Cross-layer radio resource management in integrated WWAN and WLAN networks

Computer Networks 54 (2010) 341–356

Contents lists available at ScienceDirect

Computer Networks

journal homepage: www.elsevier .com/ locate/comnet

Cross-layer radio resource management in integrated WWANand WLAN networks

Abdul Hasib a,*, Abraham O. Fapojuwo b

a Dept. of Elect. Engg. and Computer Science, North South University, Dhaka-1212, Bangladeshb Dept. of Elect. and Computer Engineering, The University of Calgary, Calgary, Alberta, Canada

a r t i c l e i n f o a b s t r a c t

Article history:Received 6 November 2008Received in revised form 23 May 2009Accepted 11 September 2009Available online 17 September 2009Responsible Editor: E. Gregori

Keywords:Heterogeneous wireless networkNetwork selectionCall admission controlPerformance analysis

1389-1286/$ - see front matter � 2009 Elsevier B.Vdoi:10.1016/j.comnet.2009.09.004

* Corresponding author. Tel.: +880 2 8055308; faE-mail addresses: [email protected] (A. Hasib)

ca (A.O. Fapojuwo).

This paper proposes a mobility adaptive network selection scheme in the context of wire-less wide area network (WWAN) and wireless local area network (WLAN) radio accesstechnologies (RATs) that supports both real-time (RT) and non-real-time (NRT) serviceclasses. Physical layer information based call admission control (CAC) is considered forthe two RATs to enforce service specific QoS requirements. The effectiveness of thecross-protocol-layer information for radio resource management (RRM) in integratedWWAN and WLAN networks is assessed analytically for individual service classes in amulti-service environment using the theory of Markov chains. The impact of non-uniformuser and mobility distributions due to the existence of hotspot in the macro-cell area andthe effect of network selection parameter measurement errors on the RRM performanceare also evaluated. Numerical results show that the proposed network selection schememinimizes the rate of unnecessary vertical handoffs, thereby providing stable communica-tion without degrading the call blocking probability and call outage probability perfor-mance metrics.

� 2009 Elsevier B.V. All rights reserved.

1. Introduction

An heterogeneous wireless network (HWN) is formedby integrating complementary radio access technologies(RATs) (e.g., wireless wide area network (WWAN), wirelesslocal area network (WLAN)). The characteristics of inte-grated RATs differ in terms of capacity, coverage area anddata transfer cost. WWANs (e.g., CDMA2000 [1]) supportwide (macro-cell tier) coverage but with low to mediumdata rate whereas WLANs (e.g., IEEE 802.11 [1]) have highdata rate but is effective only in local areas (micro-celltier). The advantage of HWN over the existing singleRAT based hierarchical networks [2] is that different tierRATs, forming an HWN, have no cross-tier interferenceand they can support the common service area with highercapacity at lower cost. On the other hand, a major chal-

. All rights reserved.

x: +880 2 8059223., fapojuwo@ucalgary.

lenge of HWN is how to efficiently utilize the resourcesof the integrated RATs while attaining the service QoS. Toaddress this challenge, this paper proposes and analyzesa cross-protocol-layer radio resource management (RRM)in a WWAN/WLAN inter-working context [3] that uses net-work layer information such as service type, cost, RAT loadand users’ mobility for network selection and employsphysical layer information (e.g., signal-to-interference ra-tio (SIR)) for call admission control (CAC). The issue of net-work selection in an HWN deals with joint management ofthe resources in the integrated RATs, referred to as com-mon radio resource management (CRRM) in the literature.

In the context of this paper, a network selection schemeincludes the functionalities of RAT selection for a new call(NRATS) and vertical handoff decision (VHD) for a roamingcall in a multi-RAT coverage area (referred to as hotspot).Several network selection schemes (e.g., [3–10]) for HWNshave already been proposed in the literature. Majority ofthese previous works fail to differentiate between mobilityinduced necessary and unnecessary vertical handoff

http://dx.doi.org/10.1016/j.comnet.2009.09.004

mailto:[email protected]

mailto:fapojuwo@ucalgary. ca

mailto:fapojuwo@ucalgary. ca

http://www.sciencedirect.com/science/journal/13891286

http://www.elsevier.com/locate/comnet

342 A. Hasib, A.O. Fapojuwo / Computer Networks 54 (2010) 341–356

(VHO). Note that, in WWAN/WLAN integrated network,unnecessary vertical handoffs can be defined as two con-secutive VHOs within a short duration for a roaming callin the hotspot or a very short WLAN session before movingout of the hotspot for a new call established in the hotspot.Unnecessary vertical handoff for high mobility users ham-pers service stability (i.e., varying data rate, frequent VHOlatency and data loss), and increases VHO processing cost.Few recent works on VHD algorithm consider cell resi-dence time [5], user speed [11,12], VHO switching cost[7], and user location [13] to confirm necessary VHOs only.This paper proposes a mobility based NRATS and VHDalgorithms for multi-mode terminals that incorporate userspeed and mobility prediction information to provide sta-ble and high data rate communication at minimum servicecost (i.e., the sum of data transfer cost and vertical handoffprocessing cost). SIR based CAC [14,15] for CDMA2000 andsaturation throughput based CAC [16] for IEEE802.11 areconsidered to ensure non-violation of service QoS objec-tives for call blocking probability, bit-error-rate (BER),and transmission delay.

Most of the previous works on network selection inHWNs present simulation based results of the QoS perfor-mance metrics such as new call or handoff call blockingprobability in the different RATs, throughput etc. To theauthors’ best knowledge, only recently has a few analyticalmodels been developed to compare different CRRMschemes [6–8,10,17]. However, a major limitation of theseanalytic models is that they apply network selection algo-rithm either to new calls [6,8] or to VHO calls [7,10], with-out considering the two types of call in a joint manner.Further, according to the authors’ best knowledge, noneof the previous works on CRRM analysis considered the ef-fect of hotspot location in the macro-cell on the HWN per-formance metrics due to the variation of WWAN cellularinter-cell interference. In [18], we analyzed the average(for all service classes) performance of HWN without con-sidering inter-cell interference effect for CDMA systems.The major contribution of this paper is to analytically de-rive service class based performance metrics (e.g., callblocking, outage and throughput) and VHO rates forWWAN/WLAN integrated network taking into accountthe variation of inter-cell interference effect due to hotspotlocation. Mathematical modeling techniques of Markovchain theory, random processes and probability theoryare used in the analysis.

The paper is organized as follows. Section 2 presents theproposed network selection scheme consisting of NRATSand VHD algorithms. Section 3 presents call admissioncontrol techniques in CDMA2000 and IEEE802.11. In Sec-tion 4, we evaluate the performance of the heterogeneoussystem. Section 5 presents numerical results with detaileddiscussion of the results. Finally, the paper is concluded inSection 6.

Fig. 1. Heterogeneous wireless network.

2. Proposed network selection algorithms

We consider an HWN formed by integrated WWAN andWLAN. In this paper and, without loss of generality, the se-lected RATs are the CDMA2000 and IEEE802.11 for the

WWAN and WLAN, respectively. A CDMA2000 base stationis deployed in a macro-cell while an IEEE802.11 accesspoint provides coverage to a micro-cell. Depending oncapacity requirements, one or more access points are de-ployed and the micro-cells overlap with the macro-cell,such overlapped region(s) is (are) referred to as hotspot(s)(gray-shaded regions in Fig. 1) and enjoy multi-RAT cover-age. In the multi-RAT coverage area, the proposed networkselection algorithms perform two basic functionalities: RATselection for new call (NRATS) and vertical handoff decision(VHD) for roaming call across the RATs. The key parametersconsidered for NRATS and VHD algorithms are networklayer information such as service and mobility type, mobil-ity prediction information, RAT load and data transfer cost.

Real-time (RT) service demands very stable and highquality communication. As such, vertical handoff shouldbe very restricted for an RT session to ensure stability ofcommunication. To achieve this objective, the mobilityprediction information [19] from the micro-cell is used toavoid unnecessary VHO for RT sessions, in the proposednetwork selection scheme. The non-real-time (NRT) appli-cation has a pretty flexible resource requirement, becausethe service is not time-sensitive. To avoid unnecessaryVHO, the NRT user mobility is classified as either vehicularor non-vehicular user based on a preset speed threshold(Vth). Maximum Doppler frequency estimation [20], whichis equivalent to the mobile speed estimation, is used by theBS to identify the user mobility type.

2.1. Network selection algorithm for new calls

The proposed NRATS algorithm (whose flow chart isshown in Fig. 2) for the given system model is describedas follows:

1. When a new call request arrives at the macro-cell, theNRATS algorithm checks the location of the user basedon the 802.11 RAT received signal strength. If the802.11 RAT received signal strength is below a pre-specified threshold value, the user is declared to be out-side of the hotspot and CDMA2000 RAT manages theresource. Else, if the 802.11 received signal strengthexceeds the pre-specified threshold, the user is in thehotspot area and the following operation is performed:

Fig. 2. Flowchart of proposed NRATS algorithm.

A. Hasib, A.O. Fapojuwo / Computer Networks 54 (2010) 341–356 343

2. The new call request type is detected from new callrequest message and service specific rules are executedas follows:(a) If it is an NRT call and the corresponding user

mobility is vehicular, to avoid repeated VHO,CDMA2000 is selected. For an NRT call of a non-vehicular user, 802.11 load is checked using thesaturation throughput constraint [16] or packetdelay constraint [16] and following operationsare performed:
(i) If 802.11 RAT is congested, then CDMA2000
is selected, this helps load balancing andensures service QoS. Else,

(ii) If 802.11 RAT is not congested, 802.11 RATis selected to minimize cost per unit band-width-time.

(b) For an RT call, the mobility prediction algorithmdetermines whether or not the user will moveout-of-hotspot during the session. If the usermoves out-of -hotspot within the session,CDMA2000 is selected to avoid VHO and ensurestability. Else, 802.11 load is checked and therules (i) and (ii) of part (a) are executed.

Note that, during CDMA2000 RAT selection, CDMA2000RAT load (e.g., possible blocking due to congestion) is notchecked. The reason is that the overflow call from WWANto WLAN RAT due to WWAN congestion is not allowed inthe proposed algorithm because it may cause unnecessaryVHOs.

2.2. Network selection algorithm for VHO decision

The flow chart for the proposed VHD algorithm is de-picted in Fig. 3. The VHD algorithm periodically (e.g., every2 s) checks the location information of the user in the HWN

service area based on the 802.11 RAT received signalstrength. If the user location changes, the rules (similarto NRATS algorithm in Section 2.1) in the flow chart arechecked, else the call remains in the serving RAT.

After network selection in HWN, QoS maintenance inthe selected RAT (either WWAN or WLAN) becomes thenext issue to address. The next section discusses the calladmission control techniques used to ensure that the ser-vice QoS of ongoing calls in the selected RAT will not be de-graded by the admission of a new or handoff call.

3. Call admission control

3.1. WWAN cellular system

In CDMA based WWAN system, the capacity of a cellvaries with the loading in the cell and in its neighbouringcells because the co-channel intra- and inter-cell interfer-ence vary with the loading. Hence, SIR based CAC is suit-able for CDMA systems to enforce QoS requirements[14,15].

For mathematical tractability, we make the followingassumptions:

1. Sufficient base stations transceiver elements andspreading codes are available.

2. Each mobile uplink transmission power is perfectlycontrolled by the serving BS. This will ensure uniformtransmission rate.

3. Frequency division duplexing (FDD) mode is assumed forthe uplink and the downlink, to avoid inter-ference across links of the same RAT. The uplink and thedownlink bandwidth can be varied in accordance withtraffic asymmetry between the downlink and uplink.

Based on transmission power Hi,x for service class i(i 2{1,M}) in RAT x(x 2 {W,L}), where W and L denote

Fig. 3. Flowchart of proposed VHD algorithm.


CDMA2000 and IEEE802.11 RAT, respectively and M isnumber of service classes, let Si,x denote the correspondingreceived signal power. For the CDMA2000 RAT, the re-ceived power at a distance d is:

Si;W ¼ Hi;Wd10eW =10d�g; ð1aÞ

where d is a constant that is a function of antenna gainsand connector losses, g is the path-loss exponent, and eW

is the random shadowing component in dB, having aGaussian distribution with zero mean and standard devia-tion rW dB. Similarly, for the IEEE802.11 RAT, the receivedpower at a distance d is given by [1]:

Si;L ¼Hi;Ld�k2

16p2d2 ; ð1bÞ

where �k is the wavelength.For simplicity of analysis, assume M = 2 and the ratio of

average received power S1;WS2;W

� �for service classes – 1 and 2

at the BS in power controlled environment is:

S1;W

S2;W¼

Ru1;WCu

1;W

Ru2;WCu

2;W; ð2aÞ

where Rui;x denotes the required uplink data rate for service

class i(i 2 {1,2}) in RAT x,(x 2 {W,L}), and Cui;x is the required

bit energy-to-interference density ratio (i.e., Eb/Io) for ser-vice class i in RAT x in the uplink. In the downlink, theWWAN capacity is determined by the BS transmissionpower (Hi,W) for service class-i since it directly affects thetransmitted interference. So, to maximize the downlinkcapacity, the transmission power for each session shouldbe minimized. The downlink transmission power ratio forservice classes – 1 and 2, denoted by H1;W

H2;W

� �, is [15]:

H1;W

H2;W¼

Rd1;WCd

1;W

Rd2;WCd

2;W

; ð2bÞ

where the superscript d on the right hand side refers todownlink and the parameters have the same definition asgiven above for the uplink. Next, we analytically derivethe condition for SIR based call admission in WWAN forthe assumed network model shown in Fig. 1. Call admis-sion conditions for multimedia applications are set suchthat admitting a call does not degrade the QoS require-ments of all the existing calls as well as meet the QoSrequirements of the new call to be admitted in the RAN.The CAC algorithm is described in the following steps:

1. BS periodically (e.g., every 1.25 ms for CDMA2000)measures uplink Eb/Io for each active MT and calculatesKu

i;W , the current average (over the period of 20 ms forCDMA2000) uplink Eb/Io for service class-i. Each activeMT also measures its downlink Eb/Io and reports themeasured Eb/Io (using power control frames) to the con-nected BS periodically (e.g., every 1.25 ms forCDMA2000). Then the BS calculates Kd

i;W , the currentaverage downlink Eb/Io for service class-i. The expres-sions for Ku

i;W and Kdi;W are given by:

Kui;W ¼

WuW

Rui;W

1

ðNi � 1Þaui þ

PMk¼1;k–iNkau

kSk;WSi;W

� �

þ bW

XM

k¼1

Noka

uk

Sk o;W

Si;W

� �; ð3aÞ

Kdi;W ¼

WdW

Rdi;W

1�z

ð1�qÞ ðNi�1Þadi þPM

k¼1;k–iNkadk

Hk;WHi;W

� �h iþbW

PMk¼1No

kadk

Hk o;WHi;W

� �;ð3bÞ


where WuW and Wd

W are the uplink and downlink band-width of WWAN, respectively, bW is the normalized(with respect to single MT) inter-cell interference ofWWAN, Ni denotes the number of service class-i calls(downlink or uplink links) in progress in WWAN, No

k isthe number of class-k calls in progress in the neigh-bouring cells, au

i ðadi Þ denotes the uplink (downlink)

activity factor for service class i, and Sk_o,Wand Hk_o,W

are respectively the received uplink and downlinkpower level for class-k calls at neighbouring cells ofWWAN.Furthermore, q denotes the average orthogo-nality factor in the downlink due to synchronous signalreception and z is the ratio of the sum of overhead chan-nel powers (e.g., pilot channel, paging channel, powercontrol channel) to the total downlink transmissionpower which has a typical value 0.3 [15].

2. When a class-i call arrives to WWAN, the serving BSestimates (i.e., assuming the call is admitted) uplinkand downlink Eb/Io of all the active service classes inthe RAT. The generalized expressions (i.e., applicableto both RATs) for estimating uplink and downlink Eb/Io

for service class-i calls of the same cell, when theclass-i call is admitted, are:

Eui;W ¼

1Ku

i;W

þRu

i;Waui

WuW

!�1

; ð4aÞ

Edi;W ¼

1

Kdi;W

þð1� qÞRd

i;Wadi

ð1� zÞWuW

!�1

: ð4bÞ

Then the generalized expressions for estimating uplinkand downlink Eb/Io for service class-j (where j – i) callsof the same cell, when the class-i call is admitted, are:

Euj;i;W ¼

1Ku

j;W

þRu

i;Waui C

ui;W

WuWCu

j;W

!�1

; ð5aÞ

Edj;i;W ¼

1

Kdj;W

þð1� qÞRd

i;Wadi C

di;W

ð1� zÞWdWCd

j;W

!�1

: ð5bÞ

3. For connection admission of the newly arrived class-icall, the estimated uplink and downlink Eb/Io of all theactive service classes in the cell should be higher orequal to the Eb/Io requirement of its type. So, to admita service class-i call in WWAN, the following logicalexpression must hold:

Eui;W PCu

i;W

� �Ed

i;W PCdi;W

� � YMj¼1j–i

Euj;i;W PCu

j;W

� �Ed

j;i;W PCdj;W

� �0BB@

1CCA:ð6Þ

3.2. WLAN

In [16], saturation throughput (BL) which represents themaximum load that is carried in the system under stablecondition for IEEE802.11 distributed coordinating functionmedium access control is determined. Based on BL, calladmission condition for IEEE802.11 is formulated asfollows:

Xj2M

Nj;LRj;L 6 BL; ð7Þ

where Nj,L is the number of service class-j calls in progressin WLAN and Rj,L is the mean data rate for service class-jcalls in WLAN.

4. Network-level analysis

The objective of the network-level analysis is to de-rive service class based performance metrics (e.g., callblocking, call outage) for an HWN that applies the pro-posed network selection scheme. Capacity analysis ofthe heterogeneous CDMA system is also performed inthis section. To this end, the following assumptions aremade:

1. System is operating under steady state conditions.2. All the macro-cells are statistical identical. For each

macro-cell, we take into account the interferencefrom two rings of surrounding macro-cells.

3. Frequency reuse factor of 1 is assumed for bothCDMA2000 and 802.11.

4. Since the cost per unit bandwidth-time of 802.11 issignificantly (e.g., 20 times [21]) lower than that ofCDMA2000, it is reasonable to consider that 802.11resource limitation is not a major issue comparedto that of CDMA2000. To apply Markovian analysistechnique [22], we assume that 802.11 resource isover-provisioned and hence the overflow call from802.11 to CDMA2000 RAT due to 802.11 congestionis excluded in the model.

5. For the system model depicted in Fig. 1, the hot-spot area can comprise one (or more than one)802.11 WLAN access point(s). Without loss of gen-erality, the current analysis considers that eachmacro-cell area has one hotspot region with highuser density and its dimension is of the order ofthe coverage area of a single 802.11 WLAN accesspoint (AP).

6. Non-uniform user distribution in the macro-cell areais modeled as the superposition of two uniform userdistributions: (1) X% users are uniformly distributedin the hotspot area, and (2) the remaining (100-X)%users are uniformly distributed in the out-of-hotspotarea. Since the hotspot area is less than the out-of-hotspot area, the user distribution across themacro-cell area becomes non-uniform.

7. New call arrival process: New calls (of all the serviceclasses) arrive in the macro-cell according to a Pois-son process with mean arrival rate kn. Inside the hot-spot and out-of-hotspot, a new call is randomlydetermined as class-i call with probability vin

i andvout

i , respectively [15].8. Handoff call arrival process: VHO calls of service

class-i arrive in CDMA2000 and 802.11 RATsaccording to independent Poisson processes [8]with mean rate kh_M,i and kh_l,i, respectively. Forservice class-i, horizontal handoff calls ofCDMA2000 RAT arrive according to a Poisson pro-cess with mean rate kh,i.


9. Call duration for service class-i,Tcall,i, is exponentiallydistributed with mean 1/li.

10. Mobility model: In a hotspot, vehicular and non-vehicular users are assumed to be mixed in a certainproportion (h, defined as the proportion of users in ahotspot that are vehicular) depending on the hotspotcharacteristics. A low value of h (e.g., h < 0.3) impliesthat a high proportion of users in the hotspot area isnon-vehicular (e.g., as found in office, campus area)whereas a high value of h (e.g., h > 0.7) correspondsto public WLAN area with large number of vehicularusers. We quantify the user mobility (i.e., vehicularand non-vehicular) in terms of the micro-cell resi-dence time, defined as the time an active or inactiveuser spends in a micro-cell. The micro-cell residencetime of a non-vehicular user follows power law dis-tribution [23], whose probability density function(pdf) is denoted by fm_WLAN(tm) = K/tm, where Kis anormalization factor. Vehicular users in a micro-cellfollow a scaled macro-cell residence time distribu-tion with pdf fm WWAN;iðtÞ ¼ /ie

�/i t=c

c , where themacro-cell cell residence time for service class-i isexponentially distributed with mean 1//i minutes[24], / is the average macro-cell boundary crossingrate and c is the micro-cell to macro-cell radius ratio.Detailed discussion about the mobility model isgiven in [24].

In the ensuring analysis, the subscript i used in theabove notations is replaced by rt and nrt for RT and NRTservice class, respectively.

4.1. Effect of cellular inter-cell interference

Inter-cell interference affects the received Eb/Io and sys-tem capacity in cellular CDMA system. For the assumedCDMA2000 propagation model, normalized inter-cellinterference or inter-cell interference per user [14] is givenby:

bW ¼dm

do

� �g

10ðeW;o�eW ;mÞ=106 1; ð8Þ

where dm is the shortest distance from the central macro-cell BS (e.g., BS ‘‘A”, shown in Fig. 4) to a user in the same

Fig. 4. Inter-cell interference configuration.

macro-cell and do is the shortest distance from co-channelBS (e.g., BS ‘‘B”, shown in Fig. 4) to the same user (fordownlink direction). The parameters eW,m and eW,o arerespectively the shadowing components of the two chan-nels that connect the user to BS ‘‘A” and to BS ‘‘B”, and theyare independent so that their difference has zero mean and2r2

W variance. By (assumption 2), bW for uplink and down-link are equal [15]. Random variable bW follows Gaussiandistribution [14] with mean �bW and standard deviationSD(bW). Note that both �bW and SD(bW) can vary, dependingon the user distribution (not the number of users) in themacro-cell or hotspot location.

4.2. Call arrival rates

For the proposed network selection algorithms (Section2), RT call (e.g. voice) arrival rates at CDMA2000 and802.11 RATs are functions of mobility prediction informa-tion, i.e., the probability of moving out of the hotspot dur-ing a session as predicted by the mobility predictionalgorithm. The estimated probabilities of moving out-of-hotspot for new and roaming hotspot RT calls are obtainedas follows [24]:

PEnew;rt ¼ h

/rt=clrt þ/rt=c

þð1� hÞ ½e�lrt þKEið�lrtÞ�lrtKðU�1Þ

� �ð1þULPÞ;

ð9Þ

PEro;rt ¼ h

/rt=clrt þ /rt=c

� ð1� hÞKEið�lrtÞ� �

ð1þULPÞ; ð10Þ

where EiðzÞ ¼ �R1�z

e�t

t dt;U is the upper limit of micro-celldwell time and ULP (�1 6ULP 6 1) is the mobility predic-tion error. The mobility prediction algorithm with param-eter ULP > 0 (error type I) erroneously predicts that ahotspot RT user will move out-of-hotspot during the ses-sion whereas the user does not. For the mobility predictionalgorithm with parameter ULP < 0 (error type II), the out-come is vice versa. For the proposed NRATS algorithm,the RT mean call arrival rate of new calls in CDMA2000and 802.11 is derived as follows:

kn M;rt ¼ ð1�XÞknvoutrt þXknvin

rt PEnew;rt; ð11Þ

kn l;rt ¼ Xknvinrt 1� PE

new;rt

� �: ð12Þ

Based on the proposed VHD algorithm, RT VHO rate fromCDMA2000 to 802.11 is derived as:

kh l;rt ¼ ½ðkh;rt þ kh M;rtÞð1� Phb;rtÞ

þ ð1�XÞknvoutrt ð1� Pnb;rtÞ�PM;rt 1� PE

ro;rt

� �; ð13Þ

where Phb,i and Pnb,i are the handoff and new call blockingprobabilities in WWAN, respectively, and PM,rt is the prob-ability of an active and out-of-hotspot user moving into thehotspot which is given by [24]:

PM;rt ¼X h /rt=c

lrtþ/rt=c � ð1� hÞKEið�lrtÞ� �

ð1�XÞ : ð14Þ

The RT VHO call arrival rate from 802.11 to CDMA2000(kh_M,rt) depends on admitted calls to 802.11 and usermobility. For the proposed network selection algorithms,


kh_M,rt should be zero when accurate mobility predictioninformation (i.e., ULP = 0) is available to NRATS and VHDalgorithms. For ULP > 0, according to Eqs. (12) and (13), aproportion (not all) of RT users who will not move out-of-hotspot during their respective sessions are admittedto 802.11. Due to the mobility characteristics of the usersadmitted to 802.11, kh_M,rt should be zero. For ULP < 0,according to (12) and (13), more users (than in the caseof ULP = 0) are admitted to 802.11 and these additionalusers will move out-of-hotspot during the session. Hence,kh_M,rt is derived as:

kh M;rt ¼

kh l;rtð1� Phb wlan;rtÞ�ULP PE

ro;rt

ð1þULP Þ 1�PEro;rtð Þ

� �;

þkn l;rtð1� Pnb wlan;rtÞ�ULP PE

new;rt

ð1þULPÞ 1�PEnew;rtð Þ

� �;ULP < 0;

0; ULP P 0:

8>>>>><>>>>>:

ð15Þ

For the proposed network selection algorithms, NRT callarrival rates in CDMA2000 and 802.11 RATs are functionsof velocity estimation information, i.e., the estimated prob-ability of being a vehicular user in the hotspot. Let UV

(�1 6UV 6 1) denote the velocity estimation error. Thevelocity estimator with parameter UV > 0 erroneously esti-mates NRT non-vehicular mobility as vehicular. Withparameter UV < 0 the estimation is vice versa. For the pro-posed NRATS algorithm, the NRT mean call arrival rates fornew calls in CDMA2000 and 802.11 RATs are derived withrespect to hotspot mobility condition (h) as follows:

kn M;nrt ¼ ð1�XÞknvoutnrt þXknvin

nrthð1þUV Þ; ð16Þkn l;nrt ¼ Xknvin

nrtð1� hð1þUV ÞÞ: ð17Þ

The first term on the right hand side of (16) represents theout-of-hotspot NRT new calls and the second term repre-sents the proportion of hotspot NRT new calls that occu-pies CDMA2000 RAT. The remaining proportion ofhotspot NRT new calls that occupies 802.11 is included in(17). For the proposed VHD algorithm, NRT VHO call arrivalrate from CDMA2000 to 802.11 is derived as:

kh l;nrt ¼ ½ðkh;nrt þ kh M;nrtÞð1� Phb wlan;nrtÞþ ð1�XÞkn;nrtð1� Pnb wlan;nrtÞ�PM;nrtð1� hð1þUV ÞÞ;

ð18Þ

where Phb_wlan,i and Pnb_wlan,i are the handoff and new callblocking probabilities in WLAN, respectively, The NRTVHO call arrival rate from 802.11 to CDMA2000 (kh_M,nrt)is a function of the probability of moving out-of-hotspotfor a user that has been admitted to 802.11, denoted byPðaÞcond;nrt . Finally, kh_M,nrt is derived as:

kh M;nrt ¼ kh l;nrtð1� Phb wlan;nrtÞPðrÞcond;nrt

þ kn l;nrtð1� Pnb wlan;nrtÞPðnÞcond;nrt : ð19Þ

In this equation, the first and second terms on the righthand side represent the roaming and new NRT calls inthe hotspot, respectively, that occupy 802.11 in the hot-spot area and finally move out-of-hotspot. When UV = 0,according to (17) and (18), NRT non-vehicular users areadmitted to 802.11. If UV > 0, a proportion (<1) of NRT

non-vehicular users is admitted to 802.11 and hencePðaÞcond;nrt remains the same as in the case of UV = 0. WhenUV < 0, according to (17) and (18), a fraction of the vehicu-lar users, in addition to the non-vehicular users, is admit-ted to 802.11. Hence, from [24]:

PðnÞcond;nrt ¼

½e�lnrtþKEið�lnrt Þ�lnrt KðU�1Þ ;UV P 0;

½e�lnrt þKEið�lnrt Þ�lnrt KðU�1Þ

� �ð1�hÞ� UV h/nrt=c

lnrtþ/nrt=c

ð1�h�UV hÞ ;UV < 0;

8>><>>: ð20Þ

PðrÞcond;nrt ¼KEið�lnrtÞ;UV P 0;

KEið�lnrtÞð1�hÞ� UV h/nrt=clnrtþ/nrt=c

ð1�h�UV hÞ ;UV < 0:

8<: ð21Þ

By (assumption 2), the horizontal handoff rate ofCDMA2000 for individual service class-i is derived asfollows:

kh;i ¼ð1� Pnb;iÞ ð1�XÞknvout

i þXknvini h

� �si

1� ð1� Phb;iÞsi: ð22Þ

Here si is the probability of moving out of a macro-cell dur-ing a call given as si ¼ /i

liþ/i.

4.3. Markov chain model

Due to the memoryless property of Poisson call arrivalprocess and the exponentially distributed channel holdingtime, a multi-service heterogeneous wireless network(CDMA2000 and 802.11 coexisting RATs) can be modeledas a 2M-dimensional Birth and Death Markov Process,where M is the number of service classes. For simplicityof the analysis, we assume M = 2 (e.g., service class-1 isthe voice (RT) application and service class-2 is the data(NRT) application). We then split the 2M-dimensional Mar-kov Process into two separate (one for each RAT) M-dimen-sional Markov processes, solving each independently. Thisapproximation is valid because, by (assumption 4), 802.11call blocking probability is negligible (e.g.,�0.01). Further,we have also confirmed the accuracy of the above approx-imation by comparing results from two models whenM = 1. The state diagrams of the Markov Processes(M = 2) are presented in Fig. 5, where the state vector ofthe Markov processes is represented by s(N1,N2), whereNi is the number of class-i calls in progress. In the statetransition diagram for CDMA2000 (shown in Fig. 5a), thetransition rate between state s(N1,N2) to state s(N1 +1,N2), due to the arrival of a voice call (i.e., new call, hori-zontal handoff call or VHO call from 802.11) isA1(s)(kn_M,1 + kh,1 + kh_M,1) and the transition rate betweenstate s(N1,N2) to state s(N1 � 1,N2), caused by the comple-tion or departure rate (due to horizontal handoff to neigh-bouring cell and VHO to 802.11) of a voice call isN1(l1 + /1) + kh_l,1. Similarly, for an NRT data call arrival(i.e., state transition from s(N1,N2) to state s(N1,N2 + 1))and departure (i.e., state transition from s(N1,N2) to states(N 1,N2 � 1)), the transition rates are A2(s)(kn_M,2 + kh,2 +kh_M,2) and N2(l2 + /2) + kh_l,2, respectively. Here, Ai(s) isthe call admission probability for service class-i call inCDMA2000 RAT and its value varies from 0 to 1 dependingon the current state ‘‘s”. Ai(s) is determined from calladmission condition, given in Eq. (6) as follows:

Fig. 5. Markov process – two service class mix (a) CDMA2000; (b) 802.11.


AiðsÞ ¼ Prob Eui;W P Cu

i;W

� �Prob Ed

i;W P Cdi;W

� �

�Y2

j¼1j–i

Prob Euj;i;W P Cu

j;W

� �Prob Ed

j;i;W P Cdj;W

� �0BB@

1CCA:ð23Þ

Since the normalized inter-cell interference (bW) ofCDMA2000 follows the Gaussian distribution, using Eqs.(3a) and (4a):

Prob Eui;W P Cu

i;W

� �¼ Q

bW � Yui

SDðbWÞ

� �ð24Þ

where, Q(.) is the Marcum’s Q-function and

Yui ¼

WuW �

P2k¼1Nkau

kRuk;WCu

k;WP2k¼1No

kaukRu

k;WCuk;W

: ð25Þ

The derivation of Eqs. (24) is given in Appendix A. Simi-larly, for the other terms in (23), the probability values


are determined using Eqn. (3a), (3b), (4a), (4b), (5a), (5b).In (24) and (25), we consider Si;W

Sk o;W¼ Si;W

Sk;Wand Hi;W

Hk o;W¼ Hi;W

Hk;W,

by (assumption 2).In the state transition diagram for 802.11 (shown in

Fig. 5b), the transition rate from state s(N1,N2) to states(N1 + 1,N2), due to the arrival of a voice call (i.e., new callor VHO call from 802.11) is kn_l,1 + kh_l,1 and the transitionrate from state s(N1,N2) to state s(N1 � 1,N2), caused by thecompletion or VHO to 802.11 of a voice call is N1l1 + kh_M,1.Similar transition rates for NRT data calls (service class-2)in 802.11 are shown in Fig. 5b. In the figure, N1m and N2m

denote the maximum number of class-1 and class-2connections in CDMA2000, whereas m1 and m2 are thecorresponding numbers that satisfy Eq. (7) for the 802.11system.

4.4. Steady-state equations

Using the rate equality principle [22], the steady-stateequations for the Markov chain for CDMA2000 can be writ-ten as:

PWðsðN1;N2ÞÞX2

i¼1

fAiðsðN1;N2ÞÞðkn M;i þ kh;i þ kh M;iÞ

þ Niðli þuiÞ þ INi>0kh l;ig¼ IN1>0PWðsðN1 � 1;N2ÞÞA1ðsðN1 � 1;N2ÞÞðkn M;1

þ kh;1 þ kh M;1Þ þ IN2>0PWðsðN1;N2

� 1ÞÞA2ðsðN1;N2 � 1ÞÞðkn M;2 þ kh;2 þ kh M;2Þþ IN1<N1mPW ðsðN1 þ 1;N2ÞÞfðN1 þ 1Þðl1 þu1Þþ kh l;1g þ IN2<N2mPW ðsðN1;N2 þ 1ÞÞfðN2 þ 1Þ� ðl2 þu2Þ þ kh l;2g ð26Þ

Similarly for 802.11,

PLðsðN1;N2ÞÞX2

i¼1

fkn l;i þ kh l;i þ Nili þ INi>0kh M;ig

¼ IN1>0PLðsðN1 � 1;N2ÞÞðkn l;1 þ kh l;1Þþ IN2>0PLðsðN1;N2 � 1ÞÞðkn l;2 þ kh l;2Þþ IN1<m1 PLðsðN1 þ 1;N2ÞÞfðN1 þ 1Þl1 þ kh M;1gþ IN2<m2 PLðsðN1;N2 þ 1ÞÞfðN2 þ 1Þl2 þ kh M;2g: ð27Þ

Eqs. (26) and (27) are solved along with the normalizationcondition to obtain the steady-state probabilities, Px(s). Inboth Eqs. (26) and (27), Icondition is a logical indicator whosevalue is 1 if the condition is true and zero otherwise. A der-ivation of Eq. (26) is given in Appendix B (Eq. (27) can bederived similarly).

4.5. Performance metrics

Based on the state probabilities (Px(s)), the servicebased performance metrics such as call blocking probabil-ity, outage probability, and throughput can be derived asfollows:

(1) Call blocking probability of a service class-i call:From Eq. (6), since we assume the same Eb/Io thresh-old for both new and handoff calls of service class-i,

CDMA2000 call blocking probability (generalized) ofservice class-i is:X
Pnb;i ¼ Phb;i ¼
s

ð1� AiðsÞÞPW ðsÞ: ð28Þ

Similarly, the IEEE802.11 call blocking probability(generalized) of service class-i is:

Phb wlan;i ¼ Pnb wlan;i ¼Xmj

Nj¼0i–j

PLðsðmi;NjÞÞ

where i 2 1;2 and j 2 1;2: ð29Þ

(2) CDMA2000 Outage probability of service class-i:According to Eqs. (3a) and (3b), the measured uplinkand downlink Eb/Io of an admitted call in CDMA2000,Ku

i;W and Kdi;W , respectively are random variables

because of the normalized inter-cell interference,bW. An admitted call is in outage if the measured Eb/Io is smaller than the required Eb/Io. We calculate out-age probability of CDMA2000 users as follows:X � � � ��
Pout;i¼
s

PW ðsÞ P Kui;W ðsÞ<Cu

i;W þP Kdi;WðsÞ<Cd

i;W

¼X

s

PW ðsÞ QWu

i ðsÞ��bW

SDðbWÞ

� �þQ

Wdi ðsÞ��bW

SDðbWÞ

! !;

ð30Þwhere from Eqs. (3a) and (3b):
P Wu
i ðsÞ¼Wu

W�aui Ru

i;WCui;W ðNi�1Þ� 2

k¼1;k–iaukNkRu

k;WCuk;WP2

k¼1aukNo

kRuk;WCu

k;W

;

ð31Þ

Ph i
Wd
i ðsÞ¼ð1�zÞWd

W�ð1�qÞ adi Rd

i;W Cdi;W ðNi�1Þþ 2

k¼1;k–iadkNkRd

k;W Cdk;WP2

k¼1adkNo

kRdk;WCd

k;W

:

ð32ÞIn Eqs. (31) and (32), the denominator representsthe inter-cell interference and the terms precededby negative sign in the numerator represent the in-tra-cell interference. Hence, with the increase ofboth intra- and inter-cell interference, the probabil-ity of outage increases.

(3) Throughput for service class-i:The service class based throughput (Ti) is the sum ofthe uplink and downlink throughput for service classcall-i in both CDMA2000 and 802.11 RATs. Hence:X � �
Ti ¼
s

PW ðsÞNi Rui;Wau

i þ Rdi;Wad

i

þX

s

PLðsÞNi Rui;La

ui þ Rd

i;Ladi

� �: ð33Þ

If Rui;x and Rd

i;x are in kbps, hence Ti is in kbps. The firstand second terms on the right hand side representthe throughput in CDMA2000 and 802.11 RAT,respectively.

5. Numerical results and discussions

The parameter values assumed in this section are listedin Tables 1 and 2. To evaluate the effect of inter-cell inter-ference on CDMA2000 performance due to non-uniform


user distribution in the macro-cell, Monte Carlo simulationis performed based on Eq. (8) and assuming macro-cell ra-dius 1000 m. Three cases are considered in the simulation:(1) hotspot is located at the center of the macro-cell, whichmeans BS and AP are co-located (assumption 5 of Section4), (2) BS and AP are 450 m apart (about half the macro-cellradius), and (3) BS and AP are 900 m apart (i.e., most of theusers are near the boundary of the macro-cell). For the pro-posed network selection scheme, the number ofCDMA2000 users in the hotspot varies with the proportionof the hotspot users that have vehicular mobility (h). It af-fects the CDMA2000 user distribution in the macro-celland hence the values of �bW and SD(bW). Assuming lowCDMA2000 call blocking probability and lower VHO ratecompared to new call arrival rate for the proposed networkselection scheme (Figs. 7–9 validate this assumption later),the proportion of calls occupying CDMA2000 in the hot-spot (H) for a certain h can be approximated by:

H � vinrt PE

new;rt þ vinnrthð1þUvÞ: ð34Þ

The values of �bW and SD(bW) for the above three cases ofthe hotspot location, obtained from simulation, are shownin Table 3. The results in Table 3 can be interpreted as fol-lows: when all the users in the hotspot follow non-vehicu-lar mobility distribution (h = 0), according to Eq. (34), thepercentage of CDMA2000 users in the hotspot (whenh = 0) is only 1%. It results in uniformly distributedCDMA2000 users in the macro-cell and same values of�bW and SD(bW) for all the three cases of hotspot location.

Table 1Traffic model [12].

Link Class 1 call Class 2 call

Up Down Up Down

CDMA2000 bit rate, Ri,W 16 kbps 16 kbps 64 kbps 384 kbps802.11 bit rate, Ri,L 32 kbps 32 kbps 128 kbps 512 kbpsActivity factor, ai 0.5 0.5 0.00285 0.015Ratio of new call arrival

out-of-hotspot, vouti

0.9 0.1

Ratio of new call arrival inhotspot, vin

i

0.3 0.7

Mean call duration, 1/li 120 s 3000 sMean macro-cell

residence time forvehicular user, 1//i

300 s 1200 s

Table 2System model.

CDMA2000 1xEV-DO capacity, WW

IEEE802.1 1b capacity (saturation), WL

% user in hotspot, XMicro- to Macro-cell radius ratio, cPath-loss exponent, gShadow fading standard deviation, rW

Required Eb/Io in CDMA2000 for class 1 and class 2 Call, Cu1;W ¼ Cd

1;W ¼ Cu2;W

Orthogonality factor in downlink, qRatio of pilot signal power to the total downlink transmission power, zUpper limit of micro-cell residence time, UPower law distribution constant, K

As h increases, the percentage of CDMA2000 users in thehotspot increases. As the hotspot distance increases frommacro-cell BS, bW increases according to Eq. (8).

Since downlink traffic is higher than uplink traffic, it isassumed that Wd

W=WW ¼ 0:75 [15] to optimize systemperformance. We have observed that the optimized valueof Wd

W=WW is valid for different values of h.In Fig. 6, CDMA2000 call blocking probability for the

proposed network selection scheme is plotted versus theproportion of the hotspot users that have vehicular mobil-ity (h) for the three hotspot locations, when kn = 1 call/min.According to (11)–(19), call arrival rates at CDMA2000 RATincreases with h. For cases 1 and 2 of hotspot locations,since bW decreases with h, inter-cell interference also de-creases assuming a fixed number of neighbouring cellusers and homogeneous user distribution. When h 6 0.8,bW causes a decrease of the call blocking probability de-spite the increase of call arrival rates. When h > 0.8, the in-crease of call arrival rates at CDMA2000 becomesdominant and the call blocking probability increases. Forcase 3, both bW and call arrival rates increase with h, hencethe call blocking probability increases with h. In theremainder of this section, we assume the case 1 hotspotlocation (i.e., the hotspot is at the center of the macro-cell).

In the following, the always best connected (ABC) selec-tion technique described in [9] is selected for comparisonwith the proposed network selection scheme. The paper[9] uses analytic hierarchy process (AHP) and grey rela-tional analysis (GRA) algorithm for network selection inan heterogeneous system. In [9], the ABC selection schemedecides on new call RAT selection and VHO, based onparameters such as throughput, timeliness, reliability,security, and cost. The common focus between this previ-ous work and the scheme proposed in this paper assuresa fair comparison of their respective results.

2.4 Mbps7.48 Mbps (68% of max. capacity) [15]50 [4]0.14 [13]8 dB [13]

¼ Cd2;W

7 dB [14]

0.5 [14]0.3 [14]400 [21]0.167 [21]

Table 3CDMA2000 normalized inter-cell interference (bW).

Case h 0 0.2 0.4 0.6 0.8 1

1 �bW 0.028 0.022 0.019 0.017 0.015 0.013SD(bW) 0.12 0.104 0.097 0.092 0.086 0.084

2 �bW 0.028 0.025 0.023 0.02 0.021 0.02SD(bW) 0.125 0.107 0.105 0.103 0.096 0.095

3 �bW 0.028 0.03 0.032 0.034 0.035 0.037SD(bW) 0.124 0.126 0.128 0.14 0.145 0.17

Fig. 6. CDMA2000 call blocking probability for three hotspot locations.


The VHO call arrival rates of the RT and NRT sessions areplotted versus the proportion of the hotspot users thathave vehicular mobility (h) in Figs. 7 and 8, respectively,with assumed kn = 1 call/min. It is observed that the VHOrates of RT and NRT calls are lower for the proposedscheme than that of ABC scheme under all mobility condi-tions because the proposed scheme uses mobility predic-

Fig. 7. Real-time vertical han

tion and user speed information for RT and NRT calls,respectively. According to Eqs. (13) and (18), since kh_l,i

is a function of PM,i which increases with h and also a func-tion of ð1� PE

ro;rtÞ (or (1 � h)) which decreases with h, theshape of VHO rate curve for the proposed network selec-tion scheme is determined by the dominating factor ofthe two. For the ABC scheme, VHO rate increases rapidly

doff call arrival rates.

Fig. 8. Non-real-time vertical handoff call arrival rates.


with h because it selects 802.11, if available for a call underall mobility conditions due to its better throughput, time-liness and lower cost than that of CDMA2000. The resultshows that the VHO rate of RT (NRT) calls for the proposedscheme is about 35 (4) times lower than that of the ABCscheme at the worst hotspot mobility condition (h = 1).Hence, the proposed scheme minimizes vertical handoffprocessing cost and assures service stability under allmobility conditions. The effect of mobility prediction errorand speed estimation error on the proposed network selec-

Fig. 9. CDMA2000 call blocking probab

tion scheme is also considered in Figs. 7 and 8. From Eqs.(9)–(21), negative error (i.e., more new and roaming callsoccupy 802.11 in hotspot than in the zero error case)causes higher VHOs than that obtained with zero error.For positive error (i.e., more calls occupy CDMA2000 inthe hotspot than in the zero error case), VHOs are lowerthan that of the zero error.

Next we evaluate the performance metrics (calculatedby (28)–(33)) to determine whether or not the reductionin the VHO rate affects the system performance. In Fig. 9,

ility against mobility parameter.

Fig. 10. CDMA2000 call blocking probability against new call arrival rate.


the call blocking probability in the CDMA2000 RAT for thetwo network selection schemes (i.e., proposed scheme andABC scheme) is plotted versus the proportion of the hot-spot users that have vehicular mobility (h), assuming kn

= 1 call/min. It is shown that the call blocking probabilitiesof both RT and NRT sessions for the proposed networkselection scheme are lower (e.g., about 5% lower whenh = 1) than that of the ABC scheme under all mobility con-ditions. The reasons are as follows. For the ABC scheme,

Fig. 11. Throu

with the increase of mobility, VHO rate increases rapidly(as seen from Figs. 7 and 8) and results in an increase inthe call blocking probability. Note that, for the ABCscheme, the distribution of bW (i.e., �bW ¼ 0:028 andSD(bW) = 0.12) is the same for all values of h becauseCDMA2000 users are uniformly distributed (assumption6 of Section 4) in the macro-cell (hotspot users occupy802.11 RAT). For the proposed scheme, the shape of thecurves is determined by bW and call arrival rates (as ex-

ghput.


plained under the discussion of Fig. 6). It is also observedthat RT sessions have higher call blocking probability thanthat of NRT sessions because of the higher required averagedata rate per call (Table 1).

In Fig. 10, the call blocking probability in theCDMA2000 RAT for the two network selection schemes isplotted versus the new call arrival rate (kn) for the worsthotspot mobility condition (h = 1). These results are consis-tent with the results of Fig. 9. With the increase of kn, thecall blocking probability of both schemes increases rapidlydue to increased HWN loading.

The variation of CDMA2000 call outage probabilityagainst h demonstrates similar shape curves, as shown inFig. 9 with about 1–3% lower value.

In Fig. 11, the throughput of the HWN for the two net-work selection schemes is plotted versus the proportion ofthe hotspot users that have vehicular mobility (h), assum-ing kn = 1 call/min. It is interesting to find that the RT andNRT throughput for the proposed network selectionscheme is lower than that for the ABC scheme under allmobility conditions. The explanation is as follows: for theABC scheme, all calls in the hotspot occupy 802.11 RATwhereas for the proposed scheme, only a fraction (depend-ing on mobility condition) of hotspot users occupy 802.11RAT. Since the 802.11 RAT has a higher data rate than theCDMA2000 RAT, this leads to the higher throughput forthe ABC scheme compared to our proposed scheme. Notethat the throughput is also a function of call blocking prob-ability and call arrival rates, the shape of the curves shownin Fig. 11 is determined by the dominant of the two factors.

6. Conclusion

This paper proposes a cross-protocol-layer RRMscheme in WWAN and WLAN integrated networks thatminimizes VHO rate, and reduces call blocking and outageprobabilities for both RT and NRT services in HWN. Per-formance of the proposed scheme is analyzed using thetheory of Markov chains. Non-uniform user distributionin macro-cell area, due to the presence of hotspot andconsideration of parameter estimation error make theanalysis realistic. Numerical results show that, under highhotspot mobility condition, the proposed network selec-tion algorithms exhibit lower VHO rate for RT and NRTcalls, up to 35 times and 4 times lower than that obtainedfrom the Always Best Connected (ABC) scheme, respec-tively. In addition, the proposed scheme has about 5%lower call blocking probabilities than those of ABCscheme under worst hotspot mobility condition. However,the proposed scheme has a lower throughput than that ofABC scheme because only a fraction of calls in the hotspotoccupy IEEE802.11. From the numerical results, it is con-cluded that the proposed RRM scheme exhibits very goodservice quality hence it is useful for practical HWNdeployment.

Appendix A

We have derived the call admission probability inCDMA2000 RAT with respect to four probability functions,

given in Eq. (23). Here, the computation of Prob Eui;W P

�Cu

i;WÞ is elaborated:From Eq. (4a),

Eui;W ¼

WuW

Rui;W

1

Niaui þ

PMk¼1;k–iNkau

kSk;WSi;W

� �þ bW

PMk¼1No

kauk

Sk o;WSi;W

� �

¼WuW

Rui;W

1

Niaui þ

PMk¼1;k–iNkau

k

Ruk;W Cu

k;W

Rui;W Cu

i;W

� �þ bW

PMk¼1No

kauk

Ruk;W Cu

k;W

Rui;W Cu

i;W

� �

¼Wu

WCui;WPM

k¼1NkaukRu

k;WCuk;W þ bW

PMk¼1No

kaukRu

k;WCuk;W

� *S1;W

S2;W¼

Ru1;WCu

1;W

Ru2;WCu

2;W;

Si;W

Sk o;W¼ Si;W

Sk;W

" #:

Now,

Prob Eui;W P Cu

i;W

� �

¼ ProbWu

WCui;WPM

k¼1NkaukRu

k;WCuk;W þ bW

PMk¼1No

kaukRu

k;WCuk;W

P Cui;W

!

¼ ProbWu

W �PM

k¼1NkaukRu

k;WCuk;WPM

k¼1Nokau

kRuk;WCu

k;W

P bW

!

¼ Q�bW � Yu

i

SDðbW Þ

� �; *bW follows normal distribution:

Here, Q(.) is the Marcum’s Q-function and we assumed

Yui ¼

WuW �

P2k¼1Nkau

kRuk;WCu

k;WP2k¼1No

kaukRu

k;WCuk;W

:

Appendix B

We write the steady state balance equations for eachstate of Fig. 5a using the rate equality principle:X

Rate Out ¼X

Rate In

PWðsð0; 0ÞÞX2

i¼1

fAið0; 0Þðkn M;i þ kh;i þ kh M;iÞg

¼ PWðsð1;0ÞÞfðl1 þu1Þ þ kh l;1gþPWðsð0;1ÞÞfðl2 þu2Þ þ kh l;2g ðStateð0;0ÞÞ

PW ðsð1;0ÞÞX2

i¼1

fAiðsð1;0ÞÞðkn M;iþkh;iþkh M;iÞgþl1þu1þkh l;1

" #

¼PW ðsð0;0ÞÞA1ðsð0;0ÞÞðkn M;1þkh;1þkh M;1ÞþPW ðsð2;0ÞÞf2ðl1

þu1Þþkh l;1gþPW ðsð1;1ÞÞfl2þu2þkh l;2g ðStateð1;0ÞÞ

PWðsð2;0ÞÞX2

i¼1

fAiðsð2;0ÞÞðkn M;i þ kh;i þ kh M;iÞ

þ 2ðl1 þu1Þ þ kh l;1g

¼ PWðsð1;0ÞÞA1ðsð1;0ÞÞðkn M;1 þ kh;1 þ kh M;1Þ

þPWðsð3;0ÞÞf3ðl1 þu1Þ þ kh l;1g

þPWðsð2;1ÞÞfl2 þu2 þ kh l;2gðStateð2;0ÞÞ...


PW ðsðN1m;0ÞÞfA2ðsðN1m;0ÞÞðkn M;2 þ kh;2 þ kh M;2Þþ N1mðli þuiÞ þ kh l;1g¼ PWðsðN1m � 1;0ÞÞA1ðsðN1m � 1;0ÞÞðkn M;1 þ kh;1 þ kh M;1ÞþPWðsðN1m;1ÞÞfl2 þu2 þ kh l;2g ðStateðN1m;0ÞÞ

PWðsð0;1ÞÞX2

i¼1

fAiðsð0;1ÞÞðkn M;iþkh;iþkh M;iÞgþl2þu2þkh l;2

" #

¼PWðsð0;0ÞÞA2ðsð0;0ÞÞðkn M;2þkh;2þkh M;2ÞþPW ðsð1;1ÞÞfðl1

þu1Þþkh l;1gþPW ðsð0;2ÞÞf2ðl2þu2Þþkh l;2g ðStateð0;1ÞÞ

PWðsð1;1ÞÞX2

i¼1

fAiðsð1;1ÞÞðkn M;iþkh;iþkh M;iÞþliþuiþkh l;ig

¼PWðsð0;1ÞÞA1ðsð0;1ÞÞðkn M;1þkh;1þkh M;1ÞþPWðsð1;0ÞÞA2ðsð1;0ÞÞðkn M;2þkh;2þkh M;2ÞþPWðsð2;1ÞÞf2ðl1þu1Þþkh l;1gþPWðsð1;2ÞÞf2ðl2þu2Þþkh l;2g ðStateð1;1ÞÞ

PWðsðN1m;N2mÞÞX2

i¼1

fNimðli þuiÞ þ kh l;ig

¼ PW ðsðN1m � 1;N2mÞÞA1ðsðN1m � 1;N2mÞÞðkn M;1 þ kh;1

þ kh M;1Þ þPWðsðN1m;N2m � 1ÞÞA2ðsðN1m;N2m � 1ÞÞ� ðkn M;2 þ kh;2 þ kh M;2Þ ðStateðN1m;N2mÞÞ:

In general, for any state s(N1,N2), we have:

PWðsðN1;N2ÞÞX2

i¼1

fAiðsðN1;N2ÞÞðkn M;i þ kh;i þ kh M;iÞ

þ Niðli þuiÞ þ INi>0kh l;ig¼ IN1>0PWðsðN1 � 1;N2ÞÞA1ðsðN1 � 1;N2ÞÞ� ðkn M;1 þ kh;1 þ kh M;1Þ þ IN2>0PWðsðN1;N2 � 1ÞÞ� A2ðsðN1;N2 � 1ÞÞðkn M;2 þ kh;2 þ kh M;2Þþ IN1<N1mPWðsðN1 þ 1;N2ÞÞfðN1 þ 1Þðl1 þu1Þþ kh l;1g þ IN2<N2mPWðsðN1;N2 þ 1ÞÞ� fðN2 þ 1Þðl2 þu2Þ þ kh l;2g:

The above is Eq. (26) in the paper. A similar enumeration ofthe steady-state equations using Fig. 5b gives Eq. (27) inthe paper.

References

[1] T.S. Rappaport, Wireless Communications: Principles and Practice,second ed., Prentice Hall PTR, 2001.

[2] L.C. Wang, C.Y. Liao, Y.L. Lin, C.J. Chang, Tier-Selection Algorithmswith Multi-Class Traffic in CDMA Hierarchical Cellular Systems, IEEEVTC Spring 2003, pp. 1158–1162.

[3] 3GPP2, 3GPP2-WLAN interworking, S.R0087-A (v1.0), 2006.[4] O. Yilmaz, A. Furuskär, J. Pettersson, A. Simonsson, Access Selection

in WCDMA and WLAN Multi-access Networks, IEEE VTC Spring 2005,pp. 2240–2244.

[5] Y. Xiaohuan, N. Mani, Y.A. Cekercioglu, A traveling distanceprediction based method to minimize unnecessary handovers fromcellular networks to WLANs, IEEE Communications Letters 12 (2008)14–16.

[6] X. Gelabert, J.P. Romero, O. Sallent, R. Agustı́, A Markovian approachto radio access technology selection in heterogeneous multi-access/multi-service wireless networks, IEEE Transaction on MobileComputing 7 (10) (2008) 1257–1270.

[7] E.S. Navarro, Y. Lin, V.W.S. Wong, An MDP-based vertical handoffdecision algorithm for heterogeneous wireless networks, IEEETransactions on Vehicular Technology 57 (2008) 1243–1254.

[8] F. Yu, V. Krishnamurthy, Optimal joint session admission control inintegrated WLAN and CDMA cellular networks with vertical handoff,IEEE Transactions on Mobile Computing (2007) 126–139.

[9] Q.Y. Song, A. Jamalipour, Network selection in an integrated wirelessLAN and UMTS environment using mathematical modeling andcomputing techniques, IEEE Wireless Communication Magazine(2005) 42–48.

[10] M. Liu, Z. Li, X. Guo, E. Dutkiewicz, Performance analysis andoptimization of handoff algorithms in heterogeneous wirelessnetworks, IEEE Transactions on Mobile Computing 7 (2008) 846–857.

[11] Y.-H. Chen, N.-Y. Yang, C.-J. Chang, F.-C. Ren, A utility function-basedaccess selection method for heterogeneous WCDMA and WLANnetworks, in: Proceedings of IEEE PIMRC07, Greece, 2007.

[12] L. Giupponi, R. Agusti, J.P. Romero, O. Sallent, A framework for JRRMwith resource reservation and multiservice provisioning inheterogeneous networks, Mobile Networks and Applications(MONET) 11 (6) (2006).

[13] T. Bejaoui, N. Nasser, Handover and class-based Call AdmissionControl policy for 4G-heterogeneous mobile networks, IEEE/ACSInternational Conference on Computer Systems and Applications(AICCSA), March 31 2008–April 4 2008, pp 373–380.

[14] K.S. Gilhousen, I.M. Jacobs, R. Padovani, A.J. Viterbi, L.A. Weaver, C.E.Wheatley, On the capacity of a cellular CDMA systems, IEEETransactions on Vehicular Technology 40 (1991) 303–312.

[15] W.S. Jeon, D.G. Jeong, Call admission control for CDMAmobile communication systems supporting multimedia services,IEEE Transactions on Wireless Communications 1 (2002)649–659.

[16] G. Bianchi, Performance analysis of the IEEE 802.11 distributedcoordination function, IEEE Journal on Selected Areas inCommunications 18 (3) (2000) 535–547.

[17] S.K. Lee, K. Sriram, K. Kim, J.H. Lee, Y.H. Kim, N. Golmie, Verticalhandoff decision algorithm for providing optimized performance inheterogeneous wireless networks, IEEE Transactions on VehicularTechnology 58 (2) (2009) 865–881.

[18] A. Hasib, A.O. Fapojuwo, Analysis of common radio resourcemanagement scheme for end-to-end QoS support in multi-serviceheterogeneous wireless networks, IEEE Transactions on VehicularTechnology 57 (4) (2008) 2426–2439.

[19] F. Yu, V.W.S. Wong, V.C.M. Leung, Performance enhancement ofcombining QoS provisioning and location management in wirelesscellular networks, IEEE Transactions on Wireless Communications 4(3) (2005) 943–953.

[20] G. Park, P. Goohyun, Y. Takki, H. Daesik, K. Changeon, A modifiedcovariance-based mobile velocity estimation method for Ricianfading channels, IEEE Communication Letters 9 (8) (2005)706–708.

[21] N. Shenoy, B. Hartpence, A mobility model for cost analysis inintegrated cellular/WLANs, ICCCN (2004) 175–180.

[22] S.M. Ross, Introduction to Probability Models, Academic Press Inc.,1985.

[23] M. Balazinska, P. Castro, Characterizing mobility and network usagein a corporate wireless local-area network, in: Proceedings ofMobiSys, 2003, pp. 303–316.

[24] A. Hasib, A.O. Fapojuwo, A mobility model for heterogeneouswireless networks, IEEE RWS 2008, Orlando, USA, pp. 815–818.

Abdul Hasib received the B.Sc. Eng. fromBangladesh University of Engineering andTechnology, Bangladesh, M.Sc. from Univer-sity of Stuttgart, Germany and the Ph.D. fromUniversity of Calgary, Canada, in 2000, 2002and 2008, respectively, in Electrical Engi-neering. He worked at NEC Network LabsHeidelberg, Germany as an intern. In 2002, hejoined Bangladesh University of Engineeringand Technology as a Lecturer with Institute ofInformation and Communication Technology.His research interests are in mobility and

radio resource management for wireless networks.


Abraham O. Fapojuwo (S’85-M’89-SM’92)received the B.Eng. degree (first class honors)from the University of Nigeria, Nsukka, in1980 and the M.Sc. and Ph.D. degrees inelectrical engineering from the University ofCalgary, Calgary, AB, Canada, in 1986 and1989, respectively. From 1990 to 1992, he wasa Research Engineer with NovAtel Communi-cations Ltd., where he performed numerousexploratory studies on the architectural defi-nition and performance modeling of digitalcellular systems and personal communica-

tions systems. From 1992 to 2001, he was with Nortel Networks, wherehe conducted, led and directed system-level performance modeling and

analysis of wireless communication networks and systems. In January2002, he joined the Department of Electrical and Computer Engineering,University of Calgary, as an Associate Professor. He is also an AdjunctScientist at TRLabs, Calgary. His current research interests include pro-tocol design and analysis for future generation wireless communicationnetworks and systems, and best practices in software reliability engi-neering and requirements engineering. Dr. Fapojuwo is a registered Pro-fessional Engineer in the Province of Alberta.

Cross-layer radio resource management in integrated WWAN and WLAN networks

Documents

Transcript of Cross-layer radio resource management in integrated WWAN and WLAN networks