AN OPTIMUM RELAY SELECTION FOR COOPERATIVE TRANSMISSION ANDSPECTRUM SENSING IN COGNITIVE NETWORKS

Hasan Kartlak1, Cengiz Bektas2, Niyazi Odabasioglu1, and Aydın Akan1

1 Department of Electrical and Electronics Eng., Istanbul UniversityAvcilar, 34320, Istanbul, Turkey, hasank@ieee.org, {niyazio,akan}@istanbul.edu.tr

2 Department of Satellite Communication and Remote SensingIstanbul Technical University, Istanbul, Turkey, cengizbektas@itu.edu.tr

ABSTRACTIn this paper, we propose a jointly optimized relay selectionscheme for both cooperative transmission and spectrumsensing purposes for cognitive radio networks. Our aimis to select a relay to achieve the sufficient performancesof cooperative transmission and spectrum sensing for thesecondary network while ensuring the quality of service(QoS) of primary user. We present error performance oftransmission and probability of detection for spectrum sens-ing via computer simulations. Results show that our jointlyoptimized scheme provides encouraging performance forboth transmission and spectrum sensing in cognitive radionetworks.

Index Terms— cooperative communication, cognitiveradio, spectrum sensing, relay selection.

I. INTRODUCTIONCognitive radio is the state of the art wireless commu-

nication technique which allows unlicensed users to utilizelicensed users’ assigned but unused radio spectrum. Assuch, it provides efficient usage of radio spectrum resources[1]. For the implementation of cognitive radio, interferencetemperature is proposed as a metric to quantify and managethe interference between primary user (PU) and secondaryuser (SU) [2]. If the interference from SU to PU is less than athreshold, the SU can access the licensed spectrum togetherwith the PU. Meanwhile, the desired quality of service (QoS)of primary transmissions will be satisfied [2]. Eventually,very low transmit power level is allowed for SUs, and thusthroughput of SU will be very limited to satisfy high QoSfor the PU. In order to cope with these constraints, improvedtransmission techniques as well as sensing the availablespectrum of the PU are necessary to achieve the desiredcommunication performance in cognitive radio networks.

This work was supported by The Research Fund of The University ofIstanbul. Project numbers: 3898 and UDP-16720, T-16723.

This work was supported by Alcatel-Lucent Turkey.

978-1-4673-2017-7/12/$31.00 c⃝2012 IEEE

Cooperative communication improves the system through-put over fading channels [3]. In cooperative communica-tion, a key issue is how to choose which terminal in thenetwork will be used as a relay. Therefore vast amount ofstudies have been conducted and still undergoing on relayselection problem in cooperative communication systems.A pioneering study on relay selection is proposed in [4]which shows the effect of relay selection into the systemperformance. Recently, other relay selection schemes withimproved performance have been proposed [5]-[6]. Relayselection problem becomes much more complicated in cog-nitive radio networks compared to the classical cooperativecommunication systems, because of interference limitationsfrom SU to PU. In some recent studies, cooperative trans-mission techniques have also been proposed for cognitiveradio systems [7]-[8].

An important component of cognitive radio networks issensing the availability of the radio spectrum. Spectrumsensing improves the efficiency of the frequency spectrum bydetecting assigned but unused frequency bands of the radiospectrum. SU can communicate over these unused frequencybands of PU. In recent years, different spectrum sensingmethods have been proposed [9]. Literature covers energy-based spectrum sensing techniques, higher-order statisticsbased methods, and cyclo-stationarity based methods [10]-[11]. Sensing time is a key issue in spectrum sensing aswell as sensing the spectrum correctly. Energy based tech-niques are mostly preferred due to their low computationalcomplexity.

Computational load of the spectrum sensing techniqueeffects the duration of the sensing. From this point of view,energy-based spectrum sensing technique using WaveletTransform (WT) is advantageous compared to other ap-proaches. WT based techniques have computational com-plexity of order N [10] for N -sample long sequence,whereas fast Fourier transform based techniques have com-putational complexity of order N(log2N). Therefore, WTbased spectrum sensing has widely been applied [12]-[13].Recent studies show that performance of spectrum sensingcan be improved with the assistance of a relay [14]. Similar

International Workshop on Cognitive and Cooperative Wireless Communication Systems 2012

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to cooperation for transmission, a relay in the network alsosenses the spectrum in addition to SU and transmits the resultto SU. Then SU combines these results using a soft or harddecision method and reaches the final result.

In this paper, we propose a jointly optimized relay selec-tion scheme for both cooperative transmission and coopera-tive spectrum sensing in cognitive radio networks, aiming toreduce the computational burden of the selection procedure.Hence, we select an optimum relay with only one selectionalgorithm for two tasks. In energy based spectrum sensingalgorithm, WT is used because of its low computational load.The transmission and sensing performances of the proposedmethod are presented by means of computer simulations.Results demonstrate that our proposed method gives suffi-cient performance for both cooperative spectrum sensing andtransmission tasks.

Rest of the paper is organized as follows. The cognitiveradio network system model is explained in section 2,simulation results are given in section 3, and section 4concludes the paper.

II. RELAY SELECTION FOR COGNITIVE RADIONETWORKS

In this section we present our jointly optimum relayselection scheme for both cooperative communication andspectrum sensing in cognitive radio networks. Our selectionalgorithm combines two steps; (i) choosing the “best relay”for cooperative transmission, (ii) choosing the “best relay”for spectrum sensing, and obtains only a single relay toperform both tasks. Clearly, the selected relay will be sub-optimal for either transmission or spectrum sensing. In thefollowing, we briefly explain our cognitive radio networkmodel with cooperation, relay selection for cooperativetransmission and cooperative spectrum sensing.

II-A. Cognitive Radio Network ModelIn the cognitive radio system, we consider two networks

as shown in Fig. 1; the first one is a primary network andthe second one is an amplify-and-forward (AF) cooperativecommunication secondary network. When the primary usersends data to a primary destination (PD), secondary trans-mitter (ST) sends its data to a secondary destination (SD) atthe same time.

In order to implement the cooperation, we consider the“receive diversity protocol” presented in [3]. We assumedthat the transmitters, PT, ST , destinations, PD,SD, andrelay terminals, R1, R2, · · · , RM have one transmit and onereceive antennas. In the first phase of this protocol, the STtransmits the data to both relay and the SD. Then in thesecond phase, the ST stays silent, while the relay amplifiesand transmits the data. In cognitive radio systems with nospectrum sensing task, the transmit power of secondarytransmitter should be limited to maintain a desired QoS ofprimary transmissions.

Secondary

Transmitter

ST SD

R1

Secondary

Destination...

Relays

R2

RN

h1TR

h2TR

hNTR

h1RD

h2RD

hNRD

hTD

Primary

Transmitter

PT PD

Primary

Destination

hTD

Secondary

Network

Primary

Network

Fig. 1. Cognitive radio network model.

PT transmits the signal xp to PD where the transmitpower of PT is denoted by PPT and data rate by Rp.Similarly, ST transmits the signal xs to SD with transmitpower PST . The outage probability of primary transmissionsis limited by a threshold PThr, because we need to guaranteethat QoS of primary transmissions will not be decreased.

II-B. Relay Selection for Cooperative TransmissionIn our study, a static method is used to control the ST’s

PST and relay’s transmit powers PSRias in [15]:

PST =σ2PT−PDPPT

σ2ST−PDΘ

ρ+ (1)

PSRi =σ2PT−PDPPT

σ2SRi−PDΘ

ρ+ (2)

where σ2PT−PD, σ2

ST−PD and σ2SRi−PD are the fading

variances of the channels from PT to PD and from STto PD respectively, Θ = 2Rp − 1, ρ+ = max(ρ, 0),ρ = (1/(1 − PThr))exp(−(Θ/σ2

PT−PDγPT ) − 1 and γPT

is the transmit signal-to-noise ratio (SNR) at PT .In first phase, signals received at relay i and the destination

SD are given respectively by the following;

rSRi =√PSThST−SRixs +

√PPThPT−SRixp + nSRi

(3)rSD =

√PSThST−SDxs +

√PPThPT−SDxp + nSD (4)

where i shows the index of selected relay. Considering pathlosses and the shadowing effects in these channels,

√PST

and√PPT show the signal powers at the relay and the

destination respectively. hST−SRi , hPT−SRi , hST−SD andhPT−SD represent the complex Gauss fading coefficients forST and PT , Source → Ri and Source → SD channelsrespectively, where nSRi and nSD show zero mean complexGaussian noise with N0/2 variance per dimension.

989

In the second phase, selected relay normalizes the receivedsignal by

√E[|rSRi |2] and transmits to the destination

receiver. The signal at the destination in second phase;

rRDi =√PSRihST−RDi

rSRi√E[|rSRi |2]

+ nRDi (5)√PSRi represents the received signal power at the desti-

nation, considering path losses and the shadowing effectsin Ri → SD channel. hST−RDi represents the complexGauss fading coefficients for this channel, where nRDi isthe zero mean complex Gaussian noise with N0/2 varianceper dimension.

Now let’s consider the relay selection procedures to opti-mize only transmission, only spectrum sensing, and finallyboth transmission and spectrum sensing tasks.(i) The “best relay” for transmission is selected asfollows: In the secondary network transmission, the bestrelay amplifies the ST’s signal and achieves the highestreceived instantaneous signal-to-noise ratio (SINR) at SD.For transmission, the best relay selection criterion is givenas:

Rb = arg maxSINRSD

= arg max|hSRi−SD|2

σ2SRi−PD

(6)

where Rb shows the selected relay. ST → Ri and Ri → SDcases are considered in selection criterion.(ii) The “best relay” for spectrum sensing is selected asfollows: The best relay to sense the spectrum of PU is therelay having the largest channel coefficient. For spectrumsensing, the best relay selection criterion is given by thefollowing:

Rb = arg max|hPT−SRi |2

σ2SRi−PD

(7)

(iii) The “best relay” for both cooperative transmissionand spectrum sensing: In this study, we propose the follow-ing criterion to select a single relay in the secondary networkto perform both transmission and spectrum sensing tasks:The selected relay amplifies the ST’s signal, achieves thehighest received instantaneous signal-to-noise ratio (SINR)at SD and detects the PU. For this optimum solution, therelay selection criterion is given by the following:

Rb = arg max|hSRi−SD|2 + |hPT−SRi |2

σ2SRi−PD

(8)

In section 3, we present computer simulations to show theperformance of our joint relay selection algorithm.

II-C. Cooperative Spectrum SensingThe discrete dyadic wavelet transform giving the wavelet

coefficients is given by the following for a discrete-timesignal x(n) [16]:

xba =∑n

x(n)ψba(n) (9)

Hence, x(n) can be written as [16]:

x(n) =

M∑a=1

N∑b=1

xbaψba(n) (10)

M shows the scale number (M = log2(N)), N shows thedata length, a and b show dilation (scale) and translationindices, respectively. ψb

a(n) shows normalized dilations andtranslations of the mother wavelet function ψ(n). The cut-off frequencies of the initial filters are π/2, and divided by 2in every step, until a desired resolution is reached. Six levelwavelet analysis is used in our simulations.

In our energy-based spectrum sensing technique, as shownin Fig. 2, we calculate the received signal’s energy by theWT. PU is detected by calculating the total energy of thewavelet coefficients in every band and the energy of thesignal in different frequency bands are obtained. If the PU issilent, the received signal will have a low energy containingonly noise. We experimentally determined a threshold whichenables us to detect when the PU is transmitting or not.

Fig. 2. Spectrum sensing algorithm.

where σ̂2r and σ̂2

w are the estimates of the normalizedvariances of the received signal, and only noise in eachscale, respectively. We calculated the normalized variancesof the wavelet coefficients to determine the threshold andestimated σ̂2

w for each SNR ranging from -1 to -10 dB inthe case of noise only. Then we calculate wavelet coefficientsof the received signal and calculate normalized variances ofthe wavelet coefficients σ̂2

r . Finally we compare σ̂2r and σ̂2

w:when σ̂2

r is higher than σ̂2w, we decide that PU is transmitting

in that frequency band.In the cooperative spectrum sensing approach, we calcu-

late the normalized variances of the wavelet coefficients atboth ST and selected the relay Ri. The normalized variancesof the wavelet coefficients are calculated as,

σ̂2r =

|hPT−ST |σ̂2PT−ST + |hPT−SRi |σ̂2

PT−SRi

|hPT−ST |+ |hPT−SRi|

(11)

where σ̂2PT−ST is the normalized variance of the wavelet

coefficient for ST . σ̂2PT−SRi

represents the normalizedvariance of the wavelet coefficient for SRi.

In the third section, we illustrate the probability of de-tection Pd of the proposed method by means of computersimulations.

Pd = Pr(M > λE |H1) (12)

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Here, M shows the normalized variance of the waveletcoefficients obtained from the received signal, λE showsthe threshold calculated by the normalized variances ofthe noise using Monte Carlo trials, H1 is the case whenthe primary user exists in the received signal. In the nextsection, we illustrate the performance of the proposed jointrelay selection for multiple-relay cognitive radio network bymeans of computer simulations.

III. SIMULATION RESULTSIn this section, we present numerical tests by means of

computer simulations via Monte Carlo iterations. We testedour algorithm using for different scenarios:

1) One of the available relays in the secondary network is“randomly chosen” for both cooperative transmissionand spectrum sensing.

2) The “best relay” for transmission is selected and usedfor both transmission and spectrum sensing tasks.

3) The “best relay” for spectrum sensing is selected andused for both tasks.

4) The proposed method of selecting “best relay” fortransmission and spectrum sensing.

We use a frame size of 130 symbols, and assumed thechannel fading coefficients are constant during one frameperiod. We assume that channel state information is knownat the ST, transmitted from the SD to the ST by a feedback,all channels between Source-Destination, Source-Relays,Relays-Destination are Rayleigh fading, and QPSK modu-lation is considered for the data symbols.

We investigate the performance of the cognitive networkmodel shown in Fig. 1 and demonstrate by means of “biterror rate (BER)” plots given in Fig. 3.

It is clearly observed from Fig. 3 that scenario (2) hasbetter error performance than others and scenario (3) hasthe worst error performance for transmission. However, theproposed method, i.e., scenario (4) has a better error perfor-mance than random relay selection, best relay selection forspectrum sensing, and it achieves a close performance to bestrelay for transmission. Performance of the spectrum sensingis tested for the above scenarios by means of probability ofdetection and the results are given in Fig. 4.

Probability of detection values of random selectionmethod are higher than scenario (2), the best relay for trans-mission. On the other hand, our proposed optimal solutionhas better Pd values than random relay and best relay fortransmission. Finally, Pd values of the proposed best relayselection for transmission and spectrum sensing are closer tothose of the best relay for spectrum sensing than others. Assuch, we conclude that the proposed relay selection approachprovides sufficient Pd values.

IV. CONCLUSIONSIn this study, we present a jointly optimum relay selec-

tion scheme for cooperative communication and spectrum

Fig. 3. Error performance curves for 4 different scenarios.

sensing for multiple-relay cognitive radio networks. Themost important advantage of the proposed relay selectionmethod is that the jointly selected relay yields sufficient errorperformance for transmission and sufficient probability ofdetection for spectrum sensing. Simulation results show thatthe proposed method provides an optimum system through-put in multiple-relay cognitive radio networks. In conclusion,our algorithm selects a relay that performs sufficiently interms of secondary network data transmission as well asspectrum sensing of the primary network.

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Fig. 4. Probability of detection for spectrum sensing.

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