy efficient [151,156] and flexible. Nomadic RN can be mounted
on a vehicle and deployed in the close proximity of a building. This will mini-
mize e.g. the penetration losses when the signal transmission is crossing from
outdoor-to-indoor [17, 245].
This chapter considers rapidly deployable RN used to enable reliable connec-
tivity for the end users in the indoor environments. Therefore, the special
focus is in outdoor-to-indoor relaying. As performance indicators we use the
system throughput and outage probability. We note that several studies has
been done on the feasibility of relaying system for improving the indoor cover-
age. For example, in [17], the network densification was used with infrastruc-
ture RN to enhanced the indoor system capacity. Similarly, relaying indoor
performance evaluation was also done in [13, 14]. Some of the outdoor-to-
indoor relaying results reported in this chapter are also published in [14].
DeNB RN
Figure 5.1. Relaying outdoor-to-indoor coverage for an UE located inside the building
84
Rapidly Deployable Relays for Indoor Environments
5.3 Description of the relay deployment cases
The study of outdoor-to-indoor relaying is carried out for three different case
studies or building designs, namely as 5×5 grid synthetic building used in
3GPP indoor simulation studies [270], dual strip synthetic building model
also used in 3GPP studies [270], realistic building design captured within the
WinProp radio propagation modelling environment [271].
These three case studies described further in the remainder of this sub-section.
5.3.1 5×5 Grid deployment case
The 5×5 apartments layout is one of the 3GPP dense-urban models [270]. This
model consists of 25 square apartments with each size of 10×10 m as shown
in Figure 5.2. We consider only one floor of the building. Building is located
either in the macrocell center or on the edge of the macrocell. The overlaying
macro network consists of seven sites controlled by eNBs. Each site employs
three antennas to provide coverage to three sectors. Each sector contains 10
randomly located outdoor UEs. Eight indoor UEs are deployed inside the
building so that there is at most one UE inside each apartment. We assume
one RN located outside the building 30 m - 60 m away from the external wall
of the building providing the outdoor-to-indoor coverage.
eNB
Relay Node
Outdoor UE
Indoor UE
Tri-Sectored Hexagonal Cellular Network 5 x 5 Grid Building at Cell Edge
0 5 10 15 20 25 30 35 40 45 500
5
10
15
20
25
30
35
40
45
50
60m, 30m
Building at cell center Building at cell edge
Figure 5.2. Relay enhanced cellular network with 5×5 apartment model
5.3.2 Dual Strip deployment case
The 3GPP dual strip model is composed by two blocks of apartments as shown
in Figure 5.3 [270]. Each strip includes two rows of apartments and strips
are separated by a street of 10 m width and they are three floors high. We
85
Rapidly Deployable Relays for Indoor Environments
assume only one dual-strip block located either on the macro cell center or
on the macro cell edge. Network consists of seven eNB sites. Eight indoor
UEs are randomly deployed in strips such that there is at most one UE in
each apartment. We assume two different locations for the relay node. These
locations are: 25m away from the external wall and at the middle of two strips,
see Figure 5.3.
eNB
Relay NodeOutdoor UEIndoor UE
Tri-Sectored Hexagonal Cellular Network
Building at cell center Building at cell edge
Dual Strip Building Model(Three-Storey)
RN located beside
Indoor UEs
25m
10m
RN located at middle
10m
10m
Figure 5.3. Relay enhanced cellular network with 3GPP dual strip building model
5.3.3 Realistic deployment case
In this case we employ the building model that is available within the Win-
prop Software suite [271], see Figure 5.4. The Winprop suite is a software tool,
which includes ray tracing capabilities for modeling of multipath propagation
in different indoor and outdoor realistic environments. Ray tracing technique
takes into account all scattering and reflecting surfaces of each of the signal
rays in the environment.
For this study, the ray tracing method utilized within WinProp is the domi-
nant path model, which provides required trade-off between computational
complexity and accuracy of path loss predictions. The WinProp tool also en-
ables the penetration losses of different building materials (walls, glass, doors,
floors etc.) to be specified explicitly from a provided materials database. For
this case study, we consider an urban area with multiple buildings and a mo-
bile network consisting of four tri-sectored macro sites. From this area we
focus on one particular building as location targeted for outdoor-to-indoor
relaying to provide connectivity to 8 UEs located within this building (see
Figure 5.4). Three candidate relay locations considered (two outdoor and
86
Rapidly Deployable Relays for Indoor Environments
one indoor for comparison) as shown in Figure 5.5.
Location 1
Location 2
Indoor LocationeNB 1
eNB 2
eNB 3eNB 4
Tri-Sectored 4 eNBs relaying enhanced cellular network
Tri-sectored eNB Relay Node
Figure 5.4. Relay enhanced cellular network with realistic building model.
Location 1
Location 2
Indoor Relay
eNB 1
Figure 5.5. Relay locations around the proposed building.
5.4 System Model
We investigate the system performance for ideal and non-ideal RL. The ideal
RL does not restrict the AL capacity and relates to the case where RN has
particularly good channel conditions and frequency resources are not setting
capacity limitations. That is, RN with ideal RL is comparable to a pico node
deployment. While in case of non-ideal RL, the RL capacities could be bottle-
neck for the AL capacities.
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Rapidly Deployable Relays for Indoor Environments
Table 5.3 summarizes the RL scenario, scheduling scheme and propagation
model combinations in the forthcoming study. Scheduling, channel models
and other system parameters are described in more details in the following
sections.
Table 5.3. Ideal and non-ideal relay backhauling
RL Backhauling Deployment Scheduling Schemes Propagation Models
Ideal RL 5×5 Grid Round Robin (RR) 3GPP
Non-Ideal RL
5×5 Grid
Dual Strip
Realistic
Max-Min Fairness (MMF) COST-231 WI
5.4.1 Throughput Model and Radio Resource Scheduling
In case of non-ideal RL we employ the resource scheduling strategy as shown
in Figure 2.13. There the DeNB allocates three (MBSFN) subframes out of the
ten to RL while the remaining seven subframes are shared by DeNB and RN
to serve UEs. The amount of data transferred over the RL (per 10 msec radio
frame) is denoted by Dr:
Dr = NMBSFN ·NPRB ·BPRB · Sr, (5.1)
where NMBSFN refers to the number of MBSFN subframes, NPRB denotes the
number of physical recourse blocks per subframe andBPRB denotes the band-
width of a PRB. The Sr is approximated from Υr via the modified Shannon’s
formula which is adjusted by two parameters namely bandwidth efficiency (
Beff ) and SINR efficiency (Υeff ):
Sr = Beff · log2(1 +
Υr
Υeff
). (5.2)
Here, Υr =PRx,DeNB
Pn +∑
PRx,Other eNBs, PRx,DeNB is the desired received power by
RN from DeNB and PRx,Other eNBs is the power received by the RN from other
neighbouring eNBs. Similarly, the transferred data over the AL is given by
Da =U∑
u=1
Da,u =U∑
u=1
NASF ·NPRB,u ·BPRB · Sa,u, (5.3)
where U refers to the number of UEs served by RN, Da,u is the individual
throughput of a uth user, NASF is the number of subframes allocated to AL
in a 10 msec frame, NPRB,u denotes the number of physical recourse blocks
allocated to uth user and Sa,u is the spectral efficiency on AL of uth user. The
spectral efficiency for an UE with Υa,u is given by
Sa,u = Beff · log2(1 +
Υa,u
Υeff
). (5.4)
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Rapidly Deployable Relays for Indoor Environments
We employ the Max-Min fairness (MMF) scheduling in the AL. This scheme
aims to enhance the throughput performance of indoor UEs, experiencing
the worst SINR levels. The worst indoor UEs are being allocated with more
PRBs so that all the UEs achieve the same throughput. MMF is especially
suitable in public safety scenarios if there is no prioritization between different
(authorized) users. If some rescue teams or actors in the field are prioritized
over others, then MMF needs to be modified accordingly.
The number Na of allocated resource blocks in AL is limited such that Na =
NASF · ∑Uu=1NPRB,u ≤ Nmax. We also assume that AL throughput Da per
each 10 msec frame is not exceeding the RL capacity Dr. That is, there is no
buffer in the RN. We require that for the individual UE throughput Da,u there
holds Da,u ≥ Dmin. The scheduling is described in Algorithm 1. We assume
an initial maximum and minimum data rate values, i.e., D0 and Dmin, respec-
tively, for the AL and direct link. Then D0 is decreased with 100 kbps per step
if the resource allocation fails with current D0.
Algorithm 1 Max-Min fairness scheduling
1: Compute the RL throughput Dr per 10 msec using (5.1)
2: Assume the initial value of D0
3: Compute the individual throughputs for UEs such that Da,u ≥ D0,
4: if Na ≥ Nmax then
5: Decrease the value of D0 and proceed to 3.
6: if D0 < Dmin then
7: Drop the worst indoor UE.
8: end if
9: end if
5.4.2 Propagation Models
We employ the 3GPP and COST 231 Walfisch-Ikegami channel models for
the path loss estimation in the direct link, RL and AL, see Table 5.4. There,
S denotes the distance in kilometers between the transmitter and receiver
and fc represents the carrier frequency in MHz. We note that in 3GPP mod-
els of Table 5.4 path loss is computed from formula Prob(S)PLLoS + (1 −Prob(S))PLNLoS.
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Rapidly Deployable Relays for Indoor Environments
Table 5.4. COST-231-WI and 3GPP channel models assumed in simulations
Links Propagation Models
Direct Link COST231−WI PLNLoS = 42.497 + 38 · log10(S) + 24.5 · log10(fc) + 0.00162 · fc · log10(fc)Relay Backhaul Link COST231−WI PLNLoS = 34.539 + 38 · log10(S) + 24.5 · log10(fc) + 0.00162 · fc · log10(fc)Access Link COST231−WI PLNLoS = 42.6 + 26 · log10(S) + 20 · log10(fc)Direct Link 3GPP Prob(S)Urban = min(0.018S , 1) · (1− exp(
−S0.063
)) + exp(−S
0.063)
Prob(S)Suburban = exp−(S−0.010.2
)
PLLoS = 103.4 + 24.2 · log10(S)PLNLoS = 131.1 + 42.8 · log10(S)
Relay Backhaul Link 3GPP Prob(S)Urban = min(0.018S , 1) · (1− exp(−S
0.072)) + exp(
−S0.072
)
Prob(S)Suburban = exp−(S−0.010.23
)
PLLoS = 100.7 + 23.5 · log10(S)PLNLoS = 125.2 + 36.3 · log10(−S)
Access Link 3GPP Prob(S)Urban = 0.5−min(0.5, 5 · exp(−0.156S
)) + min(0.5, 5 · exp( −S0.03
))
Prob(S)Suburban = 0.5−min(0.5, 3 · exp(−0.3S
)) + min(0.5, 3 · exp( −S0.095
))
PLLoS = 103.8 + 20.9 · log10(S)PLNLoS = 145.4 + 37.5 · log10(S)
5.4.3 Antenna pattern
The antenna gain depends on the antenna pattern of a transmitter. Antenna
pattern is a 3D graphical representation of antenna radiation properties as
a function of direction. The 3GPP pattern for a directional antenna is given
by (5.5) [173, 272]:
A(θ) = −min
[12
(θ
θ3dB
)2
,Am
], (5.5)
where A(θ) denotes the antenna gain at angle θ such that −180o ≤ θ ≤ 180o
and θ3dB denotes the angle of direction where gain is 3 dB lower than in the
main direction. The antenna front-to-back ratio is denoted by Am and it has
value 25 dB and 20 dB for macro eNB and relay node antennas, respectively.
5.4.4 System parameters and assumptions
We focus on the downlink of 3GPP Case 1 (Urban) and Case 3 (Suburban)
macro-cell layouts with inter-site distances (ISD) 500 m and 1732 m respec-
tively. For the current study, we ignore the interference received on RL from
the neighboring eNBs. Table 5.5 presents the simulation parameters for all
three deployment cases.
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Rapidly Deployable Relays for Indoor Environments
Table 5.5. Simulation Parameters
Parameters 5×5 Grid Dual Strip Realistic
Ideal RL Non-Ideal RL Non-Ideal RL Non-Ideal RL
Air Interface LTE-FDD LTE-FDD LTE-FDD LTE-FDD
Carrier Frequency 2000 MHz 800 MHz 2000 MHz 400 MHz
Operational BW 10 MHz 10 MHz 10 MHz 5 MHz
Fading Log-Normal Shadowing, WinProp [271], Rayleigh/Rician
Propagation 3GPP COST-231 WI 3GPP Winprop [271]
Scheduling RR MMF MMF MMF
Standard deviation 8 dB for Direct Link, 6 dB RL, 10 dB AL –
Penetration loss 20 dB 0.6 dB/m 0.6 dB/m –
Thermal Noise PSD -174 dBm/Hz -174 dBm/Hz -174 dBm/Hz -174 dBm/Hz
BW Efficiency 0.88 0.88 0.88 0.88
SINR Efficiency 1.25 1.25 1.25 1.25
IL 5 dB 5 dB 5 dB 5 dB
Macro Parameters
Tx Power 46 dBm 46 dBm 46 dBm 46 dBm
Ant Pattern A(θ) = - min
[12
(θ
θ3dB
)2
,Am
]θ3dB = 70 ◦, Am = 25 dB Directional
Ant Elevation 25 m 25 m 25 m Table 5.6
Ant Configuration Tx-2, Rx-2 Tx-2, Rx-2 Tx-2, Rx-2 Winprop [271]
eNB Ant Gain 14 dBi 14 dBi 14 dBi –
Intersite Distance 500 m / 1732 m 500 m 500 m –
Diversity Gain 5 dB 6 dB 6 dB 6 dB
Relay Node Parameters
Tx Power 30 dBm 30 dBm 30 dBm 30 dBm
Ant Pattern Omni & Directional Omni Omni
Ant Elevation 5 m 5 m 5 m Table 5.6
Ant Configuration Tx-1, Rx-2 Tx-1, Rx-2 Tx-1, Rx-2 Winprop [271]
Diversity Gain 5 dB 6 dB 6 dB 6 dB
UE Parameters
Height/Location UE height varies from 1.5 m to 7 m
Noise Figure 9 dB 9 dB 9 dB 9 dB
UE Number 10 Outdoor and 8 Indoor LTE-UEs
Ant Configuration Tx-1, Rx-2 Tx-1, Rx-2 Tx-1, Rx-2 Tx-1, Rx-2
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Rapidly Deployable Relays for Indoor Environments
Table 5.6. Antenna heights in realistic deployment case
eNB/RN Height (m)
eNB 1 Ant 1 50
eNB 1 Ant 2 40
eNB 1 Ant 3 40
eNB 2 Ant 1 25
eNB 2 Ant 2 25
eNB 2 Ant 3 25
eNB 3 Ant 1 25
eNB 3 Ant 2 25
eNB 3 Ant 3 25
eNB 4 Ant 1 50
eNB 4 Ant 2 50
eNB 4 Ant 3 50
Indoor RN 1
Outdoor RN 10
5.5 Performance Evaluation and Simulations
This section presents the simulation results generated in the selected deploy-
ment cases. Network performance is explained in terms of SINR per PRB and
indoor UEs throughput.
5.5.1 5× 5 Grid deployment case
We consider two cases: RL is ideal and RL is non-ideal. Furthermore, we carry
out a comparative performance evaluation when RN is deployed at 60 m and
30 m away from the center of the building.
Ideal RL
Here we assume the RR scheduling in AL and direct link. Figure 5.6 shows
the outdoor-to-indoor relaying performance for different RN locations. The
eNB only network case has been presented for the sake of benchmarking. Fig-
ure 5.6 is comprised of four sub plots which show the CDFs of the SINR per
PRB (a & c) and indoor UE throughput (b & d). The performance analysis has
been done for scenarios where building is on the macro-cell center and on the
macro-cell edge.
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Rapidly Deployable Relays for Indoor Environments
Figure 5.6. REC performance when RN is located 60 m and 30 m away from the center of the
building, (a) SINR per PRB (Cell Center), (b) Indoor UE throughput (Cell Center),
(c) SINR per PRB (Cell Edge), (d) Indoor UE throughput (Cell Edge),
As expected, RN deployment deteriorate the SINR levels of indoor UEs espe-
cially when building is at cell center due to high interference received from
nearby high power transmission of eNBs as shown in Figure 5.6 (a). However,
SINR is considerably improved when the RN-Building distance is reduced
(i.e., when it is 30 m away from the building center) as compared to the 60 m
separation. Table 5.7 presents SINR improvements for different RN locations
in both ISD=500 m and ISD=1732 m cases.
Although SINR is decreased due to RN deployment, it considerably improves
the indoor UE throughput as compared to the eNB only case. This follows
from the fact that resources in AL are less competed than in the direct link
where eNB also serves outdoor UEs. The indoor UE throughput are shown
in Figure 5.6 (b) and (d). If building is on the cell edge, then RN provides good
performance gain as compared to the eNB only case. From Figure 5.6 (c) we
find that SINR is degraded less in case of macro-cell edge deployment than
it is in the macro-cell center case. These SINR gains along with reduced the
contention for RN radio resources translate to the enhanced UE throughput.
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Rapidly Deployable Relays for Indoor Environments
Table 5.8 shows the throughput gains of relaying.
Table 5.7. Achievable SINR for indoor relay UEs for different RN locations and ISDs
SINR Enhancements (dB)
ISD 500 m
RN-Building DistanceCell Center Cell Edge
10th %-ile 50th %-ile 80th %-ile 10th %-ile 50th %-ile 80th %-ile
30 m 0.5367 5.6279 11.1129 0.3053 6.1023 11.8682
50 m -0.4250 3.9264 8.4634 -0.7235 3.9931 8.4943
60 m -0.5459 3.6054 8.2098 -1.1602 3.1696 7.4261
70 m -0.7474 3.1359 7.9763 -1.5920 2.4297 6.5254
ISD 1732 m
10th %-ile 50th %-ile 80th %-ile 10th %-ile 50th %-ile 80th %-ile
30 m 10.8367 22.5908 29.8260 18.0678 27.9942 33.5150
50 m 8.3682 18.8225 25.4026 14.4898 23.9955 29.2661
60 m 7.3320 17.0700 23.8905 12.7769 22.5130 27.5884
70 m 6.4327 15.7102 22.5332 10.9306 21.0087 26.1429
Table 5.8. Achievable throughput for indoor relay UEs for different RN locations and ISDs
Throughput Enhancements (Mbps)
ISD 500 m
RN-Building DistanceCell Center Cell Edge
10th %-ile 50th %-ile 80th %-ile 10th %-ile 50th %-ile 80th %-ile
30 m 0.5696 1.6394 3.3166 0.5442 1.6243 3.2905
50 m 0.4299 1.3861 2.7970 0.3854 1.3124 2.7889
60 m 0.3972 1.2072 2.5243 0.3457 1.1712 2.6416
70 m 0.3847 1.1206 2.1578 0.3148 1.0156 2.3741
Figure 5.7 presents results when RN utilizes either omni-directional or direc-
tional antennas for the transmission on AL. It is noted that the use of direc-
tional antenna provides notable performance gain in terms of SINR (a) and
throughput (b) as compared to the omni-directional antenna case as evident
in Tables 5.9 and 5.10. This is because the directional antenna concentrate the
relay transmission to the indoor RUEs as well as decrease the interference to-
wards the indoor UEs served by eNB as shown in Figure 5.7 (a). The improved
SINR levels are directly translate to enhanced throughput as can be seen from
Figure 5.7 (b). Tables 5.9 and 5.10 present the performance enhancements due
to directional antenna on AL.
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Rapidly Deployable Relays for Indoor Environments
Figure 5.7. SINR and throughput when building is located at the macro-cell edge and RN is 60
m away from the center of the building. Omni-directional and Directional antennas
are used in the AL transmission, (a) SINR per PRB and (b) Indoor UE, MUE and
RUE throughput.
Table 5.9. SINR gains by using directional Antennas for AL transmissions with ISD 500 m and
RN distance of 60 m
Impact of omni-directional and directional antennas on AL SINR (dB)
User Equipment
Cell Center
Omni Directional
10th %-ile 50th %-ile 80th %-ile 10th %-ile 50th %-ile 80th %-ile
Indoor UE -0.5459 3.6054 8.2098 -0.0533 4.3623 9.2467
Indoor RUE -0.5459 3.1447 7.2116 0.0757 4.2252 8.6904
Indoor MUE -0.6627 3.8044 8.9427 -0.1484 4.4230 9.5552
User Equipment
Cell Edge
Omni Directional
10th %-ile 50th %-ile 80th %-ile 10th %-ile 50th %-ile 80th %-ile
Indoor UE -1.1602 3.1696 7.4261 -0.4548 4.5592 9.0919
Indoor RUE -0.7220 3.5206 7.5625 0.0809 5.0851 9.5924
Indoor MUE -1.3477 2.9325 7.3938 -0.7852 4.1930 8.7780
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Rapidly Deployable Relays for Indoor Environments
Table 5.10. Throughput enhancements by using directional antennas for AL transmissions
with ISD 500 m and RN distance of 60 m
Impact of omni-directional and directional antennas on AL throughput [Mbps]
User Equipment (UE)
Cell Center
Omni Directional
10th %-ile 50th %-ile 80th %-ile 10th %-ile 50th %-ile 80th %-ile
Indoor UE 0.3972 1.2072 2.5243 0.4488 1.5788 3.4511
Indoor RUE 1.0560 2.5398 5.0902 1.2040 2.8811 5.4756
Indoor MUE 0.3699 1.0318 2.0468 0.4003 1.2293 2.5882
User Equipment (UE)
Cell Edge
Omni Directional
10th %-ile 50th %-ile 80th %-ile 10th %-ile 50th %-ile 80th %-ile
Indoor UE 0.3457 1.1712 2.6416 0.3853 1.4946 3.4518
Indoor RUE 0.9178 2.1525 3.9245 1.0613 2.5765 4.7295
Indoor MUE 0.3153 0.9016 2.0731 0.3379 1.0737 2.7921
Non-ideal RL
If RL is not ideal, then we employ the resource scheduling strategy of Fig-
ure 2.13 to share the radio resources among the direct link, RL and AL. That
is, DeNB allocates three LTE subframes out of ten per each radio frame for RL
while the remaining seven subframes are allocated for the direct link in DeNB
and AL in RN.
Figure 5.8. Performance when building is at the macro-cell center, (a) Indoor UE throughput,
(b) Indoor RUE throughput, (c) Indoor MUE throughput, (d) Number of indoor
UEs embraced to macro eNB and RN.
Figure 5.8 presents the indoor UE (for both indoor MUEs and RUEs) through-
put performance when ISD=500 m and building is at the macro-cell center.
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Rapidly Deployable Relays for Indoor Environments
The RN distance from the center of the building is either 60 m or 30 m. Re-
sults show that the use of an RN clearly improves the indoor UE performance
as compared to eNB only scenario. Moreover, the indoor UE performance
gain is further improved when the RN is deployed closer to the building.
Figure 5.9 presents the impact of subframe allocation to RL. Since AL is the
bottleneck now, the UE performance becomes worst when more resources are
allocated to RL. This is interesting since in most of the deployment options
studied in literature RL represents the end-to-end performance bottleneck.
1 2 3 4 5 6 70
0.5
1
1.5
2
2.5
3 106
Figure 5.9. Indoor UE performance for different RL subframe (MBSFN) allocations.
5.5.2 Dual Strip deployment case
Here we assume two alternative locations for the RN deployment namely, be-
side the building strips and in the middle of two strips as shown in Figure 5.3.
Figure 5.10 presents the indoor UE throughput when building is located at
the macro-cell center. It is observed that RN provides throughput gains in
both deployment scenarios as compared to the eNB only case. It is also noted
that there is more indoor UEs connected to RN, if it is deployed in the middle
of strips as compared to the scenario where RN is deployed beside the build-
ing strips. Moreover, indoor UE throughput are further improved when the
building and RN are located at the macro-cell edge.
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Rapidly Deployable Relays for Indoor Environments
Figure 5.10. UE throughput when RN is located beside and middle of the building strips and
building is located either at the macro-cell center or at the macro-cell edge, (a)
Indoor UE throughput, (b) Indoor UEs connected to eNB and RN
5.5.3 Realistic deployment case
In this deployment we assume two outdoor RN locations (i.e., location 1 and
location 2) and one indoor RN location as shown in Figure 5.5. To that end,
Figure 5.11 presents the throughput of indoor RUEs and indoor MUEs.
We find from results that indoor UE performance is improved when it is
served by RN located inside the building as compared to the case where RN
is in either of the outdoor locations, see Figure 5.11 (a). Yet, this follows from
the fact that there are less UEs connected to RN if it is located indoors, see Fig-
ure 5.11 (b). Then RN coverage is comparatively good on the deployed floor
as compared to the rest of the floors in the building. Moreover, the number of
UEs connected to RN when it is in the location 1 is higher than in location 2,
because the RN transmission on AL is less interfered by macro eNB in loca-
tion 1. It is also observed that RN experiences good RL at location 2, but still,
its indoor UEs receives high interference from macro eNB and throughput on
the AL are reduced.
Figure 5.11. UE throughput and connection nodes when RN is deployed at outdoor locations 1
or 2, or inside the building, (a) Indoor UE throughput, (b) Number of indoor UEs
connected to eNB and RN
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Rapidly Deployable Relays for Indoor Environments
5.6 Conclusions
We considered few rapid RN deployment scenarios within the 3GPP LTE-A
Type 1 inband relaying framework. The main objective was to examine the
relaying indoor performance in three different RN deployment cases, namely
in 3GPP 5×5 Grid, in 3GPP Dual-strip and in a realistic deployment cases.
We showed via system level simulations that RN can be used to improve emer-
gency service coverage and capacity in the studied outdoor-to-indoor cases.
The RUEs experience high interference from macro eNB, but still these UEs
experience less competition for the radio resource on AL and throughput is
improved accordingly. It was also observed that the end-to-end UE perfor-
mance depends on the propagation conditions on both RL and AL. The LoS
condition can be obtained due to the flexible and rapid-deployment nature of
RN. Simulation results also show that the indoor UE SINR is enhanced the
closer the RN is deployed to the building. Performance is in all cases better
when the building is located at the macro-cell edge. If RN employs directional
antenna for AL transmission, the system performance is improved because
the RN transmission is then focused towards the dedicated indoor UEs. Di-
rective antenna also minimizes the likelihood of RN interference towards the
UEs served by macro eNB.
Finally, we also addressed the radio resource management (RRM) issues in a
network with outdoor-to-indoor relaying. We considered the resource split-
ting between the RN and UE on direct link at DeNB and employed MMF
scheduling scheme on AL. The MMF scheduling scheme aims to prioritize
the worst indoor UEs. According to the simulation results, worst indoor UEs
performance was considerably improved.
99
6. Conclusions and future work
Mobile communication technologies have developed rapidly to meet the chal-
lenges of rapidly increasing data service demands. To that end, relaying is one
of the attractive candidate technologies to fulfill the stringent requirements
set by ITU-R and 3GPP for future mobile communication systems. This the-
sis focused on a two-hop decode-and-forward relaying in a mobile commu-
nication network. Thesis contributions include design, analysis and perfor-
mance evaluation of different radio resource usage approaches between the
two hops (i.e., between Relay Link (RL) and Access Link (AL)). Study also
covers interference mitigation methods that can be used to relax the RL limi-
tation that may become a bottleneck for the end-to-end (e2e) capacity. Finally,
thesis considers the outdoor-to-indoor coverage improvements using rapidly
deployable relays.
6.1 Analysis of Optimal Resource Sharing
The theme of the analysis carried out in Chapter 3 was the rate enhancement
through Resource Allocation (RA) between the RL and AL. In addition, the
performance impact from DL/UL decoupling was investigated.
A comparative analysis of conventional RA, fixed RA with and without buffer-
ing, and (resource) optimal RA were carried out. Closed-form expressions
were derived for the mean and outage e2e data rate of the two-hop DF relaying
system. In case of optimal RA the rate analysis led to an integral presentation
for which a tight (closed-form) lower bound was found.
The obtained closed-form expressions enabled a simple performance evalu-
ation where mathematical formulas were also validated through numerical
simulations. Results show that RA is essential for the performance of DF re-
lay systems. The (resource) optimal RA clearly improves the e2e relaying per-
formance but requires exchange of fast scheduling information. On the other
101
Conclusions and future work
hand, buffer can be used to compensate the lack of fast scheduling informa-
tion and fixed RA with large buffer shows very good performance. Of course
buffering increases the delay and is not necessarily feasible for all service
types. The fixed RA without buffer is feasible if service demand is not chang-
ing fast while conventional RA is clearly inferior to other RA schemes and
benefits only from its implementation simplicity. Finally, it was shown that
decoupling the DL/UL transmissions improve the spectral efficiency. This
approach will be especially beneficial when tackling the imbalance between
small (relay controlled) cells and large macro-cells.
Future work on resource optimal relaying will focus on two aspects: the spe-
cific RRM protocols and the impact of multi-antenna methodologies on the
performance of resource optimal relaying. The current analysis is very generic
and covers the case where the time sharing between RL and AL resources is
done. Yet, e.g. in 4G and 5G radio resources are forming a time-frequency
resource pool and user is assigned resources to satisfy the rate request. If
e2e link needs to carry the same amount of resources over RL and AL (as in
resource optimal relaying), then e2e scheduler should take into account the
resource needs of different users when scheduling them. If rates are not high
and number of users is large, then resource scheduling close to optimal can be
executed but in case users occupy large amounts of time-frequency resources,
it will be challenging to obtain optimal e2e scheduling. Future research will
focus on this aspect. We will, for example, consider how multi-antenna meth-
ods and different fast scheduling approaches can be used to obtain optimal or
close-to-optimal resource allocation over the e2e link.
6.2 Interference Mitigation for the Relay Backhaul Link
This section concludes the work in Chapter 4. There special focus was on the
DL of a wireless relay backhaul, where RN, in addition to the desired signal,
also receives strong interference from the neighbouring eNB. To relax the RL
bottleneck we proposed a simple and practical approach where we apply a
few bit channel feedback to improve SINR from DeNB and to mitigate the
interference from the dominant interfering eNB.
To that end, the contribution was two-fold. First, an analytical approach for
calculating the CDF of SINR and the outage probability in the RL was pro-
posed. The Rice and Rayleigh fading combinations for the desired and dom-
inant interfering links were assumed. Outage probabilities were deduced for
different interference and feedback scenarios based on the obtained SINR dis-
102
Conclusions and future work
tributions. Results showed that the mitigation of the dominant interferer in
the RL leads to notable performance gains especially if the RL represents a
bottleneck for the e2e relaying performance. Second, a simulation campaign
was carried out to study the performance of proposed transmit beamforming
schemes as compared to the baseline scenario. It was shown in LTE frame-
work that the SINR per PRB in RL and accordingly the e2e data rate can be
clearly improved with just few bit channel information that is fed back to the
interfering eNB.
The future work contains further improvements in the backhaul performance
by designing cooperative and interference-aware resource scheduling and an-
tenna schemes to enhance the e2e data rate performance. Especially, it is im-
portant to investigate more thoroughly the impact of intercell interference and
design better cooperative scheduling approaches over adjacent cells. There,
joint scheduler over multiple cells should beneficially utilise the channel feed-
back from users and the scheduling status information from different base
stations. In such scenario multi-objective optimisation will be needed.
6.3 Rapidly Deployable Relays for Outdoor-to-indoor Coverage
The performance evaluation in Chapter 5 focused on the rapidly deployable
relays that can be used in mobile systems to provide a temporary outdoor-
to-indoor coverage in e.g. emergency/public safety situations. Evaluation
considered three different building models to provide indoor environment
diversity. These deployments were: 3GPP 5x5 grid, 3GPP dual strip model
and realistic deployment case where a certain building layout with more re-
alistic (ray-tracing) propagation model was applied.
The objective of the simulation campaign was to examine the impact of
rapidly-deployed outdoor RN on the indoor service provision in terms of e2e
throughput experienced by indoor UEs. Simulation results showed that RN
can significantly improve the indoor system performance. It was observed
that the RUEs served by RN were experiencing heavy interference from the
overlaying macro eNB but they competed less for the abundant AL radio re-
sources. This led to enhanced indoor throughput.
The relaying performance depends heavily upon the propagation conditions
on both RL and AL. The Line-of-Sight towards the covered building can be
achieved due to the nomadic nature of the rapidly deployable relays. Re-
sults indicate the significant impact of the location of outdoor deployed RN
on achievable performance of UEs inside the building. Furthermore, the ben-
103
Conclusions and future work
efits of relaying became especially visible when target building was located
on the macro-cell edge where eNB coverage is weak and macro eNB will not
interfere heavily the relay AL. Moreover, it was also shown that the usage of
directional antenna in RN can provide notable additional gains. When using
so-called max-min scheduling, the relaying was clearly improving the perfor-
mance of the worst indoor UEs. This work will not be continued in future.
104
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