Optimal Cooperative Wideband Spectrum Sensing in Cognitive ...


Optimal Cooperative Wideband Spectrum Sensing in Cognitive ...

Optimal Cooperative Wideband Spectrum Sensingin Cognitive Radio NetworksVahid Jamali, Ramezan Ali Sadegh Zadeh, S. Hamid Safavi and Soheil SalariK.N.Toosi University of Technology,Faculty of Electrical and Computer EngineeringTehran, IranEmail: {v jamali k, s.hamid safavi}@ee.kntu.ac.ir, {sadeghz, salari}@eetd.kntu.ac.irAbstract—Several Challenges like low signal-to-noise ratio(SNR), fading and inaccuracy in noise power estimation makespectrum sensing techniques not reliable enough to sense the presenceof primary signals accurately. These challenges substantiatethe application of more robust algorithms by multiple cognitiveusers. Recently, Cooperative spectrum sensing is received somuch attention for handling fading and low SNR problems.Therefore, in this paper, in order to overcome the above practicalproblems, we propose a novel cooperative wideband spectrumsensing framework with LRT-based optimal fusion rule. Simulationresults confirm the effectiveness of our proposed method inimproving the probability of detection.I. INTRODUCTIONThe scarcity of spectrum and its underutilization has led tothe development of CRs for wireless networks. Consideringthe limitations of the natural frequency spectrum, it becomesobvious that the current strategy of static frequency allocationschemes cannot accommodate the requirements of increasingdemands. On the other hand, recent measurements by FederalCommunications Commission (FCC) have shown that 70%of the allocated spectrum in US is not utilized. Consequently,innovative techniques that more efficiently utilize the availablespectrum are needed. Cognitive Radio Network (CRN) arisesto be a tempting solution to the spectral congestion problemby introducing opportunistic usage of the frequency bands thatare not heavily occupied by licensed users [1-3].Spectrum Sensing (SS) has been identified as a key enablingfunctionality to ensure that CRs would not interfere with PrimaryUsers (PUs), by reliably detecting primary user signals.SS techniques in CRNs can be classified as matched filter detection,energy detection and feature detection. Matched filterdetection is a coherent detection method and it maximizes theSNR. However, a priori knowledge of the signal to be detectedis necessary in this method. Feature detection makes use ofthe periodicity of the signal’s statistical characteristics but, itis a computationally expensive method. Energy detection hasbeen adopted as an alternative spectrum sensing method forcognitive radios due to its low computational complexity andnot requiring a priori information of the signal to be detected[4].There are several factors which make energy detector practicallychallenging. First, in many practical situations, SNRlevel may be very low. For example, wireless microphonesoperating in TV bands only transmit signals with a power ofabout 50 MW and a bandwidth of 200 kHz. If SecondaryUsers (SUs) are several hundred meters away from the microphonedevices, the received SNR may be well below -20dB. Secondly, multipath fading and shadowing of wirelesschannels complicate the sensing problem, since they maycause signal power to fluctuate as much as 30 dB. Thirdly,noise/interference level may change with time and location,which yields noise uncertainty [5-8].Recently, a novel scheme based on Bayesian estimationalgorithm has been introduced in [9]. The algorithm is independentof noise power estimation, so noise uncertainty doesnot degrades its performance. However, this scheme cannotmitigate the multi-path fading and shadowing effects yet. Todo so, we propose a cooperative detection scheme based on optimallycombining the SUs decision. In our proposed scheme,each SU detects the availability of spectrum hole based onthe robust Bayesian estimation algorithm, and then sends theirdecisions to the Fusion Center (FC). To further increase thecooperation performance, we address an optimum fusion rulebased on Likelihood Ratio Test (LRT). The combining rule isa function of the probability of false alarm and probability ofdetection of individual CRs. The simulation results illustratethat the proposed framework diminishes the energy detectiondrawbacks.The remainder of this paper is organized as follows. Theproblem statement is described in Section II. The backgroundreview is presented in Section III. Section IV gives theproposed framework. Simulation results are given in SectionV. Conclusions are drawn in Section VI.II. SYSTEM MODEL AND ASSUMPTIONSA. System ModelLet us consider a CRN with M SUs. We assume that the PUsnetwork consists of channels with the same bandwidth. Theobjective is to cooperatively estimate the number of occupiedchannels and determine their locations.We assume that there are K PUs each have a channelbandwidth equal to W and the sensing duration of each channelis T. Therefore, the number of samples is equal to the timebandwidthproduct, i.e. N = TW. Consider y i,m (n) as then’th sample of the i’th channel, received by m’th SU; Hence,y i,m (n) can be represented as:978-1-4577-1177-0/11/$26.00 ©2011 IEEE 371ICUFN 2011

Fig. 1.y i,m (n) =Scheme of SUs, FC and PU location.{ɛi,m (n) H 0α i,m S i,m (n)+ɛ i,m (n) H 1(1)where ɛ i,m (n) is assumed to be complex zero mean Gaussiannoise with variance equal to σ 2 ɛ . s i,m (n) is the primary usersignal of the i’th channel received by m’th SU, which can beformulated as a complex Gaussian variable with zero meanand variance σ 2 i,m . α i,ms are the i.i.d channel coefficients.B. AssumptionsThe following assumptions will be retained for illustrativepurpose and for analytical tractability:̌SUs should calculate the power of each channel and thenvia the proposed robust algorithm, determine the occupancyof each channel. The decisions are to be sent to the fusioncenter.̌Final decision based on combination of local decisionswill be made by FC. The optimal fusion rule is obtained viaLRT.̌Sensing channels (i.e. channels between PU transmittersand SUs) are assumed to be noisy and subject to multipathfading or log-normal shadow-fading. But do to the one bittransmission of SU’s decision; the reporting channels (i.e.channels between SUs and FC) can be treated as Ideal.III. BACKGROUND REVIEWA. Conventional Energy DetectionIn a conventional energy detector, the energy of the receivedsignal of the i’th channel is calculated according toλ i = 1 N∑|y i (n)| 2 (2)Nk=1The estimated energy should be compared with a predefinedthreshold τ i . If λ i > τ i , the channel is considered to beoccupied by PU, else, the channel is decided to be idle.Since we are interested in the low-SNR regime (−22dBrecommended by 802.22 Working Group [10]), to achievepredefined sensing performance, the sample number N isalways large. Therefore, we can use the central limit theoremto approximate λ i as Gaussian [11].{λi ∼ N(σɛ 2 ,σɛ 4 /N ) H 0λ i ∼ N((σs,i 2 + σɛ 2 ), (σs,i 2 + σɛ 2 ) 2 (3)/N ) H 1where σs,i 2 and σ2 ɛ are the primary signal and noise variancesof the i’th channel respectively. Probability of detection (P d )and probability of false alarm (P fa ) are two factors used toevaluate the performance of sensing. P d denotes the probabilitythat a channel is sensed to be occupied when it is actuallyoccupied and P fa is the probability that a channel is sensedto be occupied when it is actually idle. By above assumptions,P d and P fa can be calculated as follows [11].(τi − σ 2 )ɛP fai = Qσɛ 4 / √ (4)N(τi − (σs,i 2 P di = Q+ )σ2 ɛ )(σs,i 2 + σ2 ɛ ) 2 / √ (5)NA common approach is to set the threshold τ based on theestimated noise variance σ 2 ɛ and a tolerable P fa according to(3). However, in practical implementation, due to the limitationof sensing time and fluctuation of noise, the noise variancecould not be accurately estimated which results in severelydegrading the system performance.If we also consider fading, P f a will not change since it isonly a function of noise variance. But P d should be integratedaccording to the SNR distribution [12].B. Bayesian EstimationDue to the fact that conventional energy detection is vulnerableto noise uncertainty, a Bayesian-based estimator hasrecently been proposed to estimate the occupied channels bycalculating and comparing different channels energies [9].For the Bayesian estimation-based energy detection (BEED)method, we take the sample measurements of all channels intoaccount. Let Y (n) be the vector of received signals by a SU.i.e.: Y (n) =[y 1 (n),y 2 (n),,y K (n)] T where y i (n) defined in(1). Let λ =[λ 1 ,λ 2 ,,λ K ] T denote the vector of the observedsignal energies of all the channel for a specific SU, where λ iis calculated due to (2).It is assumed that l out of K channels are occupied. Weset the vector λ to be in a decreasing order for analyticalsimplicity. Therefore, if the sample numbers is infinite, thesmallest K −l items of λ are all equal to σɛ 2 which determinesthe idle channels.λ 1 >λ 2 >...>λ l = λ l+1 = ...= λ K = σ 2 ɛ (6)However, when the number of samples is finite, the observedsample energies are all different. So it is difficult to determinethe number and locations of idle channels merely by comparingthe sample powers. The BEED method is used to estimatethe locations of occupied channels. The implementation ofBEED comprises two steps. First, estimation of the numberof occupied channels (ˆl) is calculated and then, the channelswith the largest ˆl sample energies λ i are considered to beoccupied [9].C. Cooperation between SUsCooperative spectrum sensing can be performed in twomanners. In the traditional method, the local SUs communicate372

all observations directly to a FC where decision processing The number of occupied channels l is estimated is the onei=1and,P d = 1 K∑P dK (18)C m (l) = 1 (log(K − l)+l(log( 2 ))K2Nπ ) k=1where P dk denotes the probability of detection for the k’th+ 12N log(N) (10) channel in FC. Noise uncertainty is modelled as α whereas in[17] α is uniformly distributed in the interval [−1dB, 1dB].is performed. This method often requires a large bandwidthfor the communications channel in order to obtain real-timeresults. In the second method, each SU decides locally whetherminimising (11) i.e.:ˆl = argj=0,1,...,k minBEED j (11)PU is active or not. Then the local decisions are sent to B. Global decisionthe FC where they are combined for global decision making.Assume µ i denotes the local decision vector about i’thThis method does not require the large bandwidth of the firstprimary channel from different SUs which is transmitted tomethod; however, performance is degraded because the centralthe FC. The FC should makes the final decision about theprocessor does not receive all the information. The advantagesoccupancy of the channel based on the general L out ofin cost, reliability, and communications bandwidth, however,M rule which can yield in simpler form like AND, ORmay outweigh the loss of performance.and MAJORITY rule. In section III, the advantages andIn this paper, we consider decision fusion algorithms anddisadvantages of each rule has been investigated, however; inevaluate the performance of the proposed algorithm usingthis section, we exploit optimal choose of L based on LRToptimal counting rule. The most important basic methods in[14-16]. The optimum decision rule is given by the followingcounting rule are: AND rule, OR rule, MAJORITY rule whichlikelihood ratio test:defines as follows [13].ȞOR rule: This rule declares that the PU is active if onlyP (u i |H 1 ) 1P (H 0 )(C 10 − C 00 )(12)one of the SUs says that the PU is active. Therefore, the resultP (u i |H 0 )H 0P (H 1 )(C 01 − C 11 )is the increase in the overall P d and P fa as well.the likelihood ratio and the Bayes optimum threshold is on thěAND rule: If all SUs say that the PU is active, then theleft and right-hand side, respectively. The probability of errorfinal decision declares that the PU is active. As expected, thiscriterion is assumed be Crule decreases the overall P d and P fa as well.00 = C 11 =0and C 10 − C 01 =1.Therefore, by applying Bayes rule to express the conditionaľMAJORITY rule: Another decision rule is that if halfprobabilities, substituting and simplifying, the correspondingof the SUs or more say that the PU is active, then thelog-likelihood ratio test is obtained as:final decision declares that the PU is active. The overallperformance of the system depends on the number of SUsand their detection.log P (H H1|u i ) 1 0P (H 0 |u i )HHowever the optimal fusion rule in FC is derived from LRT0(13)which requires the knowledge of each SU detection (i.e. P dUsing theorem in [16], the optimal decision rule can beand P fa ). Therefore in section III, the LRT-based optimal presented as:fusion rule is used for making final decision in FC.M∑ H 1IV. PROPOSED FRAMEWORKa i,m 0 (14)m=0 H 0A. Local decisionWhere the weight coefficient a i,m corresponding to u i,mAssume that the estimated energies of the channels at m’thdefines asSU are gathered in a vector:λ m =[λ 1,m ,λ 2,m ,...,λ K,m ] T (7)a i,0 = log P (H 1)(15)P (H 0 )where λ K,m is calculated due to (2).After calculating the energies of different channels, BEEDbaseda i,m = log P d mP famif u i,m =1 (16)estimator will be applied according to the lemma in[9]:a i,m = log 1 − P d m1 − P famif u i,m =0 (17)BEED l,m = C m (l)⎛( l∏)() K−l⎞V. SIMULATION RESULTS+ log ⎝1K∑In this section, we present simulation results that illustrateλ i,m λ i,m⎠ (8)K − lthe performance of our proposed framework compared withi=1 i=l+1simple energy detector. We assume there are 40 channelswhere,and every channel has a probability of 5.2% to be occupied.Probability of detection P 0∏d is the used factor to evaluate theλ i,m =1 (9) performance:373

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