MOBILE CELL SELECTION IN 4G LONG TERM EVOLUTION-ADVANCED (LTE-A) NETWORKS

1

SYNOPSIS: MOBILE CELL SELECTION IN 4G LTE-A NETWORKS

A

Synopsis

ON

MOBILE CELL SELECTION IN 4G LONG TERM

EVOLUTION-ADVANCED (LTE-A) NETWORKSSubmitted in Partial Fulfillment of the Requirement

for the Award of the

Degree of

MASTER OF TECHNOLOGY

In

Electronics & Communication Engineering Submitted by

MURTADHA ALI NSAIF SHUKUR

Roll No: 11127232Under the Supervisions of

Dr. H.P.Sinha

Dr. Kuldip Pahwa

ECE Department

2014

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Department of Electronics & Communication Engineering

M.M Engineering College, Maharishi Markandeshwar

University Mullana (Ambala), Haryana, India

133207

CONTENTS1. Introduction-------------------------------------------------

----------------------------------( 4 )

2. LTE

evolution. .-------------------------------------------------

-----------------------------( 8 )

3. Introduction to LTE-Advanced

------------------------------------------------------------(

9 )

4. New features of LTE-Advanced: from Release 8 to Release 10

---------------------( 12 )

5. Difference between LTE and LTE-

Advanced-------------------------------------------( 13 )

6. Key challenges in implementing LTE and LTE-

Advanced---------------------------( 14 )

7. Cell

Selection----------------------------------------------------

----------------------------( 17 )

8. Key literature

survey.------------------------------------------------------

------------------( 18 )

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9. Motivation.--------------------------------------------------

----------------------------------( 26 )

10. Formulation of problem

-------------------------------------------------------------

-------( 27 )

11. Objectives------------------------------------------------

-------------------------------------( 27 )

12. Research

methodology--------------------------------------------------

--------------------( 27 )

13. Time

schedule .---------------------------------------------------

---------------------------( 29 )

14. References------------------------------------------------

------------------------------------( 30 )

4


LIST OF TABLES

1. Comparison of parameters of UMTS, HSPA, HSPA+ and

LTE.--------------------- ( 9 )

2. Specification of LTE

system. .----------------------------------------------------

--------- ( 9 )

3. Difference between specifications of LTE and LTE-

Advanced-----------------------( 14 )

4. Research

methodology--------------------------------------------------

--------------------( 29 )

5. Time

schedule-----------------------------------------------------

---------------------------( 29 )

5


LIST OF FIGURES

Figure (1) LTE Radio Access Network

Architecture-----------------------------------------( 5 )

Figure (2) OFDMA and SC-FDMA in LTE

---------------------------------------------------( 7 )

Figure (3) MIMO in LTE

---------------------------------------------------------------

---------( 8 )

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Figure (4) LTE /SAE

architecture---------------------------------------------------

------------( 8 )

Figure (5) MIMO scheme

(8x4)----------------------------------------------------------

-------( 11 )

Figure (6) Relay types in LTE

systems--------------------------------------------------------

( 13 )

Figure (7) Example of persistent scheduling

allocation-------------------------------------( 17 )

Figure (8) Different types of cells in a

network----------------------------------------------( 18 )

Figure (9) An example of a possible scenario in a real

network---------------------------( 28 )

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1.Introduction to LTE systemsLong Term Evolution (LTE) is the result of the standardization

work done by the 3GPP to achieve a new high speed radio access

in the mobile communications frame. 3GPP is a collaboration of

groups of telecom associations working on Global System for

Mobile Communication (GSM) [3]. 3GPP published and introduced

the various standards for IP based system in Release 8, which

was also termed Long Term Evolution and abbreviated as LTE.

Initially, LTE was introduced in the Release 8 in 2008. In

2010, the Release 9 was introduced to provide enhancements to

LTE and in 2011 Release 10 was brought as LTE-Advanced, to

expand the limits and features of Release 8 and to meet the

requirements of the International Mobile Telecommunications-

Advanced (IMT-Advanced) of ITU-R for the fourth generation of

mobile technologies (4G), and the future operator and end

user’s requirements. The key reason of the evident of the LTE-A

is the growing demand for network services, such as VoIP, web

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browsing, video telephony, and video streaming, with

constraints on delays and bandwidth requirements, poses new

challenges in the design of the future generation cellular

networks [4]. Recently in 2011, LTE was further developed

through Release 10 to satisfy ITU’s IMT-Advanced requirements

for 4G cellular systems. LTE radio transmission and reception

specifications are documented in TS 36.101 for the UE (User

Equipment) and TS 36.104 for the eNB (Evolved Node B). As per

these specifications, LTE is theoretically capable of

supporting up to 1Giga Bits per second (1Gbps) for fixed user

and up to 100 Mega Bits per second (100 Mbps) for high speed

user. This is considerably high speed. For this reason, both

research and industrial communities are making a considerable

effort on the study of LTE systems, proposing new and

innovative solutions in order to analyze and improve their

performance.

In principle, LTE access network based on Orthogonal Frequency

Division Multiple Access (OFDMA) in downlink and Single Carrier

Frequency Division Multiple Access (SC-FDMA) for uplink. It is

expected to support a wide range of multimedia and Internet

services even in high mobility scenarios[4]. Therefore, it has

been designed to provide high data rates, low latency, and an

improved spectral efficiency with respect to previous 3G

networks. LTE networks are highly flexible for a worldwide

market and offer a variable bandwidth feature that gives to

network operators the possibility to throttle the bandwidth

occupation between 1.4 and 20MHz. The most important novelty

introduced by LTE specifications is the enhanced QoS support by

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means of new sophisticated RRM (Radio Resource Management)

techniques [3]. The overall goal of LTE technology is to

significantly increasing peak data rates scaled linearly

according to spectrum allocation, improving spectral

efficiency, lowering costs, improving services, making use of

new spectrum opportunities, improved quality of service, and

better integration with other open standards. Also LTE does not

support soft handover, so there is no need for a centralized

data-combining function in the network. However, in order to

achieve these goals, LTE implementers and users have to face a

number of challenges. For example, mobile operators will need

to deal with specific challenges associated with LTE, such as

interoperability with legacy and other 4G systems, ensuring

end-to-end network QoS and high-quality service delivery, and

interaction with IMS for the delivery of multimedia services

and voice.

The LTE radio access network architecture is shown in Figure 1.

LTE encompasses the evolution of the radio access through the

Evolved Universal Terrestrial Radio Access Network (EUTRAN).

LTE is accompanied by an evolution of the non-radio aspects

under the name ‘System Architecture Evolution’ (SAE) which

includes the Evolved Packet Core (EPC) network. Together LTE &

SAE comprise the Evolved Packet System (EPS).

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Figure 1: LTE Radio Access Network

Architecture [2].

In Figure 1, EPS uses the concept of EPS bearers to route IP

traffic from a gateway in the PDN to the User Equipment (UE). A

bearer is an IP packet flow with a defined Quality of Service

(QoS) between the gateway and the UE. The E-UTRAN with EPC

together set up and release bearers as required by

applications. The eNodeB is responsible for Radio resource

Management (RRM) – assignment, reassignment and release of

radio resources. It is used in the signaling of Access stratum

signaling protocols, along with scheduling and transmission of

paging messages received from the MME and broadcast information

received from the Mobility Management Entity (MME). Other

functions such as measurement gathering for scheduling,

mobility decisions and routing the user plane date to Serving

Gateway (SGW) are also taken care by the eNodeB, The User

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Equipment is mainly responsible for 3 functions: 1)

To mark its entry into the signal network and several other

changes, 2) to report its location while it is in the idle

mode, and 3) requesting uplink grants to transmit data while in

active mode. Apart from these functions several other

measurements such as Reference Symbol Received Power (RSRP),

Reference Symbol Received Quality (RSRQ), Received Signal

Strength Indicator (RSSI), and Signal to Noise Ratio (SNR) are

also carried out by the UE.

Mobile Management Entity (MME) helps authenticate UEs into the

system, tracks active and idle UEs and pages UEs when triggered

by the arrival of the new data. When a UE attaches to an eNB,

the eNB selects an MME. MME in turn selects the Serving Gateway

(SGW) and the Packet Data Network Gateway (P-GW) that will

handle the user’s bearer packets. MME also takes care of the

Non-Access stratum signaling and authentication (in conjunction

with the Home Subscriber Server - HSS). Serving Gateway (SGW)

routes and forwards user data packet, terminates downlink data

for idle UEs and is also the local mobility anchor for inter-

eNodeB handover. The mobility anchor function applies to both

UE in the E-UTRAN and other 2G/3G technologies. The S-GW also

maintains a buffer for each idle UE and holds the packets until

the UE is paged and an RF channel is re-established. For each

UE associates with the Evolved Packet Core (EPC), at a given

point of time there is a single UE. Other functions of the S-GW

include IP backhaul admission and congestion control, point of

policy enforcement and IP backhaul Quality of Service (QoS).

Packet Gateway (P-GW) is responsible for the UE Internet

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Protocol (IP) address assignment and provides connectivity to

the external packet data networks. The P-GW provides charging

(billing) support, packet filtering/screening, policy

enforcement and lawful intercept. If a UE is accessing multiple

packet data networks, it may have connectivity to more than 1

P-GW. Home Subscriber Server (HSS) is the master database that

contains the UE profiles and authentication data used by the

MME for authenticating and authorizing UEs. It also stores the

location information of the UE which is used for user mobility

and inter-technology handovers. The HSS communicates with the

MME using Diameter protocol. Policy and Charging Rules Function

(PCRF) creates rules for setting policy and charging rules for

the UE. It provides network control for service data flow

detection, gating, QoS authorization and flow based charging.

It applies security procedures, as required by the operator,

before accepting service information. Decides how a certain

service data flow will be treated in the P-GW and ensures that

the P-GW user plane traffic mapping and treatment matches the

user’s subscription profile. Serving GPRS (General Packet Radio

System) Support Node (SGSN) is responsible for the delivery of

data packets to and from UEs within its geographical service

area. The SGSN provides the interfaces between the MME and S-GW

in the Evolved Packet Core (EPC) [2]. The technologies used to

implement all the components of LTE are:

Orthogonal Frequency Division Modulation (OFDM): OFDM

technology has been incorporated into LTE because it enables

high data bandwidths to be transmitted efficiently while

still providing a high degree of resilience to reflections

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and interference. The access schemes differ between the

uplink and downlink: OFDMA (Orthogonal Frequency Division

Multiple Access) is used in the downlink; whereas, SC-FDMA

(Single Carrier - Frequency Division Multiple Access) is used

in the uplink, as shown in Figure 2. SC-FDMA is used in view

of the fact that its peak-to-average power ratio (PAPR) is

small and the more constant power enables high RF power

amplifier efficiency in the mobile handsets - an important

factor for battery powered equipment.

Figure 2: OFDMA and SC-FDMA in LTE [3].

Multiple Input Multiple Output (MIMO): One of the main

problems that previous telecommunications systems have

encountered is that of multiple signals arising from the many

reflections that they encountered. By using MIMO (Figure 3),

these additional signal paths can be used to advantage and to

increase the throughput. When using MIMO, it is necessary to

use multiple antennas to enable the different paths to be

distinguished. Accordingly schemes using 2 x 2, 4 x 2, or 4 x

4 antenna matrices can be used. While it is relatively easy

to add further antennas to a base station, the same is not

true of mobile handsets; where, the dimensions of the user

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equipment limit the number of antennas can be placed at least

half a wavelength apart.

Figure 3: MIMO in LTE [3].

System Architecture Evolution (SAE): With the very high

data rate and low latency requirements for 3G LTE, it is

necessary to evolve the system architecture to enable the

improved performance to be achieved. The new SAE network

(Figure 4) is based upon the GSM/WCDMA core networks to

enable simplified operations and easy deployment, as shown in

Figure 4. Despite this, the SAE network brings in some major

changes, and allows far more efficient and effect transfer of

data.

Figure 4: LTE /SAE architecture [2].

2. LTE Evolution

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Although there are major step changes between LTE and its 3G

predecessors, it is nevertheless looked upon as evolution of

the UMTS/3GPP 3G standards as shown in the Table 1. Although

LTE uses a different form of radio interface using OFDMA/SC-

FDMA instead of CDMA; yet there are many similarities with the

earlier forms of 3G architecture and there is scope for much

re-use. LTE can, therefore, be seen to provide a further

evolution of functionality, increased speeds and general

improved performance.

Table 1: Comparison of parameters of UMTS, HSPA, HSPA+ and LTE

WCDMA(UMTS)

HSPAHSDPA/HSUPA

HSPA+ LTE

Max downlink speed

384Kbps 14Mbps 28Mbps 100Mbps

Max uplink speed

128Kbps 5.7Mbps 11Mbps 50Mbps

Latency round trip time

150ms 100ms 50ms(max)

~10ms

3GPP releases Rel99/4

Rel 5/6 Rel 7 Rel 8/10

Approx years of initial roll out

2003/04 2005/06(HSDPA)2007/08(HSUPA)

2008/09 2009/10

Access technology

CDMA CDMA CDMA OFDMA/SC-FDMA

In addition to this, LTE is an all IP based network, supporting

both IPv4 and IPv6. There is also no basic provision for voice;

although, this can be carried as VoIP. The key measure of LTE

is the ability to provide very high bit rates. In addition, it

provides high spectral efficiency, very low latency and support

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of variable bandwidth. The detailed specification of LTE is

given in Table 2.

Table 2: Specification of LTE [1-3].

Specification Details Peak downlinkspeed 64QAM(Mbps)

100 (SISO), 172 (2x2 MIMO), 326 (4x4 MIMO)

Peak uplink speed(Mbps)

50 (QPSK), 57 (16QAM), 86 (64QAM)

Data type All packet switched data (voice and data).No circuit switched.

Channelbandwidths

1.4, 3, 5, 10, 15 and 20 MHz

Duplex schemes FDD and TDDLatency Idle to active less than 100 ms

Small packets ~10 msSpectralEfficiency

Downlink: 3 to 4 x HSDPA Rel. 6Uplink: 2 to 3 x HSUPA Rel. 6

Supported antennaconfigurations

Downlink: 4x2, 2x2, 1x2, 1x1Uplink: 1x2, 1x1

Access schemes OFDMA (downlink)SC-FDMA (uplink)

Modulation typessupported

QPSK, 16QAM, 64QAM (Uplink and downlink)

Coverage Full performance up to 5 Km, Slight degradation (5 – 30) Km

3. Introduction to LTE-Advanced LTE-Advanced extends the features of LTE in order to exceed or

at least meet the IMT-Advanced requirements. It should be a

real broadband wireless network that behaves as an advanced

fixed network like FTTH (Fiber-To-The-Home) but with better

quality of service. It also must fulfill operator’s demands

like a reduced cost per Mbit transmitted, compatibility with

all 3GPP previous systems and a better service providing in

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terms of homogeneity, constant quality of the connection and

smaller latency. The key goals of LTE-Advanced are:

Support of asymmetrical bandwidths and larger bandwidth

(maximum of 100MHz): In LTE (release 8), the bandwidth could

have different sizes but had to be the same in the downlink

and in the uplink. In LTE-Advanced (Release 10) bandwidths

can be different because due to actual demand in mobile

networks, the traffic from the station to the user is bigger

than the one from the user to the station. And they can be as

asymmetric as they want within the limit of the 100 MHz LTE-

Advanced provides. The sum of both bandwidths (downlink +

uplink) cannot exceed 100 MHz. Carrier aggregation to achieve

wider bandwidth is a key factor as well as the support of

spectrum aggregation, to achieve higher bandwidth

transmissions.

Enhanced multi-antenna transmission techniques: LTE

introduced MIMO in the data transmission. However in LTE-

Advanced, the MIMO scheme has to be extended to gain spectrum

efficiency (which is proportional to the number of antennas

used), cell edge performance and average data rates. LTE-

Advanced considers a configuration 8x8 in the downlink and

4x4 in the uplink. LTE-Advanced tries to get the network

closer to the user to provide a uniform user experience and

to increase the capacity of the network. For that purpose, it

uses advanced topology networks. Advanced topology networks

provide the benefits and the performance increase. Some of

the characteristics of this type of networks are [1]:

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They are self-organizing networks; this will minimize the

number of Drive Tests for example.

Intelligent Node Association

Support for relays

Adaptive Resource Allocation

Multicarrier (spectrum aggregation)

Coordinated Beamforming

LTE-Advanced is intended to support further evolution of LTE

and to establish EUTRAN as an IMT-Advanced technology. LTE-A

also known as LTE release 10 is set to provide higher bitrates

in a cost efficient way and at the same time also focus on

higher capacity, i.e.:

Increased peak data rate DL 3Gbps, UL 1.5Gbps

Increased number of simultaneously active subscribers

Improved performance and higher spectral efficiency

Worldwide functionality and roaming

Compatibility of services

Inter working with other radio access systems

To achieve these goals, several enhancements are being

considered. At the physical layer, LTE is expected to provide

substantial improvement in peak, average and cell-edge spectral

efficiencies, under the assumption of 8x8 antenna configuration

in the downlink and 4x4 in the uplink. Under the same

assumptions, peak spectral efficiency of 30 and 15bps/Hz should

be met for the downlink and uplink respectively. Some of the

physical layer enhancement techniques are Carrier Aggregation,

Co-ordinated Multipoints, Relays, uplink and downlink spatial

multiplexing up to 4 and 8 transmit antennae respectively.

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Small cells such as picocells and femtocells bring the network

closer to users and provide a big leap in performance[5]. But

LTE-Advanced optimizes small cell performance through features

such as ‘Range Expansion’ to make the leap even more

significant. Simply adding small cells to a network only

benefits users close to the cell, but LTE-A enhances the users

experience for all the users including those on the cell edge

with higher data rates, even when the small cells are not

positioned in optimal locations. Additionally, the advanced

receivers allow devices to discover small cells early and

further increase performance of range expansion. To achieve the

requirements set by LTE-A, support for wider transmission

bandwidths, other than the 20Mhz bandwidth set aside for LTE

specified in 3GPP release 8/9, is required. The preferred

solution to this is Carrier Aggregation (CA). It is one of the

most distinct features of LTE-A. CA allows the expansion of

effective bandwidth delivered to a user terminal through

concurrent utilization of radio resources across multiple

carriers. Multiple component carriers are then aggregated to

form a larger transmission bandwidth. LTE-A can aggregate up to

5 carriers (up to 100MHz) to increase user data rates and

capacity for busty applications [7]. The Aggregation(s) when

combined with higher order MIMO can provide extremely high data

rates, as shown in Figure 5.

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Figure 5: MIMO scheme

(8x4) [2].

4. New features of LTE-Advanced: from Release 8 to

Release 10 As discussed above, LTE is the result of the standardization

work done by the 3GPP to achieve a new high speed radio access

in the mobile communications frame. It was introduced in the

sequence of Release 8 in 2008, the Release 9 in 2010 and

Release 10 in 2011 to meet the requirements of the IMT-Advanced

of ITU-R for the fourth generation of mobile technologies (4G),

and the future operator and end user’s requirements. The key

measure of LTE is the ability to provide very high bit rates.

In addition, it provides high spectral efficiency, very low

latency and support of variable bandwidth. Some of the LTE new

features are:

Orthogonal Frequency Division Multiple Access: OFDMA, a

multi-user version of the modulation scheme called Orthogonal

Frequency-Division Multiplexing, in the downlink. This gives

robustness against multipath interference and connects with

some advanced techniques also used like MIMO (shown in Figure

5) and frequency domain channel dependent scheduling.

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Single Carrier Frequency Division Multiple Access with

Dynamic Bandwidth in the uplink: SC-FDMA has lower peak-to-

average power ratio (PAPR) which is a major improvement for

the user equipment (UE), as it improves the transmission

power efficiency.

Multiple Input Multiple Output antenna solutions: MIMO is

used to improve the data bit rates and spectral efficiency.

It consists of using multiple antennas in both the receiver

and transmitter in order to use the multipath effects, which

reduces the interference and leads to high transmission

rates. MIMO works by dividing the data flow into multiple

unique flows, and transmits them in the same radio channel at

the same time[12].

Coordinated multipoint transmission and reception (CoMP): It

is used to improve the received signal of the user terminal.

Both the serving and neighbor cells are used in a way that

the co-channel interference from neighboring cells is

reduced. It implies dynamic coordination between

geographically separated transmission points in the downlink

and reception at separated points in the uplink. This

mechanism is used to improve the coverage of high data rates

and to increase the system bit rate[15].

Relaying: Relaying increases the coverage area and capacity

of the network. User’s mobile devices communicate with the

relay node, which communicates with a donor eNB (enhanced

Node B). Relay nodes can also support higher layer

functionality like decoding user data from the donor eNB and

re-encoding the data before transmitting it to the user

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terminal. There are two types of relay nodes as shown in

Figure 6 below. Type 1 relay nodes control their cells with

their own cell identity, and are used for the purpose of the

transmission of synchronization channels and reference

symbols. This type of relays guarantee compatibility with

previous releases as it appeared in Release 8 to provide

service to Release 8 mobile terminals. However, Type 2 relay

(Figure 6) nodes don’t own an identity, so the mobile user

won’t be able to distinguish if a transmission comes from the

donor eNB or from the relay. eNBs transmit control data and

relays transmit user data.

(a) Type 1 relays (b) Type 2 relaysFigure 6: Relay types in LTE systems [4].

5. Difference between LTE and LTE-Advanced LTE and LTE Advanced are high speed 4G wireless technologies.

These 4G technologies give virtually, LAN reality to mobile

handsets and to feel the real experience of the triple play

services such as voice, video and high speed data. Both LTE and

LTE Advanced offer high speed access to Internet equivalent to

FE connection. The key differences between LTE and LTE-Advanced

are mentioned below and in Table 3.

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LTE: 3GPP has completed the specification for Long Term

Evolution as part of Release 8. The work on LTE began in 2004

and completed in 2009 and first deployment occurred in 2010.

LTE is supposed to offer 326 Mbps with 4×4 MIMO and 172 Mbps

with 2×2 MIMO in 20 MHz spectrum. LTE supports both FDD

(Frequency Division Duplexing) and TDD (Time Division

Multiplexing). The major advantage in LTE is high throughput

with low latency. In reality LTE is offering 120 Mbps and the

speed depends on the user’s closeness to the tower and the

number of users in a particular cell area[5].

LTE Advanced: After the success of LTE, 3GPP is recently

addressing to satisfy the specification of IMT Advanced

(International Mobile Telecommunications-Advanced) set for

LTE Advanced. The major advantage of LTE Advanced is its

backward compatibility meaning that, LTE devices can work in

LTE Advanced and LTE Advanced devices can operate in LTE as

well. LTE Advanced standards are defined in 3GPP release 10.

LTE Advanced expected to offer download peak rate of 1000

Mbps in a low mobility scenario and 100 Mbps in a high

mobility. Low mobility is defined as pedestrian speed (10

Km/hr) and high mobility as 350 Km/hr. LTE Advanced offers

speed of 40 times faster than 3G commercial networks. LTE

Advanced offers All-IP, High Speed, Low Latency and high

quality spectrum efficiency in mobile network which enhances

the experience of mobile triple play services. The key

differences between LTE and LTE Advanced are mentioned in

Table 3 below.

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Table 3: Difference between specifications of LTE and LTE-

Advanced.

Specification LTE LTE-APeak Data Rate DownLink

150 Mbps 1 Gbps

Peak Data Rate Up Link

75 Mbps 500Mbps

Transmission Band (DL)

20MHz 100MHz

Transmission Band (UL)

20MHz 40MHz

Scalable Bandwidths 1.3,3,5,10, and 20MHz

Up to 20-100MHz

Capacity 200 active usersper cell in 5MHz

3 times higherthan LTE

6. Key challenges in implementing LTE and LTE-AdvancedThe main design factors that should be taken into account

before defining an allocation policy for LTE are:

A. Key design aspects

1) Complexity and scalability: Ideally, A LTE packet scheduler

works with a time granularity of 1ms; i.e., it has to take

allocation decisions every TTI. Therefore, low complexity and

scalability are fundamental requirements for limiting

processing time and memory usage. Finding the best allocation

decision through complex and non-linear optimization problems

or through an exhaustive research over all the possible

combinations would be too expensive in terms of computational

cost and time [14]. For this reason, FDPS decisions are usually

based on the computation of per-RB metrics for each user. In

this manner, complexity reduction is achieved because each RB

is allocated to the user with the highest metric independently

25


of other RBs. Let N and R be the number of active users in the

current TTI and the number of available RBs, respectively; the

scheduler has to calculate M = N.R metrics every TTI. This

assures the scalability requirement thanks to the linear

dependence on the number of resource blocks and users.

2) Spectral efficiency: Effective utilization of radio

resources is one of the main goals to be achieved. To this aim,

several types of performance indicators can be considered: for

instance, a specific policy could aim at maximizing the number

of users served in a given time interval or, more commonly, the

spectral efficiency (expressed in bit/s/Hz) by always serving

users that are experiencing the best channel conditions. One of

the most used efficiency indicators is the user good-put, a

measure of the actual transmission data rate without including

layer two overheads and packet retransmissions due to physical

errors.

3) Fairness: A blind maximization of the overall cell throughput

surely enables effective channel utilization in terms of

spectral efficiency, but also brings to very unfair resource

sharing among users. Fairness is therefore a major requirement

that should be taken into account to guarantee minimum

performance also to the cell-edge users (or in general to users

experiencing bad channel conditions).

4) QoS Provisioning: As well-known, QoS provisioning is very

important in next generation mobile networks. It is a major

feature in all-IP architectures. LTE maps QoS constrained flows

to dedicated radio bearers that, depending on their QCIs,

enable special RRM procedures. QoS constraints may vary

26


depending on the application and they are usually mapped into

some parameters: minimum guaranteed bitrate, maximum delivering

delay, and packet loss rate. Thus, it is important to define

QoS-aware schedulers.

B. Practical limitations in real LTE systems

Several aspects of LTE deployment in real environment may

impact on the choice of the best allocation schemes to be

adopted. These are:

1) Uplink limitations: In downlink, due to the use of OFDMA, the

scheduler can fill out the RB allocation mask without ordering

constraints. Instead, SC-FDMA method, used for uplink

transmissions, allows the UEs to transmit only in a single

carrier mode. Therefore, the scheduler for the uplink has

limited degrees of freedom: it has to allocate contiguous RBs

to each user without the possibility of choice among the best

available ones[9].

2) Control Overhead: The PDCCH, which carries DCI messages, is

time-multiplexed with the data-channel occupying a variable

number of OFDM symbols (up to 3). As a consequence, the amount

of resources dedicated to the PDCCH is limited, thus decreasing

the degrees of freedom for the downlink scheduler. PDCCH

overhead can be reduced using specific RRM procedures, either

by lowering a priori the number of users to be served (and

hence the number of DCI messages) or by utilizing low bit rate

DCI formats. The latter solution poses some constraints on the

construction of the RB allocation mask (for example allowing

the assignment of only contiguous RBs to the same UE.

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3) Limitations on the Multi-User Diversity Gain: An important

limitation for LTE resource sharing algorithms is to derive

from the availability, at the eNB, of accurate channel quality

measurements. For the downlink, UEs are in charge of sending

CQIs to the serving eNB. For the uplink, instead, the eNB may

use reference signals transmitted by the UEs to estimate uplink

channel quality. The chosen CQI reporting scheme has great

impact on the multi-user diversity gain, as it defines the time

and frequency resolution of the channel quality information

available at the scheduler. This consideration can be easily

confirmed observing, as representative example that in the case

of wideband CQI reporting scheme (i.e., a single CQI value is

transmitted for the entire spectrum) the sensitivity to channel

conditions would become totally useless and the multi-user

diversity gain would go to zero. Thus, it appears clear how, in

such situations, the adoption of sophisticated techniques would

not bring to any gain. Furthermore, efficiency is limited

because multi-user diversity gain results to be upper bounded;

that is, increasing indefinitely the number of users competing

for allocation does not bring to any gain over a certain

threshold [14]. Moreover, it actually exists an optimal number

of candidate users depending on the system bandwidth.

4) Energy consumption: Energy saving is a required feature for

mobile terminals, and in LTE it is achieved through

Discontinuous Reception (DRX) methods. The basic idea of DRX is

to allow an UE to save energy turning off its radio equipment

when there are no data transmissions [21], as in typical

alternation of on/off periods (DRX cycles). In LTE, moreover,

28


DRX can be enabled in presence of active flows as well,

allowing off periods between packet burst transmissions. Such

operations require the knowledge of the activation of DRX

cycles. In principle, if the network could predict when a

certain user needs to transmit/receive data, an UE would only

need to be awake at such specific time instants. On the other

hand, in absence of a precise coupling between scheduling and

DRX procedures, it may occur that resources are allocated to an

UE during its off periods with consequent loss of data and

waste of bandwidth.

C. Persistent and semi-persistent scheduling

In LTE, the dynamic frequency domain strategies have the main

benefit of exploiting multiuser diversity gain, but this comes

at the cost of increased control overhead, due to the need of

forwarding DCI messages to scheduled users every TTI. For this

reason, especially in scenarios with high traffic load, the

limited amount of radio resources dedicated to control

information transmission can become a bottleneck, with

consequent degradation of QoS provisioning capabilities [23].

To face this problem, persistent solutions have been

investigated [24]. In these approaches, the control overhead is

used to preallocate to the same UE certain RBs over a time

sequence, distinguishing between active and inactive periods.

Under this scheme, once the eNB has informed a user on the

persistent allocation, the interested UE will know in advance

the specific TTI/RB couples where it should decode PDSCH

payloads (or, for the uplink direction, it should transmit

PUSCH payloads) with no need of any additional PDCCH overhead.

29


In Figure 7, a graphical example of a persistent resource

allocation is given. Here for instance, user 1 has resources

allocated every three TTIs and user 2 every five TTIs, always

on the same sub-carriers. Besides the impossibility to exploit

channel sensitivity, a major drawback of persistent approach is

that it does not appear suitable for supporting HARQ. In

practice, a single packet needs on average more than a single

transmission to be correctly decoded at the receiver, and the

number of retransmissions varies for each user, depending on

many factors such as the channel quality, the user mobility,

and the perceived inter-cell interference[30]. For these

reasons, semi-persistent algorithms are being deeply

investigated as a trade-off among static persistent approaches

and fully dynamic ones, especially if conceived for VoIP

traffic impossibility to exploit channel sensitivity.

Figure 7: Example of persistent scheduling allocation [2].

7. Cell selection: Cell selection by a mobile UE is anotherissue in LTE. In particularly, an interesting challenge in the

physical layer of LTE is how the mobile unit immediately after

powering on, locates a radio cell and locks on to it. More

30


specifically, it is very challenging to know how the mobile

unit establishes this connection with the strongest cell

station in vicinity. To do this, the mobile unit has to

overcome the challenges of estimating the channel to

communicate with the cell site and frequency

synchronization[1]. Also, multiple mobile units communicate to

the same receiver and from various distances. Thus, it is up to

the mobile to synchronize itself appropriately to the base

stations. LTE uses two signals, the Primary Synchronization

Signal and the Secondary Synchronization Signal sequentially to

determine which of the available cell sites a mobile would lock

in to. In addition if the inter cell interference is present

and if its level is high, then it will be very difficult for

the mobile UE to make a selection of the cell. The major cause

for inter cell interference is adjacent channel interference

(ACI) [5]. Adjacent-channel interference (ACI) is caused by

imprecise synchronization of multiple users accessing the same

Orthogonal Frequency Division Multiplexing (OFDM) channel.

Small frequency offsets between multiple users breaks the

orthogonality of their spectra due to the small spectral shifts

of the user’s sin(x)/(x) spectra relative to their design

locations at the zeros of the other user’s sin(x)/x. The system

multiple access procedure is designed to minimize these offsets

but there will be a small residual offset that can only be

reduced by interference cancellation techniques. Among all the

challenges mentioned above, the author of this synopsis is very

much interested in the area of cell selection. There are

different types of cells depending upon their area of usage and

31


coverage. The key cell types are Microcell, Macrocell,

Femtocell Picocell and Satellite (also known world wide

coverage). An overview of different types of cells is

presented in Figure 8.

Figure 8: Different types of cells in a network [7].

8. Key literature survey1. Aimin et.al. [26] in 2006 investigated a wireless system of

multiple cells, each having a downlink shared channel in

support of high-speed packet data services. Authors mentioned

that in practice, such a system consists of hierarchically

organized entities including a central server, Base Stations

(BSs), and Mobile Stations (MSs). Authors in this study tried

to improve global resource utilization and reduce regional

congestion given asymmetric arrivals and departures of mobile

users, a goal requiring load balancing among multiple cells.

For this purpose, authors proposed a scalable cross-layer

framework to coordinate packet-level scheduling, call-level

cell-site selection and handoff, and system-level cell

32


coverage based on load, throughput, and channel measurements.

In this framework, an opportunistic scheduling algorithm—the

weighted Alpha-Rule—exploited the gain of multiuser diversity

in each cell independently, trading aggregate (mean) downlink

throughput for fairness and minimum rate guarantees among

MSs. Authors specific contributions in such a framework are

highlighted by the minimum-rate guaranteed weighted Alpha-

Rule scheduling, the load-aware MS handoff/cell-site

selection, and the Media Access Control (MAC)-layer cell

breathing. Author’s evaluations showed that their framework

can improve global resource utilization and load balancing,

resulting in a smaller blocking rate of MS arrivals without

extra resources while the aggregate throughput remains

roughly the same or improved at the hot-spots.

2. Supratim et al. [27] in 2013 mentioned that the success of

LTE Heterogeneous Networks (Het-Nets) with macro cells and

pico-cells critically depends on efficient spectrum sharing

between high-power macros and low power picos. Authors

mentioned that the two important challenges in this context

are: (i) determining the amount of radio resources that macro

cells should offer to pico-cells, and (ii) determining the

association rules that decide which UEs should associate with

picos. In this research, authors developed an algorithm to

solve these two coupled problems in a joint manner. Author’s

algorithm accounted for network topology, traffic load, and

macro-pico interference map. Authors also presented results

for evaluations using RF plan from a real network and discuss

SON based eICIC implementation.

33


3. Sanil F. [28] in 2011 mentioned that LTE is designed with a

goal of evolving the radio access technology under the

assumption that all services would be packet-switched, rather

than following the circuit-switched model of earlier systems.

This research mainly emphasizes on Physical layer and a proof

for the physical layer message sequence. The main purpose of

this research is to study, analyze and implement a proof of

Physical layer process in control plane of LTE system. The

focus is on physical layer between the UE and eNB. Authors

performed the test trials and screenshots for the analysis of

call drops were taken and included. Author’s results showed

the achievable Downlink and Uplink throughput in multi user

and single user environment compared with the channel

capacity of 10MHz channel. The research is mainly based on

3GPP LTE specifications and discussions.

4. Berardinelli Gilberto [29] in 2010 deals with the air

interface design for the uplink of 4th generation systems

based on SC-FDM technology. The interaction of MIMO with

SC-FDM is investigated in terms of link performance as well

as its effects on the transmit waveforms. Author included the

enhancement mechanisms such as fast link adaptation and

Hybrid Automatic Repeat Request (HARQ). Author presented a

transmit diversity space frequency coding (SFC) algorithm;

where the encoding is performed over adjacent subcarriers is

shown to compromise the single carrier nature of the uplink

signals. Space Time Coding (STC) has therefore to be

preferred for a SC-FDM radio interface, despite of a lower

flexibility. An SFC scheme which does not increase the PAPR

34


of the transmit waveform is also derived. Authors performance

results obtained with Monte Carlo simulations show its

effectiveness for small transport data blocks. The

investigations carried out in this rersearch confirm SC-FDM

as a valid air interface for the uplink of LTE-A; with a

careful design of the transceiver chain, it can achieve

indeed the same link performance of OFDM, while keeping the

remarkable advantage of a higher power amplifier efficiency.

5. Jaewon et. al. [30] in 2013 presented an operational strategy

to mitigate co-channel interference (CCI) by using

geographically distributed remote radio heads (RRHs). Author

mentioned that the inter-node CCI becomes a dominant

performance degradation factor for heterogeneous network

(HetNet) systems. According to authors, there are emerging

attempts in Third Generation Partnership Project to adopt

advanced techniques to Long Term Evolution Advanced systems

to mitigate CCI problems for HetNet systems, namely, the

coordinated multipoint transmission (CoMP). However, the CoMP

scheme cannot control the CCI generated from outside

coordination boundaries. To resolve this problem, authors

proposed a partial activation strategy by using RRHs deployed

near cell edge which results in moving coverage boundary

effects. Based on Monte Carlo system level simulations,

performance of the conventional strategies and the presented

strategy is evaluated by authors. Their simulation results

showed that their scheme outperforms the enhanced inter-cell

interference coordination and CoMP schemes especially for

users located near cell edge areas.

35


6. Jakub et. al. [31] in 2009 mentioned that the objectives in

ongoing research related to mobile positioning are

inexpensive solutions providing high accuracy of position

estimation. Authors mentioned that Cell ID+RTT is identified

as one of the most available and applicable location

technique for UMTS. According to authors, this technique

provides high degree of accuracy, but only for users within

SHO. Therefore, authors in this study aimed to present an

algorithm for increasing SHO window for the time instant

needed for necessary RTT measurements. Author’s simulation

outcomes showed that application of their algorithm increases

the availability of accurate Cell ID+RTT estimates (16–20 m

in free propagation environment) for nearly all served users

in the network, simultaneously improving overall accuracy to

40 m for 90% of measurements.

7. Jung-Min et. al. [32] in 2010 mentioned that in a

hierarchical cellular network employing universal frequency

reuse, the level of both intra- and inter-cell interference

largely depends on the selection of a serving cell for the

users in the overlapping area of multiple cells. Therefore,

authors proposed a cell selection algorithm that is suitable

for hierarchical cellular networks. In their algorithm,

uplink transmit power is used as a key parameter and cells

are selected on the basis of the coordination of multiple

users, rather than the choice of a single user. Authors

simulation results showed that their algorithm improves

performance with respect to the number of supportable users

36


and the transmit power that each user needs in order to

achieve a given target SINR.

8. Dong et. al. [33] in 2012 mentioned that handover failure

probability is one of the important factors to determine the

handover performance in cellular radio system such as the 3rd

generation partnership project (3GPP) long term evolution. To

minimize the handover failure probability, the Hybird target

cell selection (TCS) scheme considering both the received

signal strength (RSS) and load information of neighboring

evolved Node Bs (eNBs) based on X2 interface in 3GPP LTE

system has been introduced in this research. Authors

mentioned that the amount of cell load in serving and

neighboring eNBs can be drastically changed over time due to

the handover operation, so the Hybrid TCS scheme should

consider the cell load change between the serving and

neighboring eNBs in the handover procedure for handover

preparation. In this research, authors proposed a modfied

Hybrid TCS scheme based on the least mean square (LMS)

algorithm for estimating the load status of the neighboring

eNBs in order to mitigate the handover failure probability.

Authors mentioned TCS scheme chooses the target eNB with

minimum load based on the LMS algorithm and providing higher

RSS. Their experiment results reveal the effectiveness of

their scheme and its advantages over the conventional schemes

in terms of handover failure probability.

9. Anna et. al. [34] in 2013 mentioned that wireless

communication network consist nowadays of multiple standards,

as well as cells of different sizes and coverage. According

37


to authors, providing the best connection in such environment

is a challenging task. Therefore, authors proposed an

approach of solving the cell selection problem in

heterogeneous networks. The method recursively applies

efficient algorithms for bipartite graph matching to provide

the final solution. This method does the cell selection in

multistandard heterogeneous networks. The idea is based on a

recursive use of algorithms dedicated to find matchings in

bipartite graphs. A platform for evaluation purposes has

already been implemented and evaluated.

10. Ömer et. al. [35] in 2010 mentioned that relaying is a

promising enhancement to current radio access networks. Relay

enhanced LTE-Advanced networks are expected to fulfill the

demanding coverage and capacity requirements in a cost-

efficient way. However, due to low transmit power, the

coverage areas of the relay nodes will be small. Therefore,

the performance of relay deployments may be limited by load

imbalances. In this study, authors presented a practical

solution for this problem by introducing a bias to cell

selection and handover decisions along with a reduction in

eNB transmit power. According to authors, this method

resulted in an extension of the relay cells and an

appropriate load balance can then be achieved. Moreover, it

is shown that a proper power control setting is necessary in

the uplink and that power control optimization can further

enhance the system performance. Author’s comprehensive system

level simulations confirm that their solution yields

38


significant user throughput gains both in the uplink and the

downlink.

11. Li Huang et. al. [36] in 2013 mentioned that femtocells

have emerged as a promising solution to provide wireless

broadband access coverage in cellular dead zones and indoor

environments. However, co-channel deployments of closed

subscriber group femtocells cause coverage holes in

macrocells due to co-channel interference. To address this

problem, cognitive radio technology has been integrated with

femtocells, in this research. CR-enabled femtocells can

actively sense their environment and exploit the network side

information obtained from sensing to adaptively mitigate

interference. Authors investigated three CR-enabled

interference mitigation techniques, including opportunistic

interference avoidance, interference cancellation, and

interference alignment. In this research, authors presented a

joint opportunistic interference avoidance scheme with Gale-

Shapley spectrum sharing (GSOIA) based on the interweave

paradigm to mitigate both tier interferences in macro/femto

heterogeneous networks. In this scheme, cognitive femtocells

opportunistically communicate over available spectrum with

minimal interference to macrocells; different femtocells are

assigned orthogonal spectrum resources with a one-to-one

matching policy to avoid intratier interference. Author’s

simulations show considerable performance improvement of the

GSOIA scheme and validate the potential benefits of CR-

enabled femtocells for in-home coverage.

39


12. Kemal et. al. [37] in 2013 mentioned that heterogeneous

cellular networks provide significant improvements in terms

of increased data rates and cell coverage, and offer reduced

user rate starvation. In this study, authors identified that

the cell selection criterion is an important factor

determining the user rates especially in the uplink

transmissions and apply cell breathing to determine the user

and base station assignments. Authors observed that their

interference-based cell selection algorithm provide better

load balancing among the base stations in the system to

improve the uplink user rates. Authors presented the

implementation steps in a typical LTE network and

demonstrated the performance improvements through

simulations.

13. Steven et. al. [38] in 2012 discussed the problem of

associating users, in an heterogeneous network, to either a

macro node or a pico node within a tightly coordinated cell

cluster. Authors introduced a new theoretical framework to

model this problem for the downlink and derived upper bounds

for achievable sum rate and minimum rate using convex

optimization. Authors proposed a heuristics based approach,

consisting in dynamic cell association, enabling to achieve

performance close to the upper bounds. Authors implemented

these heuristics in an LTE simulator and showed the potential

of such dynamic cell association for a small LTE network.

14. Kyuho et. al. [39] in 2009 mentioned that next-generation

cellular networks will provide higher cell capacity by

adopting advanced physical layer techniques and broader

40


bandwidth. Authors also mentioned that even in such networks,

boundary users would suffer from low throughput due to severe

inter-cell interference and unbalanced user distributions

among cells, unless additional schemes to mitigate this

problem are employed. Therefore, authors presented a solution

to this problem by jointly optimizing partial frequency reuse

and load-balancing schemes in a multi-cell network. Authors

formulated this problem as a network-wide utility

maximization problem and proposed optimal offline and

practical online algorithms to solve this. Author’s online

algorithm turns out to be a simple mixture of inter- and

intra-cell handover mechanisms for existing users and user

association control and cell-site selection mechanisms for

newly arriving users. According to authors, a remarkable

feature of their algorithm is that it uses a notion of

expected throughput as the decision making metric, as opposed

to signal strength in conventional systems.

15. Huthaifa Al-Jaradat et. al. [40] in 2013 studies the

technical challenges associated with some of the RRM tasks

(including Packet scheduling, interference management and

handover control), in addition it presents from the open

literature some of the proposed solutions to these technical

challenges. According to authors, Packet scheduling faces

the challenge of supporting diverse QoS requirements of

different traffic types, while maintaining the overall system

throughput and fairness among users at an acceptable level.

Another challenge is the difficulty of scheduling due to the

adoption of CA and CoMP scheme. Authors mentioned that

41


interference in LTE-A raises due to the adoption of HetNet.

eICIC is the adopted method to relieve its effect which can

be grouped under three main techniques: time domain

technique, frequency domain technique and power control

technique. HHO is the only handover supported in the LTE and

LTE-A. HHO is fairly simple and less-complex compared to the

SHO, but HHO has several disadvantages such as high outage

probability, disruption time and carrier interference.

16. Wenan et. al. [41] in 2011 mentioned a modified scheduling

scheme of Round Robin (RR) algorithm. In this scheme, each

cell users are divided into two groups: cell-edge UEs and

cell-center UEs. Also, the available frequency bands are

divided into two groups: CoMP frequency and non-CoMP

frequency. The cell-edge UEs are equally scheduled by their

local eNB on PRBs of CoMP frequency zone. The local eNB

requests cooperation from neighboring eNBs on the relative

PRB. However, other eNBs may request cooperation on the same

PRB, which is scheduling conflict. In such case, eNBs

receiving the cooperation requests follow the rule “first

come first served”, that is the eNBs respond to the first

cooperation request and ignore other requests. Cell-center

UEs are equally scheduled in a circular order and are

allocated PRBs of single cell frequency band. And if some of

cell-edge UEs are not scheduled in the cooperation mode, they

will be scheduled as cell-center UEs at the end. Finally, the

remaining resources are allocated to cell-center UEs, as they

cannot be used for CoMP.

42


17. Jong et. al. [42] in 2013 proposed an intelligent cell

selection scheme for inter-system handovers in heterogeneous

networks that satisfies user requirements as well as system

requirements. Their scheme uses uncertain parameters such as

user system preference, communication cost, mobile speed,

transmission delay, packet loss, and cell loads in the

decision process utilizing the aggregation functions in fuzzy

set theory. Contrary to many existing schemes which consider

only either system parameters or user requirements, their

scheme considers both. This scheme hierarchically analyzes

the relative value of cell selection parameters based on the

current state of heterogeneous networks, the speed of mobile

terminals, and the change in QoS. Then, it focuses on the

adaptation of relative importance values for each cell

selection parameter. Computer simulation results performed by

authors showed that their scheme provides systems and users

with much better performance compared to existing schemes.

18. Anna et. al. [43] in 2013 proposed a novel optimization

model for resource assignment in heterogeneous wireless

network. The model adopts two objective functions maximizing

the number of served users and the minimum granted utility at

once. A distinctive feature of author’s model is to consider

two consecutive time slots, in order to include handover as

an additional decision dimension. Furthermore, the solution

algorithm that author’s proposed refines a heuristic solution

approach recently proposed in literature, by considering a

real joint optimization of the considered resources. Author’s

simulation study shows that the new model leads to a

43


significant reduction in handover frequency, when compared to

a traditional scheme based on maximum SNR.

19. Chung-Ju et. al. [44] in 2013 mentioned a preference

value-based cell selection (PVCS) scheme to satisfy quality

of service (QoS) requirements, maximize accommodated number

of calls, and minimize handoff occurrence frequency in

heterogeneous wireless networks. The PVCS scheme contains

three stages including candidate cells selection, preference

value calculation, and target cell determination. The

candidate cells selection is used to sift the suitable cells

for the call request. All suitable cells form a candidate

cell set. The preference value calculation computes the

preference value of each cell in the candidate cell set,

which is optimized by considering QoS factor, loading factor,

and mobility factor, for the purpose of maintaining QoS

requirements of a call request, maximize accommodated number

of calls, and minimizing handoff occurrence frequency.

Eventually, the candidate cell, which has the maximum

preference value, would be selected for the call request.

Simulation results performed by authors showed that PVCS

scheme achieve higher total throughput than UGT while

satisfying the QoS requirements

20. Lin Gao et. al. [45] in 2011 mentioned that cell selection

and resource allocation (CS-RA) are processes of determining

cell and radio resource which provide service to mobile

station (MS). Optimizing these processes is an important step

towards maximizing the utilization of current and future

networks. In this research, authors investigated the problem

44


of CS-RA in heterogeneous wireless networks. Specifically,

author’s proposed a distributed cell selection and resource

allocation mechanism, in which the CS-RA processes are

performed by MSs independently. Author’s formulated the

problem as a two-tier game named as inter-cell game and

intra-cell game, respectively. In the first tier, i.e. the

inter-cell game, MSs selected the best cell according to an

optimal cell selection strategy derived from the expected

payoff. In the second tier, i.e., the intra-cell game, MSs

choose the proper radio resource in the serving cell to

achieve maximum payoff. Furthermore, author’s proposed

distributed algorithms named as CS-Algorithm and RA-Algorithm

to enable the independent MSs converge to Nash equilibria.

Author’s simulation results showed that their algorithms

converge effectively to Nash equilibria and that their CS-RA

mechanism achieves better performance in terms of throughput

and payoff compared to conventional mechanisms.

9. MotivationCell selection by a mobile UE is a key issue in LTE. In

particularly, an interesting challenge in the physical layer

of LTE is how the mobile unit immediately after powering on,

locates a radio cell and locks on to it. More specifically,

it is very challenging to know how the mobile unit

establishes this connection with the strongest cell station

in vicinity. To do this, the mobile unit has to overcome the

challenges of estimating the channel to communicate with the

cell site and frequency synchronization. Also, multiple

45


mobile units communicate to the same receiver and from

various distances. Thus, it is up to the mobile to

synchronize itself appropriately to the base stations. LTE

uses two signals, the Primary Synchronization Signal and the

Secondary Synchronization Signal sequentially to determine

which of the available cell sites a mobile would lock in to.

In addition if the inter cell interference is present and if

its level is high, then it will be very difficult for the

mobile UE to make a selection of the cell. The major cause

for inter cell interference is adjacent channel interference

(ACI). Adjacent-channel interference is caused by imprecise

synchronization of multiple users accessing the same

Orthogonal Frequency Division Multiplexing (OFDM) channel.

Small frequency offsets between multiple users breaks the

orthogonality of their spectra due to the small spectral

shifts of the user’s sin(x)/(x) spectra relative to their

design locations at the zeros of the other user’s sin(x)/x.

This practical problem of LTE seems very interesting; which

motivated the author to perform research in this domain.

10. Formulation of problemFrom the literature review, the ability to provide services

in a cost-effective manner is one of the most important

building blocks of competitive modern cellular systems.

Usually, an operator would like to have a maximal utilization

of the installed equipment, that is, to maximize the number

of satisfied customers at any given point in time. The

research through this synopsis will address one of the

46


problems in this domain, the cell selection mechanism. This

mechanism determines the base station (or base stations) that

provide the service to a mobile station. More specifically,

the research through this synopsis will focus on joining of

the mobile station to the network (called cell selection),

and/or when a mobile station is on the move in idle mode

(called cell reselection, or cell change). This will also

include the effect of suppression of interference due to

other mobile stations. It is because in uplink multi-user

scenario, the interference is not only introduced by

incorrect symbol timing estimation and frequency offsets, but

also by the relative delays between users. Therefore, the

author is motivated to perform research in this domain.

11. Objectives1. To do the review of mobile cell selection in LTE-A

networks and its associated problems and challenges.

2. To implement LTE physical layer using OFDMA and SCFDMA in

Matlab.

3. To develop a hierarchical cell identity based strategy to

do mobile cell selection in single and multiple user

scenarios and also in cell interference region.

4. To develop a method based on fractional loading to improve

the cell selection performance (i.e. reduction of inter-cell

interference) in overlapping regions of irregular shaped

cells of LTE. To evaluate its performance compared to

traditional frequency reuse method.

12. Research methodology

47


Conventionally, cell selection is based on the downlink

received signal strength which means mobile users will

connect to the site from which the received power is

strongest. For example, in 3GPP LTE, cell selection is

performed according to two parameters measured by a User

Equipment (UE): Reference Signal Received Power (RSRP) and

Reference Signal Received Quality (RSRQ). Reference signal

received power (RSRP), is defined as the linear average over

the power contributions of the resource elements that carry

cell-specific reference signals within the considered

measurement frequency bandwidth. RSRQ is calculated based on

RSRP which provides additional information and ensures a

reliable cell selection decision when RSRP is not sufficient.

In homogeneous networks, RSRP based cell selection guarantees

good channel conditions in both downlink and uplink. However,

it is very difficult when a mobile UE powers on and/or when

the UE moves from one cell to another or when it is in the

overlapping region of two or more cells. To better understand

consider Figure 9, which shows one possible scenario in a

real network. Assume that the UE belongs to network operator

1 (green). There are two other carriers also operating an LTE

network but of course at different frequencies. The terminal

receives all base stations but at different power levels.

Based on the above definition the UE will select the strong

cell for each carrier . Using this the UE will start with

network operator 3 and figure out after decoding the SIB Type

1 that the PLMN saved on the USIM does not match to the

transmitted one. From this information it will stop with its

48


attempt and proceed to the next strongest signal, which is

operator 2 (red). Now the PLMN does not correspond so the UE

will continue with signal 3 (green) – and the PLMN will

match. This way the mobile UE will do the cell selection. The

research methodology to be used is mentioned below.

Figure 9: An example of a possible scenario in a real

network[16].

There are many types of cells :

1. Microcells : is a cell in a mobile phone network served by a

low power cellular base station (tower), covering a limited

area such as a mall, a hotel, or a transportation hub, The

range of it is few hundreds of meters. It is used in urban

areas[32].

2. Macrocells : is a cell in a mobile phone network that

provides radio coverage served by a high power cellular base

station (tower). The macrocells provide coverage larger

than microcell The antennae for macrocells are mounted on

49


ground-based masts, rooftops and other existing structures,

at a height that provides a clear view over the surrounding

buildings and terrain. It is used in the early phase of

cellular telephony[32].

3. Femtocells: is a small, low-power cellular base station ,

typically designed for use in a home or small business.

4. Picocells: is a small cellular base station typically

covering a small area, such as in-building (offices, shopping

malls, train stations, stock exchanges, etc.), or more

recently in-aircraft[32].

Table 4: Research methodology.

Step

1

Implement LTE Physical Layer using SC-FDMA and OFDMA in

MatlabStep

2

Develop and Implement an algorithm for Mobile UE LTE Cell

Joining Using Hierarchical cell Identity Step

3

Identify the challenges of Inter-Cell Interfaces of

Overlapping Cells regions and presence of multiple mobile

UE.Step

4

Develop an algorithm using the fractional loading concept

to improve the cell selection performance (i.e. reduction

of inter-cell interference) in overlapping regions of

irregular shaped cells of LTE.Step

5

Evaluate the performance of above algorithm compared to

traditional frequency reuse method.

13. Time schedule

50


The following is the schedule to be followed for the M.Tech

dissertation

Table

5: Time schedule

Synopsis Jan, 2014

Literature Review & Paper I March, 2014

Coding April, 2014

Paper II April, 2014

Dissertation May, 2014

14. References1) D. Amzallag, R. Yehuda, D. Raz and G. Scalosub, “Cell selection

in 4G cellular networks”, IEEE Transactions on mobile

computing, vol. 12, no. 7, pp.1443-1455, July 2013.

2) F. Capozzi, G. Piro, L. Griec, G. Boggia, and P. Camarda

“Downlink packet scheduling in LTE cellular networks: key

design issues and a survey” IEEE Communications Survey &

Tutorials, vol. 15, no. 2, pp.678-697, 2013.

3) R. Nag. Kummithe, “Interference mitigation in 4G LTE-A

heterogeneous networks”, Advances in Electrical Engineering

Systems, IEEE wireless communications and networking, vo. 13,

no. 5, Dec. 2012.

4) 3gpp, 3rd generation partnership project, “Technical

specification group radio access network”, Physical channels

and modulation (release 8), 3gpp ts 36.211.

5) S. Carlaw, A. Giustina, R. Bhat and S. Rao, “Femtocells -

opportunities and challenges for Business and Technology”,

51


John Wiley & Sons Ltd. ISBN: 978-0-470-74816-9, pp.170-175,

2009.

6) A. Roessler, S. Rohde and T. Schwarz, “Cell search and cell

selection in UMTS LTE”, IEEE wireless communications and

networking, Sep.2009.

7) 3gpp, “Itu library and archive services”, url

http://www.itu.int/en/history/overview/pages/history.aspx

cited on 19th Jan. 2014.

8) 3gpp, “Evolved universal terrestrial radio access (E-UTRA);

physical layer; measurements (Release 10)”, TS 36.214

v10.1.0, March 2011.

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MOBILE CELL SELECTION IN 4G LONG TERM EVOLUTION-ADVANCED (LTE-A) NETWORKS

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