12. power control and channel allocation in cognitive radio networks with primary users’...

Power Control and Channel Allocationin Cognitive Radio Networks with

Primary Users’ CooperationAnh Tuan Hoang, Member, IEEE, Ying-Chang Liang, Senior Member, IEEE, and

Md Habibul Islam, Member, IEEE

Abstract—We consider a point-to-multipoint cognitive radio network that shares a set of channels with a primary network. Within the

cognitive radio network, a base station controls and supports a set of fixed-location wireless subscribers. The objective is to maximize

the throughput of the cognitive network while not affecting the performance of primary users. Both downlink and uplink transmission

scenarios in the cognitive network are considered. For both scenarios, we propose two-phase mixed distributed/centralized control

algorithms that require minimal cooperation between cognitive and primary devices. In the first phase, a distributed power updating

process is employed at the cognitive and primary nodes to maximize the coverage of the cognitive network while always maintaining

the constrained signal to interference plus noise ratio of primary transmissions. In the second phase, centralized channel assignment is

carried out within the cognitive network to maximize its throughput. Numerical results are obtained for the behaviors and performance

of our proposed algorithms.

Index Terms—Wireless communications, dynamic spectrum access, distributed control, joint power control and channel allocation.

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1 INTRODUCTION

THE traditional approach of fixed spectrum allocation tolicensed networks leads to spectrum underutilization.

In recent studies by the FCC, it is reported that there arevast temporal and spatial variations in the usage ofallocated spectrum, which can be as low as 15 percent [1].This motivates the concepts of opportunistic spectrum accessthat allows secondary cognitive radio networks to oppor-tunistically exploit the underutilized spectrum. In fact,opportunistic spectrum access has been encouraged by bothrecent FCC policy initiatives and IEEE standardizationactivities [2], [3].

On one hand, opportunistic spectrum access can im-prove the overall spectrum efficiency. On the other hand,transmission from cognitive devices can cause harmfulinterference to primary users of the spectrum. Thismotivates our objective of maximizing the throughput of acognitive radio network while maintaining performance ofcoexistent primary users.

In this paper, we consider a point-to-multipoint cogni-tive radio network in which a base station (BS) controlsand supports a set of fixed-location customer premiseequipments (CPEs). The spectrum of interest is dividedinto a set of nonoverlapping, independent channels. Someof these channels are used by a set of point-to-pointprimary transmissions. We are interested in the powercontrol/channel assignment problem for the cognitive

radio network. The objective is to maximize the totalthroughput for the cognitive network while maintaining arequired signal to interference plus noise ratio (SINR) forall primary receivers (PRXs).

We consider both downlink and uplink scenarios in thecognitive network. For the downlink control scenario, wepropose a two-phase downlink mixed distributed/centra-lized control algorithm (DL-MDCA) that requires minimalcooperation between cognitive and primary devices. In thefirst phase of DL-MDCA, BS and primary transmitters(PTXs) participate in a distributed power updating processthat strives to maximize the coverage of the cognitivenetwork. BS and PTXs need to exchange simple controlsignaling to initiate and terminate this power updatingprocess. Apart from that, no further control informationneeds to be exchanged between BS and PTXs. In the secondphase of DL-MDCA, based on the coverage obtained fromthe first phase, BS allocates channels to different CPEs inorder to maximize the total downlink transmission rate.This is achieved by formulating and solving a maximumweighted bipartite matching problem. For the uplinkcontrol scenario, a similar two-phase control algorithm,called UL-MDCA, is proposed. In the first phase, distrib-uted power control is carried out between PTXs and CPEs.In the second phase, centralized channel assignment isapplied within the cognitive network.

It can be noted that, in our proposed system, secondarynodes exercise “cognition” when intelligently adaptingtheir transmit power such that their SINR constraint ismet while not violating the SINR constraint of the primarylinks. This is consistent with the concept of dynamicspectrum access based on constraint in interference tem-perature [4]. In traditional priority-based network, second-ary devices are normally allowed to transmit only when the

348 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 9, NO. 3, MARCH 2010

. The authors are with the Institute for Infocomm Research, A-STAR,01 Fusionopolis Way, #21-01 Connexis, Singapore 138632.E-mail: {athoang, ycliang, habibul}@i2r.a-star.edu.sg.

Manuscript received 8 Aug. 2008; revised 4 Feb. 2009; accepted 1 May 2009;published online 29 July 2009.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference IEEECS Log Number TMC-2008-08-0313.Digital Object Identifier no. 10.1109/TMC.2009.136.

1536-1233/10/$26.00 � 2010 IEEE Published by the IEEE CS, CASS, ComSoc, IES, & SPS

higher priority devices are not transmitting. To implementour proposed system, there should be some mechanisms forcognitive nodes to sense the channels and detect primaryusers. This can be done using different spectrum sensingtechniques such as matched filter, energy detector, andcyclostationary feature detector [5], [6], [7], [8].

The rest of the paper is organized as follows: Workrelated to this paper is described in Section 2. In Section 3,we describe the system model, introduce important nota-tion, and discuss the operation principles of the cognitiveand primary networks. The problem of power control/channel assignment to maximize the downlink throughputof the cognitive radio network is considered in Section 4.Particularly, the DL-MDCA scheme is proposed and someimportant characteristics of this algorithm are discussed. InSection 5, the uplink control scenario is considered and thecontrol algorithm UL-MDCA is proposed. In Section 6, weprovide analysis of the complexity of DL-MDCA andUL-MDCA. To facilitate the study of DL-MDCA andUL-MDCA, in Section 7, we introduce some simpler controlalgorithms. Numerical results showing the behaviors andperformance of our proposed schemes are presented inSection 8. Finally, we conclude the paper and outline futureresearch directions in Section 9.

2 RELATED WORK

In our previous work [9], [10], [11], we consider relatedproblems of centralized power/channel allocation to max-imize the number of users supported in multichannelcognitive radio networks. However, [9], [10], [11] only focuson the downlink scenario and assume that primary users donot cooperate with the cognitive radio users in any way. Thispaper considers both downlink and uplink scenarios andhighlights the significant benefits when primary userscooperate with cognitive users in a simple, distributedmanner. In [12], the authors study problems of secondaryspectrum access with minimum SINR guarantee and inter-ference temperature constraint. They consider both scenar-ios, when all the secondary links can be supported and whennot all the secondary links can be supported with their SINRrequirement. In the first scenario, the control objective is tomaximize the total transmission rate of the secondary users,while in the second scenario, an access control problem alsoneeds to be dealt with. A control problem similar to [12] isalso studied in [13]. We note that, unlike this paper, in both[12] and [13], no cooperation from primary users is assumed.Furthermore, [12], [13] focus on single-channel scenario, andtherefore, channel allocation is not of concern.

Other works that consider channel assignment formultichannel cognitive radio networks include [14], [15],[16]. In [14], Wang and Liu consider a problem ofopportunistically allocating multiple licensed channels toa set of cognitive stations so that the total number ofchannel usages is maximized. In [15], Zheng and Pengconsider a similar problem and introduce a reward functionthat is proportional to the coverage areas of base stations.They also allow the interference effect to be channel specific.The problem in [16] is for a cognitive-radio, multihopwireless network. It should be noted that transmit powercontrol is not considered in [14], [15], [16]. Instead,protection of primary users is based on physical separationof communication entities. In particular, it is assumed that if

a node is beyond a certain distance from a primary user,then no harmful interference will be caused. The channelallocation problems in [14], [15] are then formulated asgraph coloring problems. The problem in [16] is solved viainteger linear programming.

Our paper is also related to [17] and [18], where aproblem of joint channel assignment and transmit powercontrol for multichannel wireless ad hoc networks isconsidered. As the work is not for cognitive radio,protecting primary users is not of concern. In a broadercontext, our paper is related to work on conventionalproblem of power control in cellular networks such as [19],[20], [21]. Compared to this related work, the problemconsidered in this paper is different due to the need toprotect primary users and the joint control over multiplechannels. In our system model, different primary users maytransmit and receive on different channels. The channelusage pattern of primary users, coupled with their loca-tions, makes the spectrum available for cognitive usageirregular. This kind of spectrum irregularity, across availablechannels and BS-CPE links, makes it necessary to carry outchannel assignment and power control jointly for allchannels in order to achieve a good system throughput.

3 SYSTEM MODEL

We consider an opportunistic spectrum access scenario asdepicted in Fig. 1. The spectrum of interest is divided intoK channels that are licensed to a primary network ofM PTX-PRX links. Each of the M PTX-PRX links occupiesone of the K channels (this implies M � K). In the samearea, a secondary cognitive radio network is deployed. Thiscognitive network consists of a BS serving a set of N CPEsby opportunistically making use of the K channels. Weconsider both downlink (from BS to CPEs) and uplink (fromCPEs to BS) scenarios in the secondary cognitive network.We assume that the BS can transmit and receive on up toK channels at a time while each CPE can transmit or receiveon only one channel at a time. Furthermore, each channelcan only be used by one CPE (in either downlink or uplink)at a given time.

HOANG ET AL.: POWER CONTROL AND CHANNEL ALLOCATION IN COGNITIVE RADIO NETWORKS WITH PRIMARY USERS’ COOPERATION 349

Fig. 1. Deployment of a cognitive radio network. The cognitive networkconsists of one BS serving multiple fixed-location CPEs. The primarynetwork is modeled as a set of point-to-point PTX-PRX links.

3.1 Notation

To facilitate further discussion, let us define the following

notation (see Table 1):

. Gppi;j;c denotes the channel gain from PTX i to PRX j

on channel c, 1 � i; j �M, 1 � c � K.. Gss

i;j;c denotes the channel gain from secondary

node i to secondary node j on channel c. Here, we

denote BS as secondary node 0 and N CPEs as

secondary nodes 1 to N , 0 � i; j � N .. Gsp

i;j;c denotes the channel gain from secondary

node i to PRX j on channel c, 0 � i � N , 1 � j �M.. Gps

i;j;c denotes the channel gain from PTX i to

secondary node j on channel c, 1 � i �M, 0 � j � N .. Pp

i;c denotes the transmit power of PTX i on channel c.

If PTX i does not transmit on channel c, then Ppi;c ¼ 0.

. Psi;c denotes the transmit power of secondary node i

on channel c. If secondary node i does not transmiton channel c, then Ps

i;c ¼ 0.. No denotes the noise spectrum density at BS, CPEs,

and PRXs. Note that we assume the noise figure

being the same for all receivers just for the sake of

brevity. The results of the paper can be applied

directly to the case when different receivers experi-

ence different noise figures.

3.2 Downlink Scenario in Cognitive Network: SINRat PRXs and CPEs

Consider the downlink scenario in the cognitive radio

network. For each channel c, let �pdi;c denote the SINR

experienced by PRX i. �pdi;c can be calculated as

�pdi;c ¼Ppi;cG

ppi;i;c

No þPM

j¼1;j6¼i Ppj;cG

ppj;i;c þ Ps

0;cGsp0;i;c

; 1 � i �M; ð1Þ

where it should be noted that Ps0;c is the transmit power of BS

on channel c and Gsp0;i;c is the channel gain from BS to PRX i.

Similarly, let �sdn;c denote the SINR experienced by CPE n.

�sdn;c can be calculated as

�sdn;c ¼Ps

0;cGss0;n;c

No þPM

j¼1 Ppj;cG

psj;n;c

; 1 � n � N: ð2Þ

3.3 Uplink Scenario in Cognitive Network: SINR atPRXs and BS

Consider the uplink scenario in the cognitive radio network.

Assuming that CPE n (1 � n � N) is assigned to transmit on

channel c and letting �pui;n;c denote the SINR experienced by

PRX i, �pui;n;c can be calculated as

�pui;n;c ¼Ppi;cG

ppi;i;c

No þPM

j¼1;j6¼i Ppj;cG

ppj;i;c þ Ps

n;cGspn;i;c

; 1 � i �M: ð3Þ

At the same time, the SINR at BS, denoted by �sun;c, can be

calculated as

�sun;c ¼Psn;cG

ssn;0;c

No þPM

j¼1 Ppj;cG

psj;0;c

: ð4Þ

3.4 Protecting Primary Users

We assume that, for appropriate performance of the

primary network, the received SINR at each PRX must

be above a predefined value of �p. In particular, let ��c

denote the set of all PRXs that receives on channel c, when

the cognitive radio network operates in the downlink

scenario, we must have

�pdi;c � �p; 8i 2 ��c: ð5Þ

When the cognitive radio network operates in the uplink

scenario and CPE n is assigned to transmit on channel c, we

must have

�pui;n;c � �p; 8i 2 ��c: ð6Þ


TABLE 1Important Notation and Acronyms

To support the operation of the cognitive network, the

primary network must be able to tolerate a certain level of

interference from cognitive transmissions. We assume that

for each channel c and PTX i transmitting on c, i.e., i 2 ��c,

there exists a transmit power ~Ppi;c such that

~Ppi;cG

ppi;i;c

No þPM

j¼1;j6¼i~Ppj;cG

ppj;i;c þ �

� �p; 8i 2 ��c; ð7Þ

where � is a positive constant. Essentially, this assumption

means that, apart from the interference caused by other

primary transmissions, each primary receiver can tolerate

an extra interference, e.g., from secondary transmission,

equal to �.

3.5 Transmission Rates for Secondary Connections

Using adaptive coding and/or modulation, the transmis-sion rate between BS and each CPE (in either downlink oruplink direction) can be varied according to the receivedSINR. For a given channel, we assume that the transmissionrate between BS and CPE with SINR of � can be written as

uð�Þ ¼ 0; if � < �s;fð�Þ; if � � �s;

�ð8Þ

where �s is the SINR threshold below which no transmis-

sion is possible between BS and CPEs. When the SINR is

� � �s, the corresponding downlink transmission can be

carried out at rate fð�Þ. Function fð�Þ depends on various

factors such as available coding/modulation schemes and

bit error rate requirement. However, in all cases, fð�Þ is

monotonically nondecreasing.

4 DOWNLINK THROUGHPUT MAXIMIZATION

In this section, let us consider the problem of power control

and channel assignment to maximize the downlink

throughput of the cognitive radio network while protecting

K PTX-PRX links. The problem of maximizing the uplink

throughput is considered in Section 5.Let an;c be a binary variable that indicates whether

channel c is assigned to the downlink transmission from BS

toward CPE n. In particular, an;c is set to 1 if channel c is

assigned to the downlink transmission toward CPE n.

Otherwise, an;c is set to 0. The problem of power control/

channel assignment to maximize the total downlink

transmission rate of the cognitive network can be stated as

arg maxPs

0;c;P pi;c;an;c

XKc¼1

XNn¼1

u��sdn;c�an;c; ð9Þ

subject to:

0 � Ps0;c � Ps; 8c 2 f1; . . . ; Kg; ð10Þ

0 < Ppi;c � Pp; 8i 2 ��c; 8c 2 f1; . . . ; Kg; ð11Þ

�pdi;c � �p; 8i 2 ��c; 8c 2 f1; . . . ; Kg; ð12Þ

XKc¼1

an;c � 1; 8n 2 f1; . . . ; Ng; ð13Þ

XNn¼1

an;c � 1; 8c 2 f1; . . . ; Kg: ð14Þ

In (10), Ps is the maximum transmit power for BS on eachchannel, and in (11), Pp is the maximum transmit power foreach PTX. These maximum powers can be regarded as theintrinsic limits for the type of secondary/primary devices,which are normally set out by spectrum regulators. Inequal-ity (12) is due to the SINR constraint for primary links.Inequality (13) is due to the fact that each CPE can onlyreceive on one channel at a time and (14) is because two CPEscannot simultaneously share a single channel.

4.1 General Approach

As the K channels are independent of each other and therate function fð:Þ is monotonically nondecreasing, withoutloss of optimality, the downlink throughput maximizationproblem can be separated into the following two phases:

. Phase 1—Power Control: For each channel, find themaximum transmit power of BS, together with thetransmit powers of PTXs, so that the SINR constraintsof all PRXs receiving on that channel are met.

. Phase 2—Channel Assignment: After Phase 1, giventhe maximum transmit powers of BS on K channels,carry out channel assignment for N CPEs so that thetotal downlink transmission rate is maximized.

For Phase 1, one control option is to carry out centralizedpower calculation for BS and all PTXs. In our previousworks in [9], [10], [11], we follow this approach and applythe Perron-Frobenious theorem ([22]) to obtain Pareto-optimal transmit power vectors. However, the centralizedpower control approach requires knowledge of all channelgains from BS to PRXs and from PTXs to CPEs. This meansa great deal of cooperation between primary and cognitivenetworks. In this paper, we follow another power controlapproach, which is carried out in distributed manner andrequires minimal cooperation between the two networks.On the other hand, as Phase 2 only involves BS and CPEs, acentralized channel assignment scheme is appropriate. Weterm our control approach the Downlink Mixed Distrib-uted/Centralized Algorithm (DL-MDCA). Each phase ofDL-MDCA is discussed below.

4.2 Phase 1 of DL-MDCA: Distributed Power Control

For Phase 1, we propose a synchronous downlink dis-tributed power updating (DL-DPU) process that strives tomaximize the coverage of BS while always guaranteeing theSINR constraint of all PRXs. The DL-DPU process is appliedto one channel at a time. For channel c, 1 � c � K, thefollowing actions are carried out:

. Initialization: Either BS or one of the PTXs operatingon channel c initiates the power updating process bybroadcasting some special tone. All PTXs thattransmit on channel c, together with BS, mustparticipate in the power updating process. PTX i,


8i 2 ��c, and BS set their transmit powers to theinitial values of Pp

i;cð0Þ and Ps0;cð0Þ, respectively.

. Power updating: At step k, the following activities arecarried out: 1) BS and PTXs transmit pilot signals atthe set power levels ofPs

0;cðkÞ andPpi;cðkÞ, respectively.

2) PRXs estimate their SINR and feed back to theircorresponding PTXs. 3) PTX i and BS update theirtransmit powers according to:

Ppi;cðkþ 1Þ ¼ Pp

i;cðkÞ��p

�pdi;cðkÞ; i 2 ��c;

P s0;cðkþ 1Þ ¼ Ps

0;cðkÞ�:ð15Þ

Here, �pdi;cðkÞ is the SINR at PRX i after step k and canbe calculated using (1). In addition, � is a powerscaling factor, which is slightly greater than one.

. Termination: The DL-DPU process will be terminatedif at least one of the following conditions is true:

- The SINR experienced by at least one of thePRXs goes below the threshold �p.

- At least one of the transmitters (BS and PTXs)has its transmit power that approaches themaximum transmit power constraint.

A node can terminate the DL-DPU process bybroadcasting some special tone (control message).

We assume that the special tone to initiate Phase 1 isbroadcasted on the same channel c. This can be achieved byperiodically reserving small time slots so that primary orcognitive nodes can broadcast the tone. During these timeslots, all nodes will not carry out normal data transmission.Nodes that wish to broadcast the special initiating tone in aparticular reserved time slot can do so in a random accessmanner, e.g., using p-persistent random access or schemesimilar to contention-based ranging in IEEE 802.16 [23]. Thishelps avoid collisions of multiple concurrent tones. It isimportant to note that due to the simple nature of the tone,the overhead of reserving time slots to broadcast the toneshould be negligible.

When one node transmits the tone to initiate the powerupdating process, it forces other nodes to cease normalcommunication to participate in the power updating process.This can affect the primary and secondary performances,especially in mobile environment when frequent powerupdates need to be carried out. To keep this negative effectunder control, one or a combination of the followingapproaches can be employed: 1) a restriction can be set sothat only primary users can transmit the tone to initiate thepower updating process; 2) we can also set the limit on howfrequent the power updating process can be carried out.

As can be seen from (15), PTX i updates its transmitpower to aim at the SINR value of ��p. On the other hand,BS keeps on increasing its transmit power in order toincrease the cell coverage. A pseudocode for the DL-DPUprocess is given in Fig. 12. Next, let us prove someimportant properties of the proposed DL-DPU process

Proposition 1. The DL-DPU process will be terminated after afinite number of iterations.

Proof. The transmit power of BS starts from an initial value

of Ps0;cð0Þ and is increased by factor � > 1 after each

iteration. So the power updating process will stop after at

most dlog�ð Ps

Ps0;cð0ÞÞe iterations. tu

Proposition 2. If the initial transmit powers Ppi;cð0Þ and Ps

0;cð0Þare selected such that �pdi;cð0Þ � �p; 8i 2 ��c, then �pdi;cðkÞ ��p; 8k � 1.

Proof. The proof is by induction. Suppose that for some

k � 0, �pdi;cðkÞ � �p. We have

�pdi;cðkþ 1Þ ¼

¼Ppi;cðkþ 1ÞGpp

i;i;c

No þPM

j¼1;j6¼i Ppj;cðkþ 1ÞGpp

j;i;c þ Ps0;cðkþ 1ÞGsp

0;i;c

�Ppi;cðkÞ��p=�

pdi;cðkÞG

ppi;i;c

No þPM

j¼1;j6¼i Ppj;cðkÞ�G

ppj;i;c þ Ps

0;cðkÞ�Gsp0;i;c

¼ �p

�pdi;cðkÞ�Pp

i;cðkÞGppi;i;c

No þ ��PM

j¼1;j6¼i Ppj;cðkÞG

ppj;i;c þ Ps

0;cðkÞGsp0;i;c

�� p

�pdi;cðkÞPpi;cðkÞG

ppi;i;c

No þPM

j¼1;j6¼i Ppj;cðkÞG

ppj;i;c þ Ps

0;cðkÞGsp0;i;c

¼ �p

�pdi;cðkÞ�pdi;cðkÞ ¼ �p:

ð16Þ

Note that the first inequality in (16) follows from

Ppj;cðkþ 1Þ ¼ �Pp

j;cðkÞ�p

�pdj;cðkÞ� �Pp

j;cðkÞ; 81 � j �M; ð17Þ

and the last inequality in (16) follows from � > 1. tuProposition 2 states that if we start with the initial

transmit powers for BS and all PTXs so that the SINRconstraints of all PRXs are met, then during the powerupdating process, the SINR constraints of all PRXs arealways maintained. Based on (7), we can set Pp

i;cð0Þ ¼ ~Ppi;c.

Then, if Ps0;c is set to a sufficiently small value such that

Ps0;cG

sp0;i � �, from (7), we will have �pdi;cð0Þ � �p; 8i 2 ��c. In

case the SINR constraint of a PRX is violated at initialization(e.g., due to the change in channel condition), the powerupdating process will be immediately terminated. The BSthen can further reduce its initial power level in subsequentpower updating process.

Proposition 3. The SINR experienced by each CPE increasesafter each power updating step.

Proof. Using (2), for 1 � n � N , we have

�sdn;cðkþ 1Þ ¼Ps

0;cðkþ 1ÞGss0;n;c

No þPM

i¼1 Ppi;cðkþ 1ÞGps

i;n;c

�Ps

0;cðkÞ�Gss0;n;c

No þPM

i¼1 Ppi;cðkÞ�G

psi;n;c

>Ps

0;cðkÞGss0;n;c

No þPM

i¼1 Ppi;cðkÞG

psi;n;c

¼ �sdn;cðkÞ;

ð18Þ

where the first inequality is due to (17) and the lastinequality follows from � > 1. tu


Proposition 3 states that the coverage, i.e., number of CPEsthat BS can cover, is nondecreasing after each iteration.

4.3 Phase 2 of DL-MDCA: Centralized ChannelAssignment

After the power updating process, for each channel c, BS has amaximum transmit power Ps

0;c. Associated with this transmitpower are the SINRs experienced byN CPEs. The problem ishow to assign K channels to different CPEs so that the totaldownlink throughput is maximized. This is achieved by firsttransforming the problem into a weighted bipartite matchingand then finding a maximal weighted match.

The weighted bipartite graph is formed as follows: First,represent the N CPEs by a set of vertices, which isconnected to another set of vertices representing the Kchannels. An edge exists between the vertex representingCPE n and the vertex representing channel c if and only if,for channel c, the SINR at CPE n is not less than �s. For eachedge, assign an weight that is equal to the correspondingtransmission rate calculated in (8). An example of such anweighted bipartite graph is given in Fig. 2.

The problem of channel assignment to maximize the totaldownlink transmission rate is equivalent to the problem offinding a maximal matching for the correspondingweighted bipartite graph. In this paper, we obtain maximalweighted matching of a bipartite graph by the followingprocedure [24]:

Maximal Weighted Bipartite Matching Procedure

. Step 1: Start with an empty match, i.e., without anyedge selected.

. Step 2: Find a maximum augmenting path for thecurrent match. An augmenting path is a path withedges alternate between matched and unmatched.The score of an augmenting path is equal to the sumof weights of unmatched edges subtracted by thesum of weights of matched edges. A maximumaugmenting path is the one with the maximumscore. If this score of the maximum augmenting pathis not positive, then finish as the current match ismaximal. Otherwise, go to Step 3.

. Step 3: Flip the maximum augmenting path obtainedin Step 2, i.e., change unmatched edges of the path tomatched and matched edges of the path to un-matched. Go back to Step 2 to find anothermaximum augmenting path and continue.

In our DL-MDCA algorithm, Phase 1 of distributed powercontrol does not require the assumption that each CPE onlyoperates on one channel at a time. This assumption is used inPhase 2 to simplify the channel assignment process. How-ever, Phase 2 can also be readily extended to cover multiple-channel operation case. In particular, if each CPE can operateon up to L channels, then when forming the bipartite graph,we just need to represent each CPE by L vertices and the restof the maximal matching operation is unchanged. It shouldalso be noted that, when channels change, the SINRconstraints for primary users can be violated. In that case,the primary users should send out warning tone andprobably restart the power updating process.

4.4 Fairness Considerations

It can be noted that by maximizing the transmit power ofthe BS, Phase 1 maximizes the set of CPEs that can bepotentially assigned channels. Given that, how fair thechannels are assigned to CPEs depends on specificalgorithm in Phase 2. In this paper, we focus on throughputmaximization and achieve that with bipartite matching. Onthe other hand, if the focus is fairness, we need to frequentlyreassign the channels. One way to do that is to divide the setof CPEs into multiple subsets, and consider these subsets ina round-robin manner. Given a particular subset of CPEs,we can then apply bipartite matching to maximize theachievable throughput for such subset.

5 UPLINK THROUGHPUT MAXIMIZATION

Now, let us consider the problem of power control/channelassignment for maximizing the uplink throughput of thecognitive radio network. Similar to the downlink scenario,we propose a two-phase Uplink Mixed Distributed/Centralized Algorithm (UL-MDCA). In the first phase ofUL-MDCA, distributed power control is carried out amongCPEs and PTXs in order to meet their SINR constraints. Inthe second phase of UL-MDCA, centralized channel assign-ment is applied within the cognitive network to maximizethe uplink throughput. It should be noted that, unlike thedownlink control scenario, in the uplink, the joint powercontrol/channel assignment is coupled, and our two-phaseapproach is not optimal. However, this approach makes thecontrol problem much more manageable.

5.1 Phase 1 of UL-MDCA: Distributed Power Controlfor Uplink Scenario

For the system model considered in this paper, uplinkpower control is significantly more complicated thandownlink power control. In the downlink case, there isonly one secondary transmitter, i.e., the BS, which interferesprimary links. On the other hand, for the uplink case,different CPEs act as different transmitters and interferewith PRXs in different ways.

We assume that, for each uplink connection, as long as theSINR (at BS) is above the required threshold of �s, thetransmission rate is fixed at a predetermined value, i.e.,fð�Þ ¼ r; r > 0. In other words, we do not consider rateadaptation for uplink transmissions in the cognitive net-work. Therefore, maximizing uplink throughput is equiva-lent to maximizing the number of uplink connections withsatisfied SINR. We further assume that there is at most one


Fig. 2. Representing the coverage within one cell as a bipartite graph.The number on each link is its capacity.

PTX-PRX link operating on each of the K channels. With

these assumptions, we propose the following Uplink

Distributed Power Control (UL-DPU) process that is applied

to one channel at a time. For channel c, c 2 f1; . . .Kg, let PTX

i be the primary transmitter that operates on channel c. The

UL-DPU process is carried out in two rounds as follows:Round 1: The CPEs are considered one at a time, starting

from CPE 1. CPE n and PTX i carry out a distributed power

updating process in an iterative manner as follows:

. Initialization: Set the initial transmit power of CPE nto Ps

n;cð0Þ and initial transmit power of PTX i to Pp.Similar to the case of downlink control, the initialtransmit power of CPE n is chosen small enough sothat Ps

n;cð0ÞGspn;i;c < �.

. Iterative Power Updating: At step k, PTX i and CPE nupdate their transmit powers as

Ppi;cðkþ 1Þ ¼ max Pmin

n ; Ppi;cðkÞ

��p

�pui;n;cðkÞ

( );

P sn;cðkþ 1Þ ¼ Ps

n;cðkÞ�;ð19Þ

where Pminn is the minimum transmit power for PTX

i when it carries out power updating with CPE n.

We set Pmin1 ¼ 0 and calculate Pmin

n (n > 1) as

explained next.. Termination: At step k, the power updating process

between CPE n and PTX i will terminate if at leastone of the following conditions is true:

- The SINR at PRX i is below the required value of�p. In this case, the uplink connection betweenCPE n and BS will not be supported. We then set

Pminnþ1 ¼ Pmin

n : ð20Þ

- The transmit power of either CPE n or PTX iexceeds the maximum allowable value (of Ps

and Pp, respectively) before CPE n can achievethe required SINR. In this case, the uplinkconnection between CPE n and BS will not besupported. We then set Pmin

nþ1 according to (20).- The SINR at BS (corresponding to the transmis-

sion from CPE n) reaches the required value of�s. In this case, CPE n records its transmit powerPsn;c ¼ Ps

n;cðkÞ, BS also remembers this CPE byincluding it into a set ��c. We then update

Pminnþ1 ¼ P

pi;cðkÞ: ð21Þ

Note that Pminn is an operating parameter of PTX i. So

when the iteration terminated, BS needs to send a one-bit

update to PTX-PRX i to inform them about the outcome

of the power updating process, i.e., whether the uplink

connection from CPE n can be supported. Based on this

one-bit information, PTX i will set Pminn according to

either (20) or (21). After the power updating process of

CPE n is terminated, BS, acting as the master, transmits a

control message to ask CPE nþ 1 to start its power

updating process.Round 2: After all N CPEs have been processed, a final

round of power updating is carried out as follows:

. PTX i keeps transmitting at power PminNþ1, i.e., the

transmit power it obtains after carrying out dis-tributed power update with CPE N .

. For each CPE n that has been able to achieve therequired SINR of �s, i.e., n 2 ��c, BS measures its SINRwhen PTX i transmits at power Pmin

Nþ1 and instructsCPE n to carry out a final power update according to

Psn;c ¼

Psn;c

�s

�sun;cif Ps

n;c

�s

�sun;c� Ps;

0 if Psn;c

�s

�sun;c> Ps:

8>>><>>>:

ð22Þ

Note that in (22), �sn;c is the SINR experienced at BS

when CPE n and PTX i transmit at powers Psn;c and

PminNþ1, respectively.

A pseudocode for the UL-DPU procedure is given in

Fig. 13.We state and prove the following important character-

istics of the UL-DPU process:

Proposition 4. Given that PTX i transmits at power PminNþ1, if

CPE n 2 ��c is assigned channel c and transmits at power

Psn;c > 0, the corresponding SINR at BS is �s.

Proof. The poof follows directly from (22) and the fact

that SINR at BS scales linearly with the transmit power

of CPEs. tuProposition 5. For each channel c, no matter what CPE in the set

��c is assigned the channel for uplink transmission, the UL-DPU

procedure ensures that the SINR of the PTX-PRX link operating

on channel c is always above the required threshold of �p.

Proof. First, as the initial transmit power of CPE n is chosen

so that Psn;cð0ÞG

spn;i;c < �, from (7), we have �pui;n;cð0Þ � �p.

Then, similar to the proof of Proposition 2, it can be

shown that during Round 1 of UL-DPU, the SINR of the

PTX-PRX link is always above the required threshold of

�p. What we need to prove is that Round 2 of UL-DPU

also ensures that the SINR of the PTX-PRX link to be

above the required threshold. If CPE n, n 2 ��c, transmits

on channel c at power Psn;c and PTX i transmits at power

PminNþ1, the SINR at PRX i is

�pui;n;c ¼PminNþ1G

ppi;i;c

No þ Psn;cG

spn;i;c

¼PminNþ1G

ppi;i;c

No þ Psn;c

�s

�sun;cGspn;i;c

¼PminNþ1

Pminn

Pminn Gpp

i;i;c

No þ Psn;c

�s

�sun;cGspn;i;c

�Pminn Gpp

i;i;c

No þ Pminn

PminNþ1

Psn;c

�s

�sun;cGspn;i;c

;

ð23Þ


where the last inequality follows from PminNþ1 � Pmin

n . We

further note that

�sun;c ¼Psn;cG

ssn;0;c

No þ PminNþ1G

psi;0;c

ð24Þ

and

�s �Psn;cG

ssn;0;c

No þ Pminn Gps

i;0;c

; ð25Þ

as it is assumed that CPE n can achieve its SINRconstraint when transmitting at power Ps

n;c while PTX i istransmitting at power Pmin

n . Therefore,

�s

�sun;c�No þ Pmin

Nþ1Gpsi;0;c

No þ Pminn Gps

i;0;c

�PminNþ1

Pminn

; ð26Þ

where the last inequality follows from PminNþ1 � Pmin

n .

Substituting (26) into (23) gives us

PminNþ1G

ppi;i;c

No þ Psn;cG

spn;i;c

�Pminn Gpp

i;i;c

No þ Psn;cG

spn;i;c

� �p: ð27Þ

So, the proof is completed. tu

5.2 Phase 2 of UL-MDCA: Centralized ChannelAssignment for Uplink Scenario

In this phase, channel assignment is carried out in a

centralized manner in the same way as it is done in Phase 2

of DL-MDCA scheme (Section 4.3). In particular, a bipartite

graph is first constructed, and after that, maximal bipartite

matching is applied to get the optimal channel allocation.

6 COMPLEXITY ANALYSIS FOR DL-MDCA AND

UL-MDCA

Let us look at the complexity of DL-MDCA and UL-MDCA.

For both algorithms, we can separate the overall complexity

into that of Phase 1 and Phase 2.

6.1 Complexity of Phase 1

Consider the DL-DPU process for the downlink scenario.For each channel, the number of power updating iterationsis bounded and does not depend either on the number ofnodes in the cognitive network or the number of PTX-PRXlinks (see Proposition 1). Therefore, the complexity of theDL-DPU process is OðKÞ, where K is the number ofchannels in the system.

Consider the UL-DPU process for the uplink scenario.

Again, for each channel and each CPE, the number of

power updating iterations is upper bounded by a constant.

Therefore, the complexity of UL-DPU process is OðNKÞ.

6.2 Complexity of Phase 2

The complexity of Phase 2 of either DL-MDCA or UL-

MDCA is due to the bipartite matching process. As it is well

known in graph theories [24], the complexity is

OðjV j2 þ jV kEjÞ, where jV j is the number of vertices and

jEj is the number of edges in the corresponding bipartite

graph. The number of vertices in our problem is equivalent

to the number of CPEs, i.e.,N . The number of edges is upper

bounded by NK. Therefore, the complexity of Phase 2 ofDL-MDCA and UL-MDCA is OðN2KÞ.

If we combine the complexity of Phase 1 and Phase 2, theoverall complexity of both DL-MDCA and UL-MDCAscales as OðN2KÞ.

7 OTHER CONTROL ALGORITHMS

It can be noted that two basic components of the proposedDL-MDCA and UL-MDCA schemes are the (limited)cooperation from primary devices during the power updat-ing process in Phase 1 and the centralized maximal bipartitematching employed for channel assignment in Phase 2. Weare interested in understanding the impacts of these twocomponents in the overall performance of DL-MDCA andUL-MDCA. To study these impacts, we consider the othercontrol algorithms given below.

7.1 Noncooperative, Simple Matching (NCSM)Algorithm

The NCSM algorithm can be used in both downlink anduplink scenarios. By noncooperative, we mean that duringthe power control process in Phase 1 (of either downlink oruplink scenario), PTXs do not adjust their transmit powertogether with BS or CPEs. Instead, each PTX fixes itstransmit power at the maximum value of Pp. BS or a CPEthen tries to maximize its transmit power on each channel,subject to the SINR constraints of all cochannel PRXs. Bysimple matching, we mean that channel assignment inPhase 2 (of either downlink or uplink scenario) is carriedout in a simple manner. In particular, CPEs are processedone by one according to a random order. For each CPE thathas not been assigned any channel, we just randomly pickone channel that this CPE can operate at (i.e., with SINRgreater than �s) and assign to it.

By comparing the performance of DL-MDCA or UL-MDCA to that of NCSM, we can see the combined impact ofhaving primary cooperation in Phase 1 and maximalbipartite matching in Phase 2.

7.2 Noncooperative, Maximal Matching (NCMM)Algorithm

The NCMM algorithm can be used in both downlink anduplink scenarios. Phase 1 of this NCMM scheme is similar tothat of NCSM scheme described above, i.e., there is nocooperation from primary transmitters. On the other hand,in Phase 2, maximal bipartite matching is employed foroptimal channel assignment. By comparing the performanceof DL-MDCA or UL-MDCA to that of NCMM, we can see theimpact of having primary users’ cooperation in Phase 1.

7.3 Limited-Cooperation, Simple Matching (LCSM)Algorithm

The LCSM algorithm can be used in both downlink anduplink scenarios. Phase 1 of this LCSM scheme is similar tothat of DL-MDCA or UL-MDCA, i.e., PTXs participate inthe distributed power updating process described inSection 4.2. On the other hand, for Phase 2, simple matchingscheme as described for NCSM is employed. By comparingthe performance of DL-MDCA or UL-MDCA to that ofLCSM, we can see the impact of carrying out optimalbipartite matching in Phase 2.


7.4 Centralized Optimal Control Algorithm

In this algorithm, primary and secondary users measureand report all channel conditions to one central node, e.g.,the BS or a primary node. This central node then calculatesand decides the transmit power levels for all the transmit-ters. In other words, centralized power control is carried outin Phase 1. In phase 2, maximal bipartite matching isemployed for optimal channel assignment.

8 NUMERICAL RESULTS AND DISCUSSION

8.1 Simulation Model

We consider a square service area of size 1;000 m� 1;000 min which a cognitive radio network is deployed. A BS isdeployed at the center of the cell to serve a set of CPEs. Thetotal number of CPEs is N ¼ 10. The number of PTX-PRXlinks is set to 5 and 10. All CPEs and PTXs are randomlydeployed across the entire service area with a uniformdistribution. Then each PRX is deployed such that the offsetsof its horizontal and vertical coordinates, relative to thecoordinates of the corresponding PTX, are uniformlydistributed within the range [50 m, 100 m]. This deploymentmodel represents a practical scenario in the currently beingdeveloped IEEE802.22 WRAN standard, which allowscognitive BS and CPEs to share spectrum with short-rangedincumbent wireless microphone devices [3]. A samplenetwork, with 10 CPEs and 5 PTX-PRX links is given in Fig. 1.

We model an orthogonal frequency-division multipleaccess (OFDMA) system in which the entire bandwidth isdivided into 48 subcarriers. Each subcarrier is regarded asone channel in our power control/channel allocationschemes. The fading channel is represented by a six-tapchannel, with exponential decay factor. Although there are48 channels (subcarriers), we assume that only 10 of themare considered for sharing between primary and secondarynetworks. These 10 subcarriers are equally spaced within theavailable bandwidth. The path loss exponent is taken to be 4.The noise power density at each CPE isNo ¼ �100 dBm. Therequired SINR for each CPE is �s ¼ 15 dB. The requiredSINR for each PRX is varied from 5 to 30 dB. The maximumtransmit power on each channel for BS and PTX is 50 mW.For the distributed power updating process, we set theinitial transmit power for secondary nodes (BS or CPEs) tobe 0.5 mW and for PTXs to be 50 mW. The scaling factor

used in the power updating process (Section 4.2) is� ¼ 1:259 � 1 dB. In our simulation, we assume that oneach channel, there is at most one PTX-PRX link.

For the transmission rate function discussed in Sec-tion 3.5, we use the approximation in [25], i.e., fð�Þ ¼ ð �0:6Þ

1=3.We obtained similar results for other approximations of therate function.

8.2 Behaviors of DL-MDCA—Downlink Scenario

Let us fist look at the behaviors of the distributed powerupdating process proposed in Section 4.2 (for downlinkscenario). In Fig. 3, we plot the SINRs experienced by a PRXand different CPEs on a particular channel during adistributed power updating process. As can be seen, as PTXstarts at its maximum transmit power, the initial SINR of thePRX is much higher than the constrained value of 15 dB. Onthe other hand, the initial SINRs of all CPEs are very low.Then, when the power updating process is employed, theSINR at PRX quickly converges to the target value, which is16 dB in this case. Note that the SINR of PRX converges to16 dB instead of the constrained value of 15 dB due to the useof power scaling factor � ¼ 1:259 � 1 dB. As can be seen inFig. 3, the SINRs of CPEs increase gradually after eachiteration. Some CPEs eventually see their SINRs cross thecutoff value of 15 dB while others never do. In Fig. 3, it is alsointeresting to observe that some CPEs may start at lowerinitial SINRs, however, their SINRs grow quicker and surpassthat of other CPEs. This is due to the variations in channelgains from BS and PTXs to different CPEs. We observed thatwith the given simulation parameters, it took 5-20 iterationsfor the power updating process to terminate.

8.3 Performance of DL-MDCA—DownlinkThroughput

In Figs. 4 and 5, we plot the percentage gains in the totaldownlink throughput of Optimal, DL-MDCA, NCMM, andLCSM, relative to that of NCSM. Note that Optimal schemeis based on centralized control as discussed in Section 7.4.Fig. 4 is for the case when there are five PTX-PRX links andFig. 5 is for 10 PTX-PRX links. As can be seen, theperformance gain of DL-MDCA is very significant andranges from 60 to 160 percent. This shows the combined


Fig. 3. Behavior, in terms of received SINR at CPEs and PRXs, whenthe distributed power control scheme is employed.

Fig. 4. Percentage gains in total downlink transmission rate for optimal,DL-MDCA, LCSM, and NCMM, relative to NCSM. Number of primarylinks ¼ 5.

impacts of having PTXs participating in the power updatingprocess in Phase 1 and carrying out maximal matching inPhase 2. It can also be noted that optimal centralized controlscheme performs slightly better than DL-MDCA. However,as discussed in Section 7.4, this optimal scheme requiresknowledge of all channel conditions for a central node tocalculate and set the transmit power levels for all secondaryand primary transmitters.

When comparing the performance of DL-MDCA andNCMM in Figs. 4 and 5, it is evident that having primaryuser’s cooperation in Phase 1 is important when the SINRconstraint of PRXs is low. However, at high SINR constraintfor PRXs, there is not much gain obtained from PTXs’cooperation. This effect is expected, as when the SINRrequirement of PRXs is low, PTXs can cooperate more byreducing their transmit powers. Also, from Figs. 4 and 5, itcan be observed that the cooperation from PTXs has morepositive impact when the number of primary links increasesfrom 5 to 10. This is because with more primary links, it isimportant that PTXs adjust their power to reduce inter-ference caused to CPEs.

When comparing the performance of DL-MDCA andLCSM in Figs. 4 and 5, it can be noted that carrying outmaximal weighted bipartite matching in Phase 2 gives

significant performance gain for DL-MDCA. Moreover, thisperformance impact (of using maximal matching) is moreprominent when the SINR constraint of PRXs increases.This is because when the SINR constraint of PRXs increases,the channel availability pattern varies more significantlyacross CPEs. That makes it important to carry out intelligent

channel assignment, which is achieved with maximalbipartite matching.

In Figs. 6 and 7, we plot the gains of DL-MDCA,NCMM, and LCSM, relative to that of NCSM, when theperformance metric is the total number of CPEs served,instead of the downlink throughput. This is equivalent tosetting the rate function fð�Þ ¼ 1; 8� � �s. As can be seen,

the performance trends are similar to that of Figs. 4 and 5.However, the gains are less significant. This is because bymaking fð:Þ a constant function, there are less variations inthe system for the control schemes to exploit. For reference,we plot the absolute number of CPEs served by the baselineNCSM scheme in Fig. 8.

8.4 Performance of UL-MDCA—Uplink Throughput

In Figs. 9 and 10, we plot the percentage gain, in the number of

uplink connections being supported, of Optimal, UL-MDCA,LCSM, and NCMM, relative to that of NCSM. Note that as weassume that all active uplinks operating at the same SINR


Fig. 6. Percentage gains in number of CPEs served for DL-MDCA,LCSM, and NCMM, relative to NCSM. Number of primary links ¼ 5.

Fig. 7. Percentage gains in number of CPEs served for DL-MDCA,LCSM, and NCMM, relative to NCSM. Number of primary links ¼ 10.

Fig. 8. Average number of CPEs served in the downlink by the baselinescheme NCSM.

Fig. 5. Percentage gains in total downlink transmission rate for optimal,DL-MDCA, LCSM, and NCMM, relative to NCSM. Number of primarylinks ¼ 10.

(equal to the minimum required SINR of �s), the percentage

gain in number of active uplink connections is equivalent to

the percentage gain in the total uplink throughput.As can be observed, the performance trends of

UL-MDCA, LCSM, and NCMM for the uplink scenario

are similar to that for the downlink scenario. When there arefive PTX-PRX links, the gain of UL-MDCA, relative toNCSM, is from 6 to 13 percent. However, when the numberof PTX-PRX links is increased to 10, the gain becomes muchmore significant and ranges from 20 to 120 percent. We alsonote that, when there are more primary links, cooperationfrom primary nodes becomes much more beneficial to thecognitive network. This is evident by the performance gapbetween LCSM and NCMM in Fig. 10. It can also beobserved that the performance of Optimal scheme isslightly better than that of UL-MDCA. However, asdiscussed in Section 7.4, this optimal scheme requiresknowledge of all channel conditions for a central node tocalculate and set the transmit power levels for all secondaryand primary transmitters. For reference, we plot theabsolute number of CPEs served by the baseline NCSMscheme in Fig. 11.

9 CONCLUSIONS

In this paper, we consider the problem of maximizing thethroughput of a cognitive radio network while protectingprimary users of the spectrum. For this, we propose twomixed distributed/centralized control schemes (for down-link and uplink scenarios) that require minimal cooperationbetween cognitive and primary devices. Numerical resultsshow the desired behaviors of our proposed algorithms and


Fig. 11. Average number of CPEs served in the uplink by the baseline

scheme NCSM.

Fig. 12. DL-DPU process for channel c.Fig. 10. Percentage gains in number of CPEs served for optimal,UL-MDCA, LCSM, and NCMM, relative to NCSM, in the uplink scenario.Number of primary links ¼ 10.

Fig. 9. Percentage gains in number of CPEs served for optimal,UL-MDCA, LCSM, and NCMM, relative to NCSM, in the uplink scenario.Number of primary links ¼ 5.

also demonstrate that the algorithms result in significantperformance gain, in terms of the downlink and uplinkthroughput of the cognitive network.

For future research, we are considering the problemunder a multicell scenario. In such a scenario, multiplecognitive cells share the common set of channels. Theproblem then becomes more complex, as there is a trade-offof coverage and throughput among cochannel cells.

ACKNOWLEDGMENTS

Dr. Y.-C. Liang was the corresponding author.

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Fig. 13. UL-DPU process for channel c. Note that PTX i is the onlyprimary transmitter operating on channel c.

Anh Tuan Hoang received the bachelor’sdegree (with first class honors) in telecommu-nications engineering from the University ofSydney in 2000 and the PhD degree inelectrical engineering from the National Univer-sity of Singapore in 2005. He is currently aresearch fellow at the Department of Network-ing Protocols, Institute for Infocomm Research,Singapore. His research focuses on design/optimization of wireless communication net-

works. His specific areas of interest include cross-layer design,dynamic spectrum access, and cooperative communications. He is amember of the IEEE.

Ying-Chang Liang received the PhD degree inelectrical engineering in 1993. He is now asenior scientist in the Institute for InfocommResearch (I2R), Singapore, where he has beenleading the research activities in the area ofcognitive radio and cooperative communicationsand the standardization activities in IEEE 802.22wireless regional networks (WRAN) for which histeam has made fundamental contributions inphysical layer, MAC layer, and spectrum sen-

sing solutions. He also holds adjunct associate professorship positionsat Nanyang Technological University (NTU), Singapore, and theNational University of Singapore (NUS), and adjunct professorshipposition at the University of Electronic Science and Technology of China(UESTC). He has been teaching graduate courses at the NUS since2004. From December 2002 to December 2003, he was a visitingscholar at the Department of Electrical Engineering, Stanford University.His research interest includes cognitive radio, dynamic spectrumaccess, reconfigurable signal processing for broadband communica-tions, space-time wireless communications, wireless networking, in-formation theory, and statistical signal processing. He served as anassociate editor of the IEEE Transactions on Wireless Communicationsfrom 2002 to 2005, lead guest editor of the IEEE Journal on SelectedAreas in Communications special issue on cognitive radio: theory andapplications, and guest editor of the Computer Networks Journal(Elsevier) special issue on cognitive wireless networks. He receivedthe Best Paper Awards from the IEEE VTC-Fall 1999 and the IEEEPIMRC 2005, and the 2007 Institute of Engineers Singapore (IES)Prestigious Engineering Achievement Award. He has served for variousIEEE conferences as a technical program committee (TPC) member. Hewas the publication chair of the 2001 IEEE Workshop on StatisticalSignal Processing, TPC cochair of the 2006 IEEE InternationalConference on Communication Systems (ICCS ’06), panel cochair ofthe 2008 IEEE Vehicular Technology Conference Spring (VTC ’08-Spring), TPC cochair of the 3rd International Conference on CognitiveRadio-Oriented Wireless Networks and Communications (CrownCom’08), deputy chair of the 2008 IEEE Symposium on New Frontiers inDynamic Spectrum Access Networks (DySPAN ’08), and cochair of theThematic Program on Random Matrix Theory and Its Applications inStatistics and Wireless Communications, Institute for MathematicalSciences, the National University of Singapore, 2006. He holds sixgranted patents and more than 15 filed patents. He is a senior memberof the IEEE.

Md Habibul Islam received the MSc degree inelectrical engineering from Tajik Technical Uni-versity, in 1995, the MBA degree in finance fromthe Institute of Business Administration (IBA),the University of Dhaka, Bangladesh, in 1999,and the MS degree in electrical engineering andthe PhD degree in telecommunications engi-neering from the University of Texas at Dallas in2002 and 2005, respectively. His dissertationfocused on the interference suppression

schemes for the downlink of multiple antenna code-division multipleaccess (CDMA) systems. In February 2006, he joined the Institute forInfocom Research (I2R), Singapore, as a research fellow, where he isworking on the multiple antenna schemes for IEEE 802.22 WirelessRegional Area Networks (WRAN) standard. His research interestsinclude cognitive radio, orthogonal frequency-division multiplexing/orthogonal frequency-division multiple access (OFDM/OFDMA), andmultiple antenna technology, such as space-time coding and beamform-ing. He is a member of the IEEE.

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