PERFORMANCE MEASUREMENT OF ONE-BIT HARD DECISION FUSION SCHEME FOR COOPERATIVE SPECTRUM SENSING IN CR

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1 Int. Rev. Appl. Sci. Eng. 8 (2017) 1, 9 16 DOI: / PERFORMANCE MEASUREMENT OF ONE-BIT HARD DECISION FUSION SCHEME FOR COOPERATIVE SPECTRUM SENSING IN CR M. AL-RAWI University of Ibb, Yemen muhrawi@yahoo.com This paper measures the performance of cooperative spectrum sensing, over Rayleigh-fading channel and additive white Gaussian noise, based on one-bit hard decision scheme for both AND and OR rules. Three measures based on energy detection are considered including effect of false alarm probability, effect of number of users, and effect of number of samples. Simulation results show that the detection probability increases with increasing false alarm probability, number of users, and number of samples for both AND and OR rules. Also, the performance of OR rule is better than the performance of AND rule. eywords: hard decision scheme, cooperative spectrum sensing, cognitive radio 1. Introduction Cognitive radio (CR) is a new paradigm in wireless communications that promises an enhanced utilization of the limited spectral resource. The basic idea is to employ a hierarchical model, where primary ( licensed) and secondary (unlicensed) users coexist in the same frequency spectrum. The cognitive radio system include several functions, the important function is spectrum sensing which detects the unused channel. There are number of schemes for spectrum sensing lie energy detection, matched filter detection, and cyclostationary detection. One of the most critical issues of spectrum sensing is the hidden terminal problem, which happens when the cognitive radio is shadowed over the sensing channel. In this case, the cognitive radio cannot reliably sense the presence of the primary user (PU) due to the very low signalto-noise ratio (SNR) of the received signal. Then, this cognitive radio assumes that the observed channel is vacant and begins to access this channel while the PU is still in operation. To address this issue, multiple cognitive radios can be coordinated to perform spectrum sensing cooperatively. Several wors [1 7] have shown that cooperative spectrum sensing can greatly increase the probability of detection in fading channels. In general, cooperative spectrum sensing is performed as follows: Step 1: Every cognitive radio performs local spectrum measurements independently and then maes a binary decision. Step 2: All the cognitive radios forward their binary decisions to a common receiver which is an access point (AP) in a wireless local area networ (LAN) or a base station (BS) in a cellular networ. Step 3: The common receiver combines those binary decisions and maes a final decision to infer the absence or presence of the PU in the observed band. It can be seen that cooperative spectrum sensing will go through two successive channels: 1) sensing channel (from the PU to cognitive radios); and 2) reporting channel (from the cognitive radios to the common receiver). The merit of cooperative spectrum sensing primarily lies in the achievable space diversity brought by the independent sensing channels, namely sensing diversity gain, provided by the multiple cognitive radios. Even though one cognitive radio may fail to detect the signal of the PU, there are still many chances for other cognitive radios to detect it. With the increase of the number of cooperative cognitive radios, the probability of missed detection for all the users will be extremely small. Another merit of cooperative spectrum sensing is the mutual benefit by communicating with each other to improve the sensing perfor- This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided the original author and source are credited, a lin to the CC License is provided, and changes if any are indicated The Author(s) ISSN

2 M. AL-RAWI mance. When one cognitive radio is far away from the PU, the received signal may be too wea to be detected. However, by employing a cognitive radio who is located nearby the PU as a relay, the signal of the PU can be detected reliably by the far user. To implement the above three steps, seven elements of cooperative sensing are presented as follows: 1) Cooperation model: is concerned with how CR users cooperate to perform sensing. 2) Sensing technique: this element is crucial in cooperative spectrum sensing to sense primary signals by using signal processing techniques 3) Hypothesis testing: in order to decide on the presence or absence of a PU, a statistical test is performed to get a decision on the presence of PU. 4) Control channel and reporting: is used by CR users to report sensing result to the fusion center (FC). 5) Data fusion: is a process of combining local sensing data to mae cooperation decision. 6) User selection: in order to maximize the cooperative gain, this element provides us the way to optimally select the cooperating CR users. 7) nowledge base: means a prior nowledge included PU and CR user location, PU activity, and models or other information in the aim to facilitate PU detection. 2. Classification of cooperative sensing The cooperative spectrum sensing can be classified into three categories based on how cooperating CR users share the sensing data in the networ: centralized [8 10], distributed [10, 11], and relay-assisted [10, 12, 13]. In centralized cooperative sensing, a central identity called fusion center (FC) controls the three-step process of cooperative sensing. First, the FC selects a channel or a frequency band of interest for sensing and instructs all cooperating CR users to individually perform local sensing. Second, all cooperating CR users report their sensing results via the control channel. Then the FC combines the received local sensing information, determines the presence of PUs, and diffuses the decision bac to cooperating CR users. The distributed cooperative sensing does not rely on a FC for maing the cooperative decision. In this case, CR users communicate among themselves and converge to a unified decision on the presence or absence of PUs by iterations. In this manner, this distributed scheme may tae several iterations to reach the unanimous cooperative decision. In relay-assisted cooperative sensing, since both sensing channel and report channel are not perfect, a CR user observing a wea sensing channel and a strong report channel and a CR user with a strong sensing channel and a wea report channel, for example, can complement and cooperate with each other to improve the performance of cooperative sensing. 3. System model and data fusion 3.1. System model Let us consider a cognitive radio networ, with cognitive users (indexed by = {1, 2,, }) and a common receiver, as shown in Fig. 1. The common receiver is introduced as a BS which manages the cognitive radio networ and all associated cognitive radios. Each cognitive radio performs local spectrum sensing independently. Before the CRs use the licensed spectrum, itshould detect whether the PU is present or not by using any method of spectrum sensing. When a CR user is sensing the spectrum channel of the PU in the cognitive radio networ, there are two hypotheses for the received signal X(n): absent or present, which are denoted by H 0 and H 1, respectively. Then, H 0 : X (n) = W (n), (1) H 1 : X (n) = h s(n) + W (n), (2) where s(n) are samples of the transmitted signal (PU signal), W (n) is the receiver noise for the th CR user that is additive white Gaussian noise (AWGN), and h, is the complex channel gain of the sensing channel between the PU and the th cognitive radio. The local detector (energy detector (ED)) decides whether the signal exists according to the signals that are received, and sends to the center their decisions Fig. 1. System model 10 Int. Rev. Appl. Sci. Eng. 8, 2017

3 PERFORMANCE MEASUREMENT OF ONE-BIT HARD DECISION FUSION SCHEME upon which the center maes the final decision. The observed energy by the th CR user is given by [14] M 1 2 E = x ( n), (3) M m = 1 where M denotes the number of collected samples. Let λ be the local decision threshold for each CR user, then the local false alarm probability, which defines the probability of the algorithm falsely declaring the presence of primary signal (P f, ) under hypothesis H 0, can be obtained from Eq. (3) as Pf = Pr{Decision = H1\ H0} = Pr{ Ex ( ) > λ\ H0} (4) M λ M =Γ, / Γ, where Γ( ) and Γ(, ) are complete and incomplete gamma functions, respectively. The detection probability, which defines the probability of the algorithm correctly detecting the presence of primary signal (P d ) under hypothesis H 1, is given by the following formula [15]: Pd = Pr{Decision = H1\ H1} = Pr{ Ex ( ) > λ\ H1} (5) = Q 2 γ, λ, M /2 m ( ) where γ is the signal to noise ratio (SNR), Q M/2 (, ) is the generalized Marcum Q-function. Under Rayleigh-fading, the probability distribution function of is exponentially distributed as: f (γ) = (1/γ) exp{ γ/ γ }, (6) where γ is the average SNR. The average P d in this case, P d, Ray is given as [16]: d,ray M /2 0 λ ( M /2) 2 2 ( ) P = Q 2 γ, λ f( γ)dγ 3.2. Data fusion ( M /2) 1 1 λ 1+ γ = e + n! 2 γ n= 0 λ λ ( M /2) γ 2 1 γλ e e. n! 2+ 2γ n= 0 (7) In cooperative sensing, data fusion is a process of combining local sensing data for hypothesis testing, which is also an element of cooperative sensing. Depending on the control channel bandwidth requirement, reported sensing results may be of different forms, types, and sizes. In general, the sensing results reported to the FC or shared with neighboring users can be combined in three different ways in descending order of demanding control channel bandwidth: i) Soft Combining: CR users can transmit the entire local sensing samples or the complete local test statistics for soft decision. ii) Hard Combining: CR users mae a local decision and transmit the one-bit decision for hard combining. iii) Quantized Soft Combining: CR users can quantize the local sensing results and send only the quantized data for soft combining to alleviate control channel communication overhead. Obviously, using soft combining at the FC can achieve the best detection performance among all three at the cost of control channel overhead while the quantized soft combining and hard combining require much less control channel bandwidth with possibly degraded performance due to the loss of information from quantization. In this paper, only hard combining scheme is considered which is described below in details Hard decision fusion in centralized sensing In this scheme, each user decides on the presence or absence of the PU and sends a one bit decision to the data fusion center. The main advantage of this method is its easiness in the sense that it needs limited bandwidth. When binary decisions are reported to the common node, three rules of decision can be used, the AND, OR, and MAJORITY rule. The individual statistics Δ i are quantized to one bit with Δ i = {0, 1}. The {1} means that the signal is present, and {0} means that the signal is absent. If the channels between cognitive users and the common receiver are perfect, the false alarm probability Q f, the detection probability Q d and the missing probability Q m for -out-of- rule of the cooperative spectrum sensing are given by [17] i i Qf = P f, i (1 Pf, i), i (8) i i Qd = Pd, i (1 Pd, i), i (9) Q m = 1 Q d, (10) where P f, i, and P d, i are the false alarm probability, and detection probability of the ith CR, respectively. The AND rule decides that a signal is present if all users have detected a signal. The cooperative test using the AND rule can be formulated as follows [17]: Int. Rev. Appl. Sci. Eng. 8,

4 M. AL-RAWI H H 1 0 Δ 1 = = (11) :, :otherwise. By substituting = in (8) and (9), then cooperative false alarm probability Q f and cooperative detection probability Q d of the final decision are obtained as [17]: Q = P (12) Q ( f i ) f, AND,, = (13) ( Pd i ) d, AND,, The OR rule decides that a signal is present if any of the users detect a signal. Hence, the cooperative test using the OR rule can be formulated as follows [17]: H1 : Δ 1, 1 = (14) H0 :otherwise. By substituting = 1 in (8) and (9), then cooperative false alarm probability and cooperative detection probability of the final decision are obtained as [17]: Q Q ( Pf i ) 1 1, (15) f, OR =, ( Pd i ) 1 1, (16) d, OR =, The MAJORITY rule decides that a signal is present if half or more of the users detect a signal. Hence, the cooperative test using the MAJORITY rule can be formulated as follows [17]: H1 : Δ, = 1 2 (17) H0 :otherwise. By substituting = /2 in (8) and (9), then cooperative false alarm probability and cooperative detection probability of the final decision are obtained as [17]: i i Qf, MAJ = P, ( 1, ), i /2 f i Pf i = i (18) i i Qd, MAJ = P, ( 1, ), i /2 d i Pd i = i (19) where the operator represents the floor operator. 4. Simulation results The system model shown in Fig. 1 is investigated by computer simulation over Rayleigh-fading channel and AWGN. The energy detection is used as spectrum sensing technique. The performance of the system is measured using complementary receiver operating characteristic (ROC) curves. The ROC is widely employed in signal detection theory due to the fact that it is an ideal technique to measure the trade-off between the probability of detection and the one of the false alarm. The performances of the system with onebit hard decision for multiuser based on AND and OR rules are measured. Several measures are considered including effect of false alarm probability, effect of the number of users, and effect of the number of samples. Fig. 2. Q d vs average SNR applying AND rule for different values of Q f 12 Int. Rev. Appl. Sci. Eng. 8, 2017

5 PERFORMANCE MEASUREMENT OF ONE-BIT HARD DECISION FUSION SCHEME Fig. 3. Q d vs average SNR applying OR rule for different values of Q f Our results obtained from these measures are demonstrated in the following subsections Effect of false alarm probability The Q d is evaluated with respect to average SNR considering three values of Q f : 0.1, 0.05, and The number of samples is fixed at M = 6 with four users are applied into the energy detector for both AND and OR rules. Figures 2 and 3 show the performances of the system for AND rule and OR rule, respectively. It can be observed that Q d increases when SNR or Q f increases. Higher SNR improves the detection probability of the individual CR which in further improves the Q d after cooperation. Also, it can be observed that the OR rule performs better than AND rule. In case of OR rule, the Q d reaches its optimal value at SNR > 6 db, while, for AND rule, it reaches its optimal value at SNR > 10 db Effect of number of users Figures 4 and 5 show the Q d vs SNR for different number of CR users ( = 1, 4, 6) applying AND rule and Fig. 4. Q d vs average SNR applying AND rule for different Int. Rev. Appl. Sci. Eng. 8,

6 M. AL-RAWI Fig. 5. Q d vs average SNR applying OR rule for different OR rule, respectively, while, Q f = 0.05 and M = 6. It can be concluded that as increases, Q d increases, and OR rule wors better than AND rule for large Effect of number of samples Figures 6 and 7 show Q d vs SNR for different number of samples (M = 2, 4, 6) at Q f = 0.05 and = 4, for AND rule and OR rule, respectively. It can be observed that as M increases, Q d increases. Also, OR rule performs better than AND rule. On the other hand, as M increases, the sensing time increases, while the main aim of spectrum sensing is to achieve a better Q d during lower sensing time Comparison between AND and OR rules Figure 8 shows Q m vs Q f for both AND and OR rules. The number of users is fixed at 8, M = 6, and SNR = 3 db. It can be seen that OR rule wors better than AND rule, so it can be concluded that OR rule is very conservative for CRs to access the licensed band maing the chance of causing interference to PU minimum which decreases Q m. Fig. 6. Q d vs average SNR applying AND rule for different M 14 Int. Rev. Appl. Sci. Eng. 8, 2017

7 PERFORMANCE MEASUREMENT OF ONE-BIT HARD DECISION FUSION SCHEME Fig. 7. Q d vs average SNR applying OR rule for different M Fig. 8. Q m vs Q f applying AND and OR rules 5. Conclusion The performance of cooperative spectrum sensing in CR over Raleigh-fading channel and AWGN was studied. The performances with one-bit hard decision scheme based on AND and OR rules are measured using energy detection technique. Our results show that Q d increases with increasing Q f,, and M. In addition, OR rule performs better than AND rule. References [1] Sun H. (2011), Collaborative spectrum sensing in cognitive radio networs. PhD Dissertation, University of Edinburgh, U. [2] Pudi S., Sundara T., Padmaja N. (2013), Performance analysis of cognitive radio based on cooperative spectrum sensing. International Journal of Engineering Trends and Technology, 4(4). [3] Padmavathi G., Shanmugavel S. (2015), Performance analysis of cooperative spectrum sensing technique for low Int. Rev. Appl. Sci. Eng. 8,

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