The Journal of China Universities of Posts and Telecommunications April 2010, 17(2): 1–7 www.sciencedirect.com/science/journal/10058885
www.buptjournal.cn/xben
SNR-based weighted cooperative spectrum sensing in cognitive radio networks WU Su-wen ( ), ZHU Jin-kang, QIU Ling, ZHAO Ming Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China
Abstract
Cooperative spectrum sensing (CSS) is an approach to confront fading environment. However, in conventional cooperative spectrum sensing (CCSS), the difference among the secondary users (SUs) is ignored when SUs suffer from different fading. In this article, a signal-to-noise ratio (SNR)-based weighted spectrum sensing scheme is proposed to improve the sensing performance. The sensing performance of the weighted spectrum sensing scheme is then derived. Considering the minor contribution of the SUs with small weighted factor, a selective CSS scheme is proposed, where SUs with low SNR are not selected into cooperative spectrum sensing. The simulation results confirm the analytical results. And the performance of weighted scheme is better than that of conventional schemes. In the case where the SNR of SUs are randomly distributed, the performance of selective scheme is almost the same as the weighted scheme while the number of cooperative SUs is reduced to save the consumption of system resource in cooperation with little additional complexity. Keywords cooperative spectrum sensing, SNR-based weighted, secondary users
1
Introduction
With the increasing demands of radio spectrum, cognitive radio (CR) has attracted more and more attention recently [1]. CR provides a way of using limited radio spectrum efficiently. In CR networks, the SUs in the network are allowed to borrow the unused radio spectrum from primary users (PUs). Hence, one of the most important components of CR technology is to detect whether there are primary users currently using the spectrum. The detection technology is called spectrum sensing. If the SU detects the spectrum hole in the network, it can utilize the vacant spectrum without any interference to the PU. One of the major challenges of implementing spectrum sensing is detection performance. Failing to detect the presence of the PU may cause significant interference to PU, and false alarms result in inefficient utilization of the spectrum. A single SU may fail to detect the presence/absence of the PU due to fading. To avoid this, CSS was proposed in
Received date: 22-07-2009 Corresponding author: WU Su-wen, E-mail:
[email protected] DOI: 10.1016/S1005-8885(09)60437-4
Refs. [2–4]. The cooperative scheme can improve the sensing performance by exploiting the sensing diversity. However, in most of the schemes, all the SUs are treated equally regardless that the SNR of all the SUs are same or not. Obviously, it is not efficient, because it does not utilize the SNR difference of the SUs. Recently, attention has been directed to CSS with considering the difference of SUs [5–8]. In Ref. [5], the credibility of SUs is considered and Dempster-Shafer’s evidence theory was used to make final decision. Fuzzy theory was considered in Ref. [6]. Ruiliang Chen et al. introduced a reputation-based mechanism to the final decision. The reputation was updated after each sensing process and the weighted factor was updated according to the reputation [7]. In Ref. [8], the weighted factor was set according to the SNR of the SUs. However, in the scheme, each SU reported its own energy statistic to the fusion center where the cost of report was too great and it was not practical. While in this article, the binary decision reported by the SUs is considered. In this work, unlike the CCSS scheme, the authors consider the SNR difference of the SUs, and propose a SNR-based weighted cooperative spectrum sensing (WCSS) scheme. If
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The Journal of China Universities of Posts and Telecommunications
the SNR of one SU is high, its decision is reliable and its contribution to the final decision should be great. Thus, the weighted factor assigned to this SU should be large. The authors define the weighted factor relative to the SNR, and derive the detection probability and false alarm probability of the WCSS scheme. Note that in earlier works, the performance of weighted spectrum sensing has not been derived. Moreover, considering the minor contribution of the SU with low SNR to the final decision, the authors propose a selective weighted cooperative spectrum sensing (SWCSS) scheme. In this scheme, the SUs with SNR higher than the SNR threshold are selected into cooperation. From the simulation results, the performance of WCSS scheme is better than that of CCSS scheme. Also, if the SNR threshold is set appropriately, the performance of the SWCSS scheme is almost the same as the WCSS scheme, while the number of the SUs in cooperation is reduced. Thus, the resource consumed in the CSS scheme is reduced. The challenge is how to set the SNR threshold. Moreover, the selection is based on the SNR, thus the additional complexity is little. The remainder of this article is organized as follows. The system model is given in Sect. 2, and the WCSS scheme and SWCSS scheme are proposed in Sect. 3. In Sect. 4, the simulation results are shown to verify the performance. Finally, the conclusions are drawn in Sect. 5.
2
System model
2.1
2010
Local spectrum sensing
The detection problem for local sensing at SU i can be expressed by a binary hypothesis test H 0 : yi (t ) = ni (t ) ⎫ (1) ⎬ H1 : yi (t ) = hi (t ) s (t ) + ni (t ) ⎭ where yi (t ) is the received signal by the SU i, and s (t ) is
the PU’s transmitted signal, ni (t ) denotes the additive white Gaussian noise (AWGN) at SU i and is modeled as complex Gaussian random variables, and hi (t ) is the channel coefficient between the PU and the SU i. Moreover, channels corresponding to different SUs are assumed to be independent. H 0 is a null hypothesis, which denotes that there is no presence of PU in a current spectrum band. H1 is the alternative hypothesis, which indicates that there exists the PU. In this article, the detection metrics are considered such as the detection probability and false alarm probability, which are P ( D1 | H1 ) and P ( D1 | H 0 ) , respectively. In general, the signal of PU is unknown. It has been found that the optimal detector for detecting a weak unknown signal is the energy detector [10]. With the energy detection scheme, the SU makes the final decision ui based on the energy of received signal,
⎧1; Yi≥λi ui = ⎨ ⎩0; Yi < λi
(2)
where Yi = ∫ yi2 (t )dt denotes the energy of received signal, T
The process of CSS is divided into two phases (Fig. 1). In the first phase, every local SU performs local spectrum sensing independently. In the second phase, every local SU sends its detection results to the fusion center through dedicated control channels firstly [9], and then the fusion center makes a final decision on whether the PU is present or not.
0
T is the sensing time. λi is the local energy threshold for SU i. It is assumed that the energy threshold of all SUs is the same, i.e. λi = λ (i = 1,2,..., N ) . For SU i with energy detection, the detection probability and false alarm probability over AWGN channel are given by the following expressions [3,11]: Pd,i = P ( D1 | H1 ) = Qρ γ i , λi ⎫ ⎪ (3) Γ( ρ , λi / 2) ⎬ Pf ,i = P ( D1 | H 0 ) = ⎪ Γ( ρ ) ⎭
(
)
where γ i denotes the SNR at the SU i, respectively. Γ(⋅, ⋅) is the incomplete gamma function, Γ(⋅) is the gamma function and Qρ (⋅, ⋅) is the generalized Marcum Q-function,
and ρ is the time-bandwidth product.
Fig. 1
System model of WCSS
For SU i with energy detection over Raleigh fading channel, the detection probability and false alarm probability are [3,11]:
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WU Su-wen, et al. / SNR-based weighted cooperative spectrum sensing in cognitive radio networks u −1
⎛ − 2(1λ+i γ ) ⎫ i ⎜e −⎪ ⎪ n=0 ⎝ ⎪ n λ ⎞ ⎪⎪ ⎞ − i u −2 1 ⎛ λ γ i i e 2 ∑ ⎜⎜ (4) ⎟⎟ ⎟⎟ ⎬ n ! + 2(1 γ ) n=0 i ⎠ ⎠ ⎝ ⎪ ⎪ Γ( ρ , λi / 2) ⎪ Pf ,i = ⎪ Γ( ρ ) ⎪⎭ where γ i denotes the average SNR over Raleigh fading Pd,i = e
−
λi u − 2 2
1 ⎛ λi ⎞
∑ n! ⎜⎝ 2 ⎟⎠
n
⎛1+ γi ⎞ +⎜ ⎟ ⎝ γi ⎠
channel at the SU i. 2.2
Final decision at the fusion center
In this phase, all the SUs send their final decisions ui (i = 1,2,..., N ) to the fusion center through dedicated control channels. Then the fusion center combines those local decisions to make the final decision u as follows: N ⎧ ⎪1; ∑ wiui≥k ⎪ i =1 u=⎨ (5) N ⎪0; < w u k ∑ i i ⎪⎩ i =1 where wi denotes the weighted factor of SU i and k denotes the decision threshold. The scheme is called ‘k-out-of-N’ rule. In the CCSS schemes, the weighted factor of each SU is equal, i.e. wi = 1, i = 1,2..., N . If k = 1 , then the scheme is ‘OR’ rule. If k = N , the scheme is ‘AND’ rule. The scheme is majority rule if k = ⎢⎡ N 2 ⎥⎤ , where ⎢⎡ x ⎥⎤ denotes the smallest integer greater than x. In this article, the ‘OR’ rule is considered, i.e. k =1.
3 Weighted cooperative spectrum sensing In this section, the authors determine the weighted factor in the fusion center for different SUs, and propose another selective CSS scheme.
wi =
3
γ
i (6) 1 N γn ∑ N n =1 where the γ n denote the SNR of the SU n. The definition is
a little different from the weighted factor in Ref. [8]. In Ref. [8], the sum of the weighted factors is 1. While in this article, the sum is N. This is due to the final fusion scheme [cf. Eq. (5)]. The weighted factor would affect the final fusion result, thus the final performance such as Qd and Qf should be derived, where the Qd and Qf
denote final detection
probability and false alarm probabitity respectively in the fusion center. To derive the detection probability, the authors derive the missed probability first. The PU exists, but the final decision is u = 0 , which means
N
∑wu i =1
i i
