An Efﬁcient MAC Protocol With Selective Grouping and...

3928 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 62, NO. 8, OCTOBER 2013

An Efficient MAC Protocol With Selective Groupingand Cooperative Sensing in Cognitive

Radio NetworksYi Liu, Shengli Xie, Senior Member, IEEE, Rong Yu, Member, IEEE, Yan Zhang, Senior Member, IEEE, and

Chau Yuen, Senior Member, IEEE

Abstract—In cognitive radio (CR) networks, spectrum sensingis a crucial technique for discovering spectrum opportunities forsecondary users (SUs). The quality of spectrum sensing is evalu-ated by both sensing accuracy and sensing efficiency. Here, sensingaccuracy is represented by the false-alarm probability and thedetection probability, whereas sensing efficiency is represented bythe sensing overhead and network throughput. In this paper, wepropose a group-based cooperative medium access control (MAC)protocol called GC-MAC, which addresses the tradeoff betweensensing accuracy and efficiency. In GC-MAC, the cooperative SUsare grouped into several teams. During a sensing period, each teamsenses a different channel while SUs in the same team perform thejoint detection on the targeted channel. The sensing process willnot stop unless an available channel is discovered. To reduce thesensing overhead, an SU-selecting algorithm is presented to chooseselectively the cooperative SUs based on the channel dynamics andusage patterns. Then, an analytical model is built to study thesensing accuracy–efficiency tradeoff under two types of channelconditions: a time-invariant channel and a time-varying channel.An optimization problem that maximizes achievable throughput isformulated to optimize the important design parameters. Both sat-uration and nonsaturation situations are investigated with respectto throughput and sensing overhead. Simulation results indicatethat the proposed protocol is able to significantly decrease sensingoverhead and increase network throughput with guaranteed sens-ing accuracy.

Index Terms—Cognitive medium access control (MAC), sensingaccuracy, sensing efficiency, spectrum sensing.

I. INTRODUCTION

R ECENTLY, the explosive increase in wireless devicesand applications has posed a serious problem of the

Manuscript received September 10, 2012; revised January 16, 2013 andMarch 18, 2013; accepted April 6, 2013. Date of publication April 18, 2013;date of current version October 12, 2013. This work was supported in partby the National Natural Science Foundation of China under Grant U1035001,Grant U1201253, and Grant 61273192; by the Research Council of Norwayunder Project 217006/E20; by the European Commission COST Action underGrant IC0902, Grant IC0905, and Grant IC1004; by the European CommissionSeventh Framework Programme through Project EVANS under Grant 2010-269323; and by the International Design Center under Grant IDG31100102and Grant IDD11100101. The review of this paper was coordinated byProf. J. Deng.

Y. Liu is with Guangdong University of Technology, Guangzhou 510006,China, and also with Singapore University of Technology and Design,Singapore 138682 (e-mail: [email protected]).

S. Xie and R. Yu are with Guangdong University of Technology, Guangzhou510006, China (e-mail: [email protected]; [email protected]).

Y. Zhang is with Simula Research Laboratory, Lysaker 1325, Norway(e-mail: [email protected]).

C. Yuen is with Singapore University of Technology and Design, Singapore138682 (e-mail: [email protected]).

Digital Object Identifier 10.1109/TVT.2013.2258952

compelling need of numerous radio spectra The problem isgreatly caused by the current fixed frequency-allocation policy,which allocates a fixed frequency band to a specific wirelesssystem. However, a recent report published by the FederalCommunications Commission (FCC) has revealed that mostof the licensed spectrum is rarely utilized continuously acrosstime and space [1]. To address spectrum scarcity and spectrumunderutilization, cognitive radio (CR) has been proposed toeffectively utilize the spectrum [2]–[4]. In CR networks, thesecondary (unlicensed) users (SUs) are allowed to operateopportunistically in the frequency bands originally allocatedto the primary (licensed) users (PUs) when the bands are notoccupied by PUs. SUs are capable of sensing unused bandsand adjust transmission parameters accordingly, which makesCR an excellent candidate technology for improving spectrumutilization.

Spectrum sensing is a fundamental technology for SUs toefficiently and accurately detect PUs to avoid interference toprimary networks. However, in CR networks, many unreliableconditions [6]–[8], such as channel uncertainty, noise uncer-tainty, and no knowledge of primary signals, will degrade theperformance of spectrum sensing. Cooperative sensing [9]–[15], has been extensively studied as a promising alternativeto improving sensing performance at both the physical leveland the medium access control (MAC) level. The main interestof this paper is the cooperative sensing mechanism at theMAC level, which performs sensing operations in two aspects:1) assign multiple SUs to sense a single channel for improv-ing the sensing accuracy; and 2) assign cooperative SUs tosearch for available spectrums in parallel to enhance sensingefficiency.

The improvement of sensing accuracy is extensively treatedin [10]–[12]. The study in [10] reports a cooperative sensing ap-proach through multiuser cooperation and evaluates the sensingaccuracy. The authors of [11] consider cooperative sensing byusing a counting rule and derive optimal strategies under boththe Neyman–Pearson criterion and the Bayesian criterion. Thestudy in [12] presents a new cooperative wideband spectrumsensing scheme that exploits the spatial diversity among multi-ple SUs, which also contributes to the improvement of sensingaccuracy. These studies have mainly focused on improvingsensing accuracy, whereas sensing efficiency has been ignored.The enhancement of sensing efficiency has been investigatedin [13] and [14]. The study in [13] introduces an opportunis-tic multichannel MAC protocol, which integrates two novel

0018-9545 © 2013 IEEE

LIU et al.: EFFICIENT MAC PROTOCOL WITH SELECTIVE GROUPING AND COOPERATIVE SENSING 3929

cooperative sensing mechanisms, i.e., a random sensing policyand a negotiation-based sensing policy. The latter strategy as-signs SUs to sense collaboratively different channels to improvethe sensing efficiency. For the sake of reducing sensing over-head, the authors of [14] propose a multichannel cooperativesensing scheme, where the cooperative SUs are optimally se-lected to sense the distinct channels at the same time for sensingefficiency. These works assume that the sensing accuracy of onechannel by a single SU is completely true, which may not bepractical in real communication systems.

In addition, the given works did not consider the design ofthe cooperative MAC protocol for distributed networks and per-form theoretical analysis of sensing overhead and throughput.Hence, we are interested in achieving both sensing accuracy andsensing efficiency by introducing a cooperation protocol in theMAC layer for CR networks. Several cognitive MAC protocolshave been proposed in the literature to address various issues inthe CR network [13], [17], [20]–[25]. However, these protocolsdo not leverage the benefit of cooperation at the MAC layer forenhancing the sensing efficiency without degrading the sensingaccuracy.

In this paper, we propose a group-based cooperative MACprotocol called GC-MAC. In GC-MAC, the cooperative SUsare grouped into several teams. During a sensing period, eachteam senses a different channel. The sensing process will notstop unless an available spectrum channel is discovered. Thepurpose of team division is twofold: 1) sensing a channel byseveral SUs for the improvement of sensing accuracy; and2) finding more spectrum opportunities by sensing distinctchannels by different teams. Therefore, multiple distinct chan-nels can be simultaneously detected within one sensing period,which leads to the enhancement of sensing efficiency.

To reduce the sensing overhead, we propose an SU-selectingalgorithm for GC-MAC protocol. In the SU-selecting algo-rithm, we selectively choose the optimal number of the coop-erative SUs for each team based on the channel occupationdynamics to substantially reduce sensing overhead. We analyzethe sensing overhead and throughput in the saturation andno-saturation network cases, respectively. In the saturationnetworks, each SU always has data to transmit. In thenonsaturation networks, an SU may have an empty queue.In every network case, we consider two types of channelconditions: time-invariant channel and time-varying channel.In each condition, the sensing overhead and the throughputare incorporated into an achievable throughput maximizationproblem, which is formulated to find the key design parame-ters: the number of the cooperative teams and the number ofSUs in one team. Furthermore, we present extensive examplesto demonstrate the sensing efficiency compared with existingschemes and to show the determination of the crucial parame-ters. Simulation results demonstrate that our proposed schemeis able to achieve substantially higher throughput and lowersensing overhead, as compared to existing mechanisms.

The remainder of this paper is organized as follows. InSection II, the system models are introduced. Section III reportsour proposed group-based MAC protocols for a cooperativeCR network. Section IV introduces an SU-selecting algorithmfor appropriately selecting the cooperative SUs to reduce the

sensing overhead. Then, we study the sensing overhead andachievable throughput in the saturation and nonsaturation net-works in Sections V and VI, respectively. Section VII evaluatesthe performance of the proposed GC-MAC protocol based onour developed analytical models. Finally, we draw our conclu-sions in Section VIII.

II. SYSTEMS MODELS

A. Channel Usage Model

We assume that each licensed channel alternates between ON

and OFF states, of which the OFF time is not used by PUs andhence can be exploited by the SUs. Assume that the durationof the ON and the OFF periods is independently exponentiallydistributed. For a given licensed channel, the duration of theON period follows an exponentially distributed parameter μON

and the duration of OFF period with an exponentially distributedparameter μOFF. We define the channel availability as thenormalized period that is available for SUs. Let p denote thechannel availability. Then, we have p = μON/(μOFF + μON).Similar to [13], in this paper, we mainly consider that thelicensed channels used by the same set of PUs, i.e., the licensedchannel availability information sensed by each SUs is consis-tent among all SUs.

We consider two scenarios depending on the channel dy-namics. The first scenario is the time-invariant channel withunchanged channel date rate R. The throughput of the SU byusing a time-invariant channel only depends on the constantdata rate and the valid transmission time Tr. The second typeof channel is the time-varying channel. The finite-state Markovchannel (FSMC) model is employed to model the dynamicsof the time-varying channel [19]. The dynamics of the time-varying channel is partitioned based on the channel data rate.It is reasonable to employ the channel data rate instead ofthe SNR, which has been used in conventional FSMC models.Since the channel data rate is closely relevant to the applicationlayer requirements, hence, its usage facilitates the constructionof resource demands from an application perspective. The setof the channel state is denoted as M ≡ {1, 2, . . . ,M} with|M| = M . Let ci represent the channel state i (i ∈ M). Thestate space is denoted as S ≡ {ci, i ∈ M}. Let πi (i ∈ M)represent the steady-state probability at state ci. Then, thesteady-state probability can be solved using the similar tech-nique in [19]. During data transmission within a frame, thetime variation is slow enough that the channel data rate doesnot substantially change. This assumption is acceptable due tothe short data transmission period within a frame and has beenfrequently used, e.g., in [18] and [26].

B. Energy Detection Model

To discuss our problem, we employ energy detection [7] asthe spectrum sensing scheme. Both of the real-valued signalmodel and the complex-valued signal model are used to de-scribe the received signal at the SU’s receiver.

1) Real-Valued Signal Model: Let ts be the sensing time andfs be the sample frequency during sensing time. We denotes N


as the number of samples in a sensing period, i.e., N = tsfs.The received signal rk(n) at the nth sample and the kth SU isgiven by

rk(n) =

{wk(n), H0

sk(n) + wk(n), H1

where H0 represents the hypothesis that PUs are absent, and H1

represents the hypothesis that PUs are present. sk(n) representsthe PU’s transmitted signal, which is assumed as a real-valuedGaussian signal with zero mean and variance σ2

s . wk(n) denotesa Gaussian process with zero mean and variance σ2

w.Let ek(r) denote the test statistic of the kth SU. Then, we

have ek(r) =∑N

n=1 |rk(n)|2. The detection and false-alarmprobability of kth SU are given by

P kd = Pr [ek(r) > λ|H1] P k

f = Pr [ek(r) > λ|H0]

where λ is a decision threshold of the energy detector for a SU.The test statistic e(r) is known as a chi-square distribution

with (e(r)/σ2w) ∼ χ2

N under hypothesis H0, and (e(r)/σ2s +

σ2w) ∼ χ2

N under hypothesis H1. However, if the number ofsamples is large, we can use the central limit theorem toapproximate the chi-square distribution by Gaussian distribu-tion [7] under hypothesis Hz(z = 0, 1) with mean μz andvariance σ2

z as{μ0 = Nσ2

w σ20 = 2Nσ4

w, H0

μ1 = N(σ2s + σ2

w

)σ21 = 2N

(σ2s + σ2

w

)2, H1.

Therefore, the probabilities P kd and P k

f that can be approxi-mated in terms of the Q function are given as follows:

P kd = Q

(λ−N

(σ2s + σ2

w

)√

2N (σ2s + σ2

w)

)P kf = Q

(λ−Nσ2

w√2Nσ2

w

)

where Q(x) = (1/√

2π)∫∞x e(−(t2/2))dt.

2) Complex-Valued Signal Model: Considering the complex-valued signal model, the received signal rk(n) at the nth sampleand the kth SU can be given by

rk(n) =

{wk(n), H0

hksk(n) + wk(n), H1

where the channel coefficients hk are zero-mean unit-variancecomplex Gaussian random variables. sk(n) represents the PU’stransmitted signal, which is assumed as a Gaussian signal withzero mean and variance σ2

s . wk(n) denotes a Gaussian processwith zero mean and variance σ2

w.The test statistic of the kth SU ek(r) =

∑Nn=1 |rk(n)|2. The

detection and false-alarm probability of kth SU are given by

P kd = Pr [ek(r) > λc|H1] P k

f = Pr [ek(r) > λc|H0]

where λc is a decision threshold of the energy detector for asingle SU considering the complex-valued signal model. Fora large N , the distribution of ek(r) can be approximated asGaussian distribution [7] with mean μz and variance σ2

z underhypothesis Hz(z = 0, 1) as{

μ0=Nσ2w σ2

0=2Nσ4w, H0

μ1=N(|hk|2σ2

s + σ2w

)σ21=2N

(|hk|2σ2

s + σ2w

)2, H1.

Finally, we can obtain the probabilities P kd and P k

f in termsof the Q function as

P kd = Q

(λ−N

(|hk|2σ2

s + σ2w

)√

2N (|hk|2σ2s + σ2

w)

)P kf = Q

(λ−Nσ2

w√2Nσ2

w

)

where Q(x) = (1/√

2π)∫∞x e(−(t2/2))dt.

C. Counting Rule

To improve sensing performance, an efficient fusion ruleis needed to make a final decision to the availability of thechannel. Depending on every SUs’ individual decision fromone team, there are three popular fusion rules: AND rule, OR

rule, and majority rule [18]. The AND rule mainly focuses onmaximizing the discovery of spectrum opportunities, which aredeemed to exist if only one decision states that there is no PU.In the OR rule, as far as limiting the interference to the PU, thespectrum is assumed to be available only when all the reportingdecisions declare that no PU is present. The last majority rule isbased on the majority of the individual decisions. If more thanhalf of the decisions declare the appearance of PU, then the finaldecision claims that there is a PU. Without loss of generality, weuse the majority rule in this paper with the assumption that allthe individual decisions are independent and that P k

d = Pd andP kf = Pf [18]. Then, the joint detection probability and false-

alarm probability by a j number of SUs are given by

Pd(j) =

j−� j2 �∑

y=0

(j

� j2�+ y

)(1 − Pd)

j−� j2 �−yP

� j2 �+y

d (1)

Pf (j) =

j−� j2 �∑

y=0

(j

� j2�+ y

)(1 − Pf )

j−� j2 �−yP

� j2 �+y

f . (2)

III. GROUP-BASED COOPERATIVE MEDIUM

ACCESS CONTROL

Here, we present the specifications of the proposed MACprotocol, together with the group-based cooperative spectrumsensing scheme and the SU-selecting algorithm. To describe ourprotocol conveniently, we have the following assumptions.

• Each SU is equipped with a single antenna that cannotoperate the sensing and transmission at the same time.According to this constraint, the sensing overhead causedby sensing is unavoidable and cannot be neglected inprotocol design.

• A common control channel is available for all SUs tocommunicate at any time.

• An SUs can be assigned to perform cooperative sensingeven when they have the packets to transmit.

A time frame of the secondary network operation is dividedinto three phases: 1) reservation; 2) sensing; and 3) transmis-sion. All SUs are categorized into three types as follows:

• Source SU (SUs): an SU that has data to transmit;• Cooperative SUs (SUc): SUs that are selected for cooper-

ative sensing;


Fig. 1. Sensing work flow of SUs.

• Destination SU (SUd): an SU that receives the data packetfrom the source SU.

A. Reservation

In GC-MAC, any SUs entering the network first try toperform a handshake with SUd on the control channel to reservea data channel. This allows the SUs and SUd to switch to thechosen channel for data transmission. Here, we use reservationfor request-to-send/request for clear-to-send (R-RTS/R-CTS)packets for SUs and SUd to compete the data channel with otherSUs. The SUs will listen to control channel for a time intervalT . If no R-RTS/R-CTS is received or time T is expired, theSUs participates in the reservation process. Otherwise, it willdefer and wait for the notification from the transmission pairor a timeout. Whenever there is at least one packet bufferedin the queue, SUs sends the reservation requirement to SUd.Upon receiving the requirement, SUd will reply, and otherSUs overhearing these message exchanging cease their ownsensing and wait for the notification from this transmissionpair or a timer expiration. When the sensing or cooperativesensing is finished, other neighboring SUs start a new roundof competition for the control channel with a random backoff.

B. Sensing

After reserving the data channel, SUs and SUd start tosense the spectrum channel. In this phase, we use secondaryRTS/CTS (S-RTS/S-CTS) packets for spectrum sensing andnegotiation between SUs and SUd. To indicate the mechanismof our scheme, the certificate RTS/CTS (C-RTS/CTS) packetsare included in the RTS/CTS model for SUc to acknowledgeits participation. Fig. 1 shows the flowchart of the sensing

Fig. 2. Sensing work flow of SUc and SUd.

procedure of the source node SUs. Fig. 2 shows the flowchartof the sensing procedures of SUc and SUd. In particular, weprovide the detailed description as follows.

Source SU (SUs):

1) SUs senses the channel to judge the availability of thechannel. If the channel is not occupied by a PU, SUs

sends an S-RTS packet to SUd, including the availabil-ity information of the detected channel. Otherwise, SUs

sends the channel unavailability information to SUd.2) If an S-CTS packet from SUd is not heard after a CTS

timer, SUs should perform a random backoff as if itencounters a collision. If SUs receives the information ofchannel availability from SUd. SUs and SUd will start thetransmission phase (please refer to Section III-C). If SUs

receives the information of channel unavailability fromSUd, SUs will send C-RTS to the neighborhood of SUc

and SUd.3) If SUs does not receive any feedback from SUc, it

then sends cooperation requirement again after a randombackoff. If the feedback is successfully received, SUs

counts the number of SUc according to the SU-selectingalgorithm (please refer to Section IV). When the numberof SUc’s satisfies the requirement of the cooperativesensing, SUs stops sending the cooperation requirementto the neighborhood of SUc and divides the chosen SUc’sinto a number of teams.

4) SUs sends the cooperative information to the SUc’s andthen join the cooperative sensing with SUc’s. Such in-formation includes grouping information and the specificchannels.

5) Upon receiving the sensing results, SUs should declarethe success of spectrum sensing and return to step 1.


Otherwise, SUs should perform a random backoff andthen return to step 4.

Cooperative SU (SUc):

1) Upon receiving the cooperation requirement, SUc sendsfeedback to the source node SUs and waits for the coop-erative information.

2) If the information for the cooperation is not received aftera CTS timer, SUc assumes that the information is lost andthen reverts to the original state. Otherwise, SUc starts thechannel sensing based on the cooperative information.

3) After the time duration ts, SUc determines the PU’sactivity on the detected channel and sends cooperationacknowledgement to SUs with the sensing result.

Destination SU (SUd):

1) SUd senses the same channel with SUs in a synchronousway. After the sensing time ts, SUd makes the finaldecision about the state (ON/OFF) of the channel and waitsfor the sensing requirement from the source node SUs.

2) If the destination node SUd receives the sensing re-quirement with the sensing result from the source nodeSUs, it delivers the sensing result back to SUs. If thesensing result indicates that the channel is available, SUd

is ready for receiving data. Otherwise, SUd waits for thecooperation requirement.

3) If cooperation requirement is received, SUd will jointhe cooperative sensing and report the sensing results toSUs. Then, SUd returns to step 2. If neither a sensingrequirement nor an cooperation requirement is heard aftera timer, SUd will go back to the initial state.

C. Transmission

After the source node SUs and the destination node SUd

successfully find an available channel, they begin to use thechannel to transmit data packets. Here, we use the trans-mit RTS/CTS (T-RTS/T-CTS) pair to indicate the transmis-sion process. Before starting the transmission, SUs will sendT-RTS to SUd for declaring the beginning of transmission.Upon receiving this requirement, SUd replies T-CTS. If thisfeedback is received, SUs sends the data packets to SUd andsets acknowledgment timeout. When the acknowledgment fromSUd arrives, SUs should declare the transmission success overthe control channel. This success information ends the deferringof the neighboring SUs and starts a new round of reservation.If acknowledgment is not received after an acknowledgmenttimeout, SUs should perform a random backoff and retransmitthe data packets.

IV. REDUCING SENSING OVERHEAD VIA

SECONDARY USER-SELECTING ALGORITHM

Here, we would like to reduce the sensing overhead byintroducing an SU-selecting algorithm. In this algorithm, weemploy the alternative pattern and the channel data rate of theSUs’ used channel as the cooperative SU’s selection conditions.

A. Channel Pattern for SUs

Each channel alternates between the ON state and the OFF

state, which depends on the PUs’ usage pattern. The channelthat an SU uses may be busy after a period τ based on theprevious idle status. During the busy period, the SUs are notallowed to access the channels, which are occupied by a PU.In this case, if these SUs are selected for cooperative sensing,the overhead of cooperation can be substantially reduced sincesensing overhead is mainly incurred by ceasing transmissionsduring the cooperative sensing period. Let Iε ∈ {0, 1} representthe binary channel state of channel ε. Iε = 1 refers to the ON

state, and Iε = 0 refers to the OFF state. Let PIε1(τ) denotethe transition probability that the εth channel will be busy afterτ s with the initial state Iε. We can express the transitionprobability P01(τ) from the channel OFF state to the channelONstate as [27]

P01(τ) = p− pe−(μOFF+μON)τ (3)

where p is the channel availability.It is shown that the P01(τ) only relate with the most recent

channel state Iε = 0 and τ , which is the time between themost recent sensing and the current sensing. Considering thatτ is different among the channels, then P01(τ) is accordinglydifferent with distinct SUs. To reduce the sensing overhead, ourgoal is to select the cooperative SUs with the high P01(τ). In thefollowing, we first present the optimal SU-selecting algorithmin the time-invariant channel case. Then, we derive the optimalselecting algorithm for the case where the channel has a time-varying feature.

B. SU-Selecting Algorithm

1) Time-Invariant Channel Case: A channel may stay at theidle state after τ s. The sensing overhead is expected to be highif the SUs who used these channels are chosen for cooperativesensing. Thereafter, to reduce sensing overhead, we select thecooperative SUs in the descending order of the probabilityP01(τ). We can present the SU-selecting algorithm as follows.

• SUs delivers the cooperative sensing request message(MSG-CSR) to the neighboring SUc’s when a PU’s activ-ity is detected on a channel.

• The kth SUc calculates P01(τk), where τk represents thetime duration from the moment of the most recent sensingto the moment of receiving MSG-CSR.

• SUs selects the cooperative SUc’s according to the de-scending order of P01(τk).

The probability P00(τk) can be alternatively employed sinceP00(τk) = 1 − P01(τk). Hence, the SU-selecting algorithm canobtain the same strategy if we choose the cooperative SUs in theascending order of the probability P00(τk).

2) Time-Varying Channel Case: To reduce the sensing over-head, the SUs that have the highest P01(τ) should be selectedfor cooperation in the time-invariant channel case. Here, theprobability P01(τ) represents the transition probability from theOFF state to the ON state. However, this strategy may not beefficient in the time-varying case where the channel data ratechanges over the time. We choose the SUs not only based on


the probability P01(τ) but also based on the channel data rateof their used channels. The SUs’ used channels, which haveboth the lowest channel data rate and the highest P01(τ) (orlowest P00(τ)), are selected to perform sensing and to searchthe available channels. As a consequence, in the time-varyingchannel case, the SU-selecting algorithm can be provided asfollows.

• SUs delivers the MSG-CSR to the SUcs when PU’s activ-ity is detected on a channel.

• The kth SUc calculates P00(τk), where τk represents thetime duration from the moment of the most recent sensingto the moment of receiving the message MSG-CSR.

• SUs multiplies P00(τk) by the channel data rate Rk of thekth SU’s channel.

• SUs selects the cooperative SUs according to the ascend-ing order of P00(τk)Rk.

V. ANALYSIS AND OPTIMIZATION FOR THE

SATURATION NETWORKS

Here, we will analyze the sensing overhead and throughputin a saturation networks. Our objective is to find two key designparameters: the number of cooperative teams and the numberof SUs in one team. In a saturation network, we considerthe CR network consisting of C licensed channels and a Knumber of SUs. The set of licensed channels is denoted asC ≡ {1, 2, . . . , C} with |C| = C. The set of SUs is denoted asK ≡ {1, 2, . . . ,K} with |K| = K. We allow the cooperativesensing scheme to choose a certain number of SUs, which arefurther divided into U teams. Each team has a q (q ≥ 1) numberof SUs and is assigned to sense a distinct channel during eachsensing period ts. The relationship among the variables K, U ,and q satisfies Uq ≤ K.

A. Time-Invariant Channel Case

1) Sensing Overhead: We define Ts as the total time dura-tion spent by the kth cooperative SU after an ns number of thecooperative sensing. With the proposed group-based sensingstrategy, up to a U number of channels can be detected inone sensing period. Hence, all channels can be completelysensed within an �C/U� number of sensing, and the variablens varies between 1 and �C/U�. If the channels can be foundafter an ns number of cooperative sensing, the cooperative SUscannot transmit any packets during Ts = nsts sensing periods.This operation is unfortunately unavoidable in the cooperativesensing. Let oTI

k denote the sensing overhead caused by the kthcooperative SU in the time-invariant situation. Then, we have

oTIk =

Ts∫0

RkP00(τk)dτk (4)

whereRk denotes the channel data rate of the channel used by thekth cooperative SU. Since the channel data rate is a constant inthe time-invariant channel case, we obtain sensing overhead as

oTIk =

Ts∫0

RP00(τk)dτk. (5)

2) Throughput: Let Ps represent the probability that a chan-nel is successfully found. This is equal to the probability that achannel is available and that no false alarm is generated by a qnumber of cooperative SUs. Then, we have Ps = p[1 − Pf (q)],where p is the channel availability and Pf (q) is given by (2).Let u denote the number of available channels that are foundin cooperative sensing. The probability distribution function ofthe random variable u is given by

(Uu

)(1 − Ps)

U−uPus . Then,

we can obtain the probability PTIav,1 that the available channels

can be found in one cooperative sensing as

PTIav,1 =

U∑u=1

(U

u

)(1 − Ps)

U−uPus . (6)

With the proposed group-based sensing strategy, up to a Unumber of channels can be detected in one sensing period.Hence, all channels can be sensed completely within a �C/U�number of sensing periods. We can then obtain the probabilityPTIav, ns

that an available channel is found after ns cooperativesensing as follows:

PTIav, ns

=(1 − PTI

av,1

)ns−1PTIav,1. (7)

Let Tr denote the average transmission time for an SU usinga discovered available channel. We can derive the throughput ofan SU by using this channel as follows:

T TI =

�C/U�∑ns=1

PTIav, ns

TrR

=

�C/U�∑ns=1

TrR

[1 −

U∑u=1

(U

u

)(1 − Ps)

U−uPus

]ns−1

×[

U∑u=1

(U

u

)(1 − Ps)

U−uPus

](8)

where Tr =∫∞0 μOFFe

−μOFFttdt = 1/μOFF.To determine the optimal value of U and q, we introduce

a new term, i.e., the achievable throughput, which is definedas the difference between sensing overhead and throughput. Itis clear that the achievable throughput is able to demonstratethe purely achieved throughput after removing the penalty withrespect to sensing overhead. For this perspective, the concept isable to capture the inherent tradeoff in the cooperative sensing.

Suppose that the available channel is discovered at the nsthdetection by a U number of teams. We can obtain the totalsensing overhead OTI as follows:

OTI =

�C/U�∑ns=1

nsPTIav, ns

qUoTIk . (9)

Our objective is to find the optimal U and q for the groupsensing to maximize the achievable throughput. The optimiza-tion problem is formulated as

maxq,U

GTI = T TI −OTI

s.t. qU ≤K;

Pf (q) ≤Pf, th; Pd(q) ≥ Pd, th (10)


where Pf, th and Pd, th represent the threshold of the false-alarm probability and detection probability, respectively. Basedon the derived expression of T TI and OTI, the optimal numberof cooperative teams and SUs in one team can be determinedby solving (10). Considering the prohibitively high complexityof the optimization problem, we have resorted to numericalmethods to find the optimal result to maximize the achievablethroughput.

B. Time-Varying Channel Case

Here, we will perform an analytical analysis on sensingoverhead and throughput in the time-varying channel case. It isworth noting that the analysis in the time-varying channel caseis not a trivial extension of the analysis in the time-invariantchannel case. On the one hand, the analysis in the time-invariantchannel case is necessary to provide an easy understandingof the SU cooperation behavior and the inherent tradeoff be-tween throughput and sensing overhead. On the other hand,the time-varying channel case is much more complicated thanthe time-invariant case by considering the complex channel dy-namics. The development of sensing overhead and throughputis dependent on the channel dynamics, which leads to newequations for a channel data rate, sensing overhead, through-put, and hence, achievable throughput in the time-varyingcase.

1) Sensing Overhead: Based on the SU-selecting algorithm,we can analyze the sensing overhead caused by the group-based sensing under the time-varying channel case. Let R =[R1, R2, . . . , RM ] represent the channel-data-rate vector oflength M . Without loss of generality, we suppose R1 < R2 <· · · < RM . Let X = [X1, . . . , XK ] be a random sample fromR of length K. Hereby, the vector X represents the specificvalue of a parallel sensing and hence has length K insteadof M . Let Xk(k ∈ K) denote the kth-order statistics of thesample. Employing the order statistics theory [29], we canderive the probability Pr{Xk = Rn} (k ∈ K;n ∈ M), whichshows that the kth SU’s channel data rate is equal to Rn.We suppose that there are an (h− 1) number of samplesin X with the probability Pr{Xi < Rn} (1 ≤ i ≤ h− 1; 1 ≤h ≤ k); an (l − h+ 1) number of samples in X with theprobability Pr{Xi = Rn} (1 ≤ i ≤ l − h+ 1; k ≤ l ≤ K);and an (n− l) number of samples in X with the probabilityPr{Xi > Rn} (1 ≤ i ≤ n− l).

The random variables Xi are statistically independentand identically distributed with the generic form X; thus,

we have

Pr{X < Rn} =∑

Ri<Rn

Pr{X = Ri} =

n−1∑I=1

πi.

Since the (h− 1) samples could be any random samples fromX , we obtain the probability of this case

(Kh−1

)(∑n−1

i=1 πi)h−1.

For the probability Pr{Xi = Rn}, we have

Pr{X = Rn} = πn.

Since the number of (l − h+ 1) samples could be any randomsamples from the rest of (K − h+ 1) samples of X , we obtainthe probability of this case as

(K−h+1l−h−1

)(πn)

l−h+1, i.e.,

Pr{Xi > Rn} = 1 − Pr{X ≤ Rn}

= 1 −∑

Ri<Rn

Pr{X = Rn}

= 1 −n∑

i=1

πi.

Similarly, we obtain the probability of this condition as(K−1K−

)(1 −

∑ni=1 πi)

K−l. By summarizing all possibilities, theprobability Pr{Xk = Rn} is given by (11) at the bottom of thepage. Then, the channel data rate of the selected SU, denoted asRk (k ∈ K), is given by

Rk =M∑n=1

n∑i=1

Ri Pr{Xk = Rn}. (12)

Let oTVk denote the sensing overhead caused by the cooper-

ative SUk after an ns number of cooperative sensing under thetime-varying channel condition. We can obtain

oTVk =

Ts∫0

RkP00(τk)dτk (13)

where Ts = nsts denotes the time spent by the kth cooperativeSU after ns number of sensing.

2) Throughput: Let v represent the number of spectrumchannels that are found in a cooperative sensing. The prob-ability density function of the random variable v is given by(Uv

)(1 − Ps)

U−vP vs , where Ps is given by (1). Let PTV

av denotethe probability that an available channel can be found in one

Pr{Xk = Rn} =

K∑l=k

k∑h=1

⎡⎣( K

h− 1

)(n−1∑i=1

πi

)h−1 (K − h+ 1l − h− 1

)(πn)

l−h+1(K − l

K − l

)(1 −

n∑i=1

πi

)K−l⎤⎦

=K∑l=k

k∑h=1

⎡⎣ K!

(h− 1)!(l − h+ 1)!(K − 1)!

(n−1∑i=1

πi

)h−1

(πn)l−h+1

(1 −

n∑i=1

πi

)K−l⎤⎦ (11)


cooperative sensing in the time-varying channel case. Then,we have

PTVav =

U∑v=1

(U

v

)(1 − Ps)

U−vP vs . (14)

We need to find the available channel with the highestchannel data rate by the U teams. We will select the channel thathas the highest channel data rate in these v channels for the SUto access. Let Rm (1 ≤ m ≤ M) denote the highest channelrate in these v numbers of channels. It is worth noting that thesubscript m in Rm represents the index of channel data rate,which ranges from 1 to M . Let Prate,v denote the probabilitythat there are channels whose maximum rate is no lower thanRm (1 ≤ m ≤ M) in the founded v channels. Then, we have

Prate,v =

(m∑i=1

πi

)v [1 −

(m−1∑i=1

πi

)v]. (15)

Conditioning on all possibilities on the random variable v,we obtain the probability Prate that there are channels whosemaximum rate is no lower than Rm (1 ≤ m ≤ M), i.e.,

Prate =

U∑v=1

(U

v

)(1 − Ps)

U−vP vs Prate, v. (16)

We obtain the probability Pmaxrate that Rm is the maximalchannel data rate from all discovered available channels, i.e.,

Pmaxrate =(1 − PTV

av

)ns−1Prate. (17)

With the proposed sensing strategy, each sensing period mayfind up to a U number of channels. Hence, all channels can besensed completely within an �C/U� number of sensing periods.We can derive the throughput of the SU by using this channel as

T TV =

�C/U�∑ns=1

M∑m=1

PmaxrateTrRm

=

�C/U�∑ns=1

M∑m=1

TrRm

(1 − PTV

av

)ns−1U∑

v=1

(U

v

)

× (1 − Ps)U−vP v

s

(m∑i=1

πi

)v [1 −

(m−1∑i=1

πi

)v]

(18)


−μOFFttdt = 1/μOFF.We formulate the achievable throughput optimization prob-

lem by considering both throughput and sensing overhead inthe time-varying channel condition. The total sensing overheadOTV is given by

OTV =

�C/U�∑ns=1

nsqUPTVav oTV

k . (19)

Consequently, the achievable throughput maximization prob-lem in the time-varying channel case is formulated as

maxq,U

GTV = T TV −OTV

s.t. qU ≤ K;

Pf (q) ≤ Pf, th; Pd(q) ≥ Pd, th (20)

where T TV and OTV are given by (18) and (19), respectively.By solving (20), we can find the optimal U and q for the groupsensing to maximize the achievable throughput.

VI. ANALYSIS AND OPTIMIZATION FOR THE

NONSATURATION NETWORKS

Here, we will derive the sensing overhead and throughputin the nonsaturation networks. Suppose that an SU may havean empty queue. In this network, we consider a discrete-time queue with an infinite capacity buffer for the queuingbehavior of an SU. The packet arrival of the SUs is assumedto be a Poisson process with arrival rate λpac. The packets areserved on a first-in–first-out (FIFO) basis. The service time ofeach packet is modeled as identically distributed nonnegativerandom variables, denoted as χn(n ≥ 1), whose arrival processis independent to each another. The similar assumption hasbeen frequently used in the literature, e.g., [13], [28]. Let F (t)denote the service time cumulative distribution function (cdf)with mean 0 < 1/μ =

∫∞0 tdF (t). Let ρ represent the traffic

load, and it is given by ρ = λpac/μ. For a practical system, thetraffic load is less than 1, i.e., ρ < 1.

Similar to the saturation network, we still consider the CRnetwork consisting of C licensed channels and a K number ofSUs. The cooperative SUs are divided into U teams. Each teamhas a q (q ≥ 1) number of SUs. Each team is assigned to sense adistinct channel during each sensing period ts. The relationshipamong the variables K, U , and q also satisfies Uq ≤ K. Next,we will formulate the throughput maximization problem with atime-invariant and time-varying channel, respectively.

A. Time-Invariant Channel Case

Since the channel data rate will not change with the timein the time-invariant channel case, the packet service time is aconstant, which means we are able to employ the single-serverqueuing model M/D/1 to evaluate the group sensing schemewith the time-invariant channel.

Based on the result of [29], the variance of service timeE(χ2) = 0 in the M/D/1 model. Let N

TIq denote the average

number of packets in a queue for time-invariant channel case.Then, we have

NTIq =

∞∑v=1

vpv+1 =ρ2

2(1 − ρ). (21)

1) Sensing Overhead: To reduce the sensing overhead, westill select qU SUs that have the lowest channel data rate andleast P00(t) among K SUs in the nonsaturation network. As ex-plained, each group sensing can sense a U number of channels.


Hence, all channels can be sensed completely within a �C/U�number of group sensing. Let NTI,ns

sense be the total number ofpackets that can be transmitted in the ns number of groupsensing by the qU sensing SUs if they are not participating thegroup-based cooperative sensing. NTI,ns

sense is given by

NTI,nssense =min

{nsqUN

TIq , (qUTsRuse)/l

}; 1≤ns≤�C/U�

(22)

where Ruse denotes the channel data rate of the using channel,

l denotes the length of a packet, Ts = tsns, and NTIq is given

by (21).Suppose that the available channel is discovered at the nsth

detection by an U number of teams in a nonsaturation network.Then, in a time-invariant channel case, we can obtain the totalsensing overhead OTI

nonsat as follows:

OTInonsat =

�C/U�∑ns=1

PTIav, ns

NTI,nssense l (23)

where PTIav, ns

is given by (7).2) Throughput: Let Tr denote the average transmission time

for an SU using a discovered available channel. In the time-invariant channel case, the average number of packets that SUssend during Tr at the equilibrium state is given by

NTID = min

{N

TIq , (TrRuse)/l

}(24)


−μOFFttdt = 1/μOFF.With the proposed sensing strategy, each sensing period may

find up to a U number of channels. Hence, all channels can besensed completely within a �C/U� number of sensing periods.Hence, we can derive the throughput of an SU by using thediscovered available channel as follows:

T TInonsat =

�C/U�∑ns=1

PTIav, ns

NTID l (25)

where the item PTIav, ns

is given by (14).In terms of the achievable throughput maximization, we

formulate the following problem:

maxq,U

GTInonsat = T TI

nonsat −OTInonsat

s.t. qU ≤ K;

Pf (q) ≤ Pf, th; Pd(q) ≥ Pd, th. (26)

B. Time-Varying Channel Case

Considering the time-varying channel case, the channel datarate may vary from time slot to time slot. This alternativeindicates that an SU’s capacity is a random variable. Followingthis reasoning, we can use the M/G/1 queuing model.

1) Sensing Overhead: Since the service time of each pack-ets depends on the channel data rate, we can express the cdfF (t) as

F (t) = l/Ri(t) (27)

where Ri(t) denotes the channel data rate of the ith channel

state at the tth time slot. Let NTVq denote the average number

of packets in a queue for a time-varying channel case. Then, wehave

NTVq =

∞∑v=1

vpv+1 =λ2E(χ2) + ρ2

2(1 − ρ)(28)

where E(χ2) =∫∞0 t2dF (t).

In the time-varying channel case, let NTV,nssense be the total

number of packets that cannot be transmitted by the qU co-operative SUs in an ns number of group sensing. NTV,ns

sense isgiven by

NTV,nssense =min

{nsqUN

TVq , (qUTsRuse)/l

}; 1≤ns≤�C/U�

(29)

where Ts = tsns, and NTVq is given by (28).

Then, in a time-varying channel case, the total sensing over-head for discovering an available channel can be obtained asfollows:

OTVnonsat =

�C/U�∑ns=1

PTVav NTV,ns

sense l. (30)

2) Throughput: We use Tr to denote the average transmis-sion time for an SU using a discovered available channel inthe time-varying channel case. Then, the average number ofpackets that SUs send during Tr is given by

NTVD = min

{N

TVq , (TrRuse)/l

}(31)


−μOFFttdt = 1/μOFF.The proposed sensing strategy may find up to a U number

of channels during each sensing period. All channels can becompletely sensed within a �C/U� number of sensing periods.Supposing that the available channel can be found after an ns

number of group sensing, we can obtain the throughput of anSU by using a discovered available channel in the time-varychannel case, i.e.,

T TVnonsat =

�C/U�∑ns=1

M∑m=1

PmaxrateNTVD l (32)

where the item Pmaxrate is given by (17).Finally, we formulate the following problem in terms of

achievable throughput maximization:

maxq,U

GTVnonsat = T TV

nonsat −OTVnonsat

s.t. qU ≤ K;

Pf (q) ≤ Pf, th; Pd(q) ≥ Pd, th. (33)

Considering the complexity of the optimization problems, westill use numerical methods to find the optimal result to maxi-mize the achievable throughput in the nonsaturation network.The optimal results are provided in the following under time-invariant and time-varying channel conditions, respectively.


TABLE IACHIEVABLE SATURATION THROUGHPUT WITH DIFFERENT COMBINATIONS OF U AND j IN THE TIME-INVARIANT CHANNEL CASE

TABLE IIACHIEVABLE SATURATION THROUGHPUT WITH DIFFERENT COMBINATIONS OF U AND j IN THE TIME-VARYING CHANNEL CASE

VII. SIMULATION RESULTS

Here, we demonstrate the performance of the proposed GC-MAC in CR networks. The network consists of total C = 10licensed channels. The channel parameter of the OFF periodμOFF = 1/100. We concentrate on the low SNR situation; theSNR threshold for a PU at the tagged SU is γ = −10 dB.The channel bandwidth is 1 MHz, and the target probabilityof detection Pd = 0.9, which is an important parameter usedby 802.22 standard [30]. The length of RTS/CTS packets andsensing period are 40 B and 1 ms, respectively. Considering thetime-varying channel case, the number of channel data rate stateis M = 10. Accordingly, the channel data rate of each channelranges between 0.1–1 MB/s, which decreases or increases itsvalue by 10% once every 5 ms.

Table I shows the impacts of the number of cooperativeteams and the number of SUs in one team on the achievablesaturation throughput in the time-invariant channel situation.In these examples, the channel availability p is set as 1/2. Wecan determine the optimal achievable throughput by choosingappropriate parameters. From Table I, we observe that theachievable throughput is maximized as 0.9822. In the time-varying channel case, Table II shows the achievable saturationthroughput and that the maximal value is 0.8154. The saturationthroughput in the time-varying case is lower than that in thetime-invariant case. This is expected since the channel data ratemay be reduced in the time-varying condition due to fadingand signal variation. Similarly, we can obtain the maximal

nonsaturation throughput in the time-invariant channel caseand the time-invariant channel case as 0.9107 and 0.8095,respectively.

A. Achievable Throughput

We compare our GC-MAC, which uses a group-based coop-erative sensing scheme (GCSS) with an accuracy priority coop-erative sensing scheme (ACSS) [11] and an efficiency prioritycooperative sensing scheme (ECSS) [13]. In the ACSS, everycooperative SU monitors a single channel during each sensingperiod. The main focus of this scheme is to improve sensingaccuracy of a PU’s activity. In the SCSS, the cooperative SUsare assigned to sense different channels simultaneously forthe sensing efficiency enhancement. This sensing operationassumes that the sensing of each channel by a single SU isaccurate, which however may be difficult to achieve in practicalCR networks.

1) Time-Invariant Channel Case: Fig. 3 shows the through-put comparison among GCSS, ACSS, and ECSS in the time-invariant channel case when p = 2/3 and 1/2. In this example,the sensing accuracy requirement is set as Pf, th = 0.05. It isobserved that the achievable throughput in all three schemesincreases with higher channel availability p, which is intu-itively understandable. The result indicates that the GCSS isable to achieve a much higher throughput than ACSS andECSS. This is because GCSS is able to search and find more


Fig. 3. Saturation throughput in the time-invariant channel case with dif-ferent p.

Fig. 4. Nonsaturation throughput in the time-invariant channel case withdifferent p.

spectrum opportunities. When the number of the cooperativeSUs becomes larger, there is a higher chance to find the avail-able channels, which leads to less sensing overhead. In addition,the ECSS uses all SUs to sense different channels, which causesless sensing accuracy of a single channel and leads to lowerthroughput. Comparatively, the proposed GCSS chooses theoptimal number of teams and the number of SUs in each team.In this case, sensing overhead is significantly reduced, andthroughput increases. As a consequence, our proposed GCSSis able to achieve high sensing efficiency with low sensingoverhead.

Fig. 4 shows the nonsaturation throughput comparisonamong GCSS, ACSS, and ECSS in the time-invariant channelcase when p = 2/3, 1/2. Again, the Pf, th = 0.05 is assumed as0.05. It can be observed that the GCSS substantially outper-forms the other two schemes. In addition, we notice that it willobtain higher throughput if the channel availability p becomeslarger.

2) Time-Varying Channel Case: Figs. 5 and 6 show the satu-ration and nonsaturation throughput comparison among GCSS,ACSS, and ECSS in the time-varying channel case when p =2/3, 1/2 and Pf, th = 0.05. The comparison indicates that GCSSis able to achieve higher throughput than ACSS and ECSS.

Fig. 5. Saturation throughput in the time-varying channel case with dif-ferent p.

Fig. 6. Nonsaturation throughput in the time-varying channel case withdifferent p.

This is because GCSS is able to detect and find more spectrumopportunities even when the channel is dynamic. When thenumber of cooperative SUs becomes larger, our scheme notonly finds the available channel quicker but also chooses thechannel with a maximal rate if more than one available channelsare found. Moreover, with the comparison to ECSS, GCSS hasthe advantage of reducing sensing overhead. As a consequence,the proposed GCSS achieves higher throughput in the time-varying channel case.

In addition, we illustrate the achievable throughput com-parison among GCSS, ACSS, and ECSS under the complex-valued signal model. Figs. 7 and 8 show the saturation andnonsaturation throughput comparison among GCSS, ACSS,and ECSS in the time-varying channel case, respectively. Weobserve that GCSS can also obtain higher throughput than thatin ACSS and ECSS. This observation indicates the effectivenessof our proposed MAC protocol in both of the real-valued andcomplex-valued signal models.

B. Sensing Overhead

1) Time-Invariant Channel Case: Fig. 9 shows sensingoverhead among GCSS, ACSS, and ECSS in the time-invariant


Fig. 7. Saturation throughput comparison under complex-valued signal sys-tem in the time-varying channel case with different p.

Fig. 8. Nonsaturation throughput comparison under complex-valued signalsystem in the time-varying channel case with different p.

Fig. 9. Sensing overhead in the time-invariant channel case with different pfor the saturation situation.

channel case for saturation situation. It is observed that GCSSgenerates the lowest sensing overhead. This can be explainedas follows. the GCSS selects the SUs to cooperate by usingthe SU-selecting algorithm. The algorithm chooses the SUs

Fig. 10. Sensing overhead in the time-invariant channel case with different pfor the nonsaturation situation.

Fig. 11. Sensing overhead in the time-varying channel case with different pfor the saturation situation.

with a low channel available probability P00 for the cooper-ative sensing. This operation can substantially reduce sensingoverhead by avoiding the temporary stopping of the ongoingtransmissions when their channels are occupied by PUs. Com-paratively, ACSS and ECSS have no similar mechanisms andhence generate higher sensing overhead. Fig. 10 shows thesensing overhead for a nonsaturation situation. Similar obser-vations and conclusions can be made. In addition, we noticethat sensing overhead decreases when the channel availability pbecomes larger. With more channel availability, there are morechances to find spectrum opportunities in a fixed period and,hence, less sensing overheads.

2) Time-Varying Channel Case: Considering the time-varying channel case, Figs. 11 and 12 show the sensing over-head with different channel availability p under saturationand nonsaturation situations, respectively. It is clear that sens-ing overhead becomes lower when the channel availability pincreases. Again, the proposed GCSS incurs lower sensingoverhead than ACSS and ECSS. With the time-varying channel,we have considered the channel dynamics and rate variation inselecting appropriate SUs to perform sensing. Following thisway, sensing overhead in traditional cooperative sensing can bepartially avoided.


Fig. 12. Sensing overhead in the time-varying channel case with different pfor the nonsaturation situation.

VIII. CONCLUSION

We have designed an efficient MAC protocol with selec-tive grouping and cooperative sensing in CR networks. Inour protocol, the cooperative MAC can quickly discover thespectrum opportunities without degrading sensing accuracy. AnSU-selecting algorithm is proposed for specifically choosingthe cooperative SUs to substantially reduce sensing overheadin both time-invariant and time-varying channel cases. Weformulate the throughput maximization problems to determinethe crucial design parameters and to investigate the tradeoffbetween sensing overhead and throughput. Simulation resultsshow that our proposed protocol can significantly reduce sens-ing overhead without degrading sensing accuracy.

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Yi Liu received the Ph.D. degree from South ChinaUniversity of Technology, Guangzhou, China, in2011.

He was then with the Institute of Intelligent Infor-mation Processing, Guangdong University of Tech-nology, Guangzhou. Since 2011, he has been withthe Singapore University of Technology and De-sign, Singapore, where he is currently a PostdoctoralResearcher. His research interests include cognitiveradio networks, cooperative communications, smartgrids, and intelligent signal processing.


Shengli Xie (M’01–SM’02) received the M.S. de-gree in mathematics from Central China NormalUniversity, Wuhan, China, in 1992 and the Ph.D.degree in control theory and applications from SouthChina University of Technology, Guangzhou, China,in 1997.

He is currently a Full Professor and the ViceHead of the Institute of Automation and Radio Engi-neering with Guangdong University of Technology,Guangzhou. He is the author or co-author of twobooks and more than 70 scientific papers in journals

and conference proceedings. His research interests include automatic controland blind signal processing.

Dr. Xie received the Second Prize in China’s State Natural Science Award in2009 for his work on blind source separation and identification.

Rong Yu (S’05–M’08) received the Ph.D. degreefrom Tsinghua University, Beijing, China, in 2007.

He was then with the School of Electronic andInformation Engineering, South China University ofTechnology, Guangzhou, China. Since 2010, he hasbeen with the Institute of Intelligent InformationProcessing, Guangdong University of Technology,Guangzhou, where he is currently an Associate Pro-fessor. He is the co-inventor of over ten patentsand the author or co-author of over 50 internationaljournal and conference papers. His research interests

include wireless communications and networking, cognitive radio, wirelesssensor networks, and home networking.

Dr. Yu is currently serving as the Deputy Secretary General of the Internet ofThings (IoT) Industry Alliance, Guangdong, China, and the Deputy Head of theIoT Engineering Center, Guangdong. He is a member of the Home NetworkingStandard Committee in China, where he leads the standardization work of threestandards.

Yan Zhang (M’05–SM’10) received the Ph.D.degree from Nanyang Technological University,Singapore.

He is currently with the Simula Research Labo-ratory, Lysaker, Norway, and is an Adjunct Asso-ciate Professor with the University of Oslo, Oslo,Norway. His recent research interests include cog-nitive radio, smart grid, and machine-to-machinecommunications.

Dr. Zhang is an Associate Editor and a GuestEditor for a number of international journals. He

serves as an Organizing Committee Chair for many international conferences.

Chau Yuen (SM’12) received the B.Eng. and Ph.D.degrees from Nanyang Technological University,Singapore, in 2000 and 2004, respectively.

In 2005, he was a Postdoctoral Fellow withLucent Technologies Bell Labs, Murray Hill, NJ,USA. In 2008, he was a Visiting Assistant Professorwith Hong Kong Polytechnic University, Kowloon,Hong Kong. From 2006 to 2010, he was with theInstitute for Infocomm Research, Singapore, as aSenior Research Engineer. Since 2010, he has beenan Assistant Professor with the Singapore University

of Technology and Design, Singapore.Dr. Yuen serves as an Associate Editor for the IEEE TRANSACTIONS ON

VEHICULAR TECHNOLOGY. He received the IEEE Asia–Pacific OutstandingYoung Researcher Award in 2012.

An Efﬁcient MAC Protocol With Selective Grouping and...

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