 Research
 Open Access
A clusterbased selective cooperative spectrum sensing scheme in cognitive radio
 Nhan NguyenThanh^{1} and
 Insoo Koo^{2}Email author
https://doi.org/10.1186/168714992013176
© NguyenThanh and Koo; licensee Springer. 2013
 Received: 16 February 2013
 Accepted: 11 June 2013
 Published: 24 June 2013
Abstract
Developing an effective cooperative spectrum sensing (CSS) scheme in cognitive radio (CR), which is considered as promising system for enhancing spectrum utilization, is necessary. In this paper, a clusterbased optimal selective CSS scheme is proposed for reducing reporting time and bandwidth while maintaining a certain level of sensing performance. Clusters are organized based on the identification of primary signal signaltonoise ratio value, and the cluster head in each cluster is dynamically chosen according to the sensing data qualities of CR users. The cluster sensing decision is made based on an optimal threshold for selective CSS which minimizes the probability of sensing error. A parallel reporting mechanism based on frequency division is proposed to considerably reduce the time for reporting decision to fusion center of clusters. In the fusion center, the optimal ChairVashney rule is utilized to obtain a high sensing performance based on the available cluster’s information.
Keywords
 Cognitive radio
 Cooperative spectrum sensing
 Cluster
 Selective combination
 Parallel reporting mechanism
1 Introduction
Cognitive radio (CR) has been recently proposed as a promising technology to improve spectrum utilization by enabling secondary access to unused licensed bands. A prerequisite to this secondary access is having no interference to the primary system. This requirement makes spectrum sensing a key function in cognitive radio systems. Among common spectrum sensing techniques, energy detection is an engaging method due to its simplicity and efficiency. However, the major disadvantage of energy detection is the hidden node problem, in which the sensing node cannot distinguish between an idle and a deeply faded or shadowed band [1]. Cooperative spectrum sensing (CSS) which uses a distributed detection model has been considered to overcome that problem [2–12].
Cooperation among CR users (CUs) is usually coordinated by a fusion center (FC). For each sensing interval, CUs will send their sensing data to the FC. In the FC, all local sensing data will be combined to make a final decision on whether the primary signal is present or absent. An optimal data fusion rule was firstly considered by Chair and Varshney in [13]. Despite a good performance, the requirement for knowledge of detection and false alarm probabilities at each local node is still a barrier to the optimal fusion rule.
CSS schemes require a large communication resource including sensing time delay, control channel overhead, and consumption energy for reporting sensing data to the FC, especially when the network size is large. There are some previous works [3–9] that considered this problem. In our previous work [3], we proposed an ordered sequential reporting mechanism based on sensing data quality to reduce communication resources. A similar sequential ordered report transmission approach was considered for reducing the reporting time in [4]. However, the reporting time of these methods is still unpredictably long. In [5], the authors proposed to use a censored truncated sequential spectrum sensing technique for saving energy. On the other hand, clusterbased CSS schemes are considered for reducing the energy of CSS [6] and for minimizing the bandwidth requirements by reducing the number of terminals reporting to the fusion center [7]. In [8], Chen et al. proposed a clusterbased CSS scheme to optimize the cooperation overhead along with the sensing reliability. In fact, these proposed cluster schemes can reduce the amount of direct cooperation with the FC but cannot reduce the communication overhead between CUs and the cluster header. A similar problem can be observed in the cluster scheme in [9], though the optimal cluster size to maximize the throughput used for negotiation is identified. Another consideration of the cluster scheme is to enhance sensing performance when the reporting channel suffers from a severe fading environment [10, 11].
In this paper, we propose a clusterbased selective CSS scheme which utilizes an efficient selective method for the best quality sensing data and a parallel reporting mechanism. The selective method, which is usually adopted in cooperative communications [14, 15], is applied in each cluster to implicitly select the best sensing node during each sensing interval as the cluster header without additional collaboration among CUs. The parallel reporting mechanism based on frequency division is considered to strongly reduce the reporting time of the cluster decision. In the FC, the optimal ChairVashney rule (CV rule) is utilized to obtain a high sensing performance based on the available cluster’s signaltonoise ratio (SNR). In this way, the proposed cooperative sensing will be performed with an extremely low cooperation resource while a certain high level of sensing performance is ensured.
The remainder of this paper is organized as follows. In Section 2, some background on spectrum sensing and optimal fusion rule is described. In Section 3, we present system descriptions. The proposed system model and detailed descriptions of the proposed clusterbased selective CSS scheme are also given in Section 4. Simulation results are shown in Section 5. Finally, the conclusions are drawn in Section 6.
2 Preliminaries
2.1 Local spectrum sensing
where H_{0} and H_{1} correspond, respectively, to hypotheses of absence and presence of the PU signal, x_{ i }(t) represents received data at CU _{ i }, h_{ i } denotes the gain of the channel between the PU and the CU _{ i }, s(t) is the signal transmitted from the primary user, and n(t) is additive white Gaussian noise. Additionally, channels corresponding to different CUs are assumed to be independent, and further, all CUs and PUs share a common spectrum allocation.
where x_{ j } is the j th sample of the received signal and N = 2T W in which T and W correspond to detection time and signal bandwidth in hertz, respectively.
where γ_{ i } is the SNR of the primary signal at the CU.
respectively, where Q(.) is the MarcumQ function, i.e., $Q\left(x\right)=\frac{1}{\sqrt{2\pi}}{\int}_{x}^{\infty}{e}^{}\frac{{t}^{2}}{2}\mathit{\text{dt}}$.
2.2 The optimal fusion rule for global decision
Chair and Varshney provided the optimal data fusion rule in a distributed local hard decision detection system [13]. This optimal rule is in fact the sum of weighted local decisions where the weights are functions of probabilities of detection and false alarm.
Local false alarm probability p_{ f }_{ i } and local detection probability p_{ d }_{ i } are defined in (6) and (7), respectively.
3 System description
 1.
How can the scheme efficiently select the cluster header, which is the node with the best quality for sensing data, for each sensing interval without any extra overhead among nodes in the cluster?
 2.
How can the cluster header optimally make the cluster decision?
 3.
What is the method for reporting the cluster decision to the FC?
The answers to these questions are given in the following section.
4 The proposed clusterbased selective CSS scheme
4.1 Selective CSS mechanism
It is obvious that the reliability of the sensing data will be higher if the absolute value of the normalized LLR is larger. We propose utilization of the absolute value of the normalized LLR $\left{Y}_{i,{c}_{j}}\right$ as the reliability coefficient for selecting the cluster header as well as the selective cluster data.
where ${Y}_{{c}_{j}}$ is equal to the normalized LLR with highest absolute value and ${\tau}_{{c}_{j}}$ is the cluster threshold. Next, we consider the problem of choosing the optimal cluster threshold.
4.2 Cluster threshold determination
In order to make a controllable cluster decision that follows a certain criterion such as the NeymanPearson criterion or minimum error probability criterion, one factor to consider is the probability density function of the cluster’s selective sensing data which is utilized to make the cluster decision. In this subsection, we will formulate this requirement.
where the conditional PDF’s of the LLR under H_{0} and H_{1} are determined in [12] as follows:
where a = [N^{2}/4 + N log(2γ + 1)/γ](2γ + 1) and b = 2N(2γ + 1)/γ.
Otherwise, ${f}_{\Lambda \left{H}_{0}\right.}\left(\Lambda \right)=0$, ${F}_{\Lambda \left{H}_{1}\right.}\left(\Lambda \right)=0$, ${f}_{\Lambda \left{H}_{0}\right.}\left(\Lambda \right)=0$, and ${f}_{\Lambda \left{H}_{1}\right.}\left(\Lambda \right)=0$.
The derivation of (22) can be found in the Appendix. Similarly, the conditional PDF of the n_{0}th absolute order sample under the H_{ j } hypothesis, j = 0,1, ${f}_{{Y}_{\left({n}_{0}\right),{c}_{j}}\left{H}_{j}\right.}\left(y\right)$ can be achieved.
4.3 Parallel report mechanism
In this method, the problems of strict synchronization and contention collision, which can occur with the previous method, are completely resolved. Indeed, with this parallel contention and reporting mechanism, the synchronization among CUs can be looser since there is only one contention time that is identical to the reporting time. No collision between two cluster reports will occur since these cluster decisions are transmitted at different frequencies. Even in the case that two CUs in a cluster have the same value of the most reliable sensing data, a collision still will not occur since the two nodes will transmit the same frequency, and at the receiver side, two transmitted frequencies can be considered as two versions of a multipath signal. The remainder problem with this parallel reporting method is that the FC needs to be equipped with parallel communication devices such as an FFT block, which is usually used in an OFDM receiver, or a filter bank block to detect multiple reporting frequencies. However, this requirement is not a big issue.
5 Simulation results
The simulation of the proposed clusterbased selective CSS scheme is conducted under the following assumptions:

The LU signal is a DTV signal as in [18].

The bandwidth of the PU signal is 6 MHz, and the AWGN channel is considered.

The local sensing time is 50 μ s.

The probability of the presence and absence of PU signal is 0.5 for both.

The network has n_{0} nodes and can be divided into n_{ c } clusters. Each cluster includes n_{0} nodes.
respectively. The energy consumption efficiency (EE) and the reporting timesaving efficiency (TE) of the conventional cluster and the proposed CSS schemes compared with the direct CSS scheme can be easily obtained by EE _{∗} = 1  E_{∗}/E_{DIR} and TE _{∗} = 1  T_{∗}/T_{DIR}, respectively, where the asterisk (*) can be replaced by CON or PROP.
6 Conclusions
In this paper, we have proposed a clusterbased CSS scheme which includes the selective method in the cluster and the optimal fusion rule in the FC. The proposed selective combination method can dramatically reduce the reporting time and energy consumption while achieving a certain high level of sensing performance especially when it is combined with the proposed frequency divisionbased parallel reporting mechanism.
Appendix
Derivation of Equation 22
By replacing Y with ${Y}^{{c}_{j}}$ and substituting k = n_{0} into (30), Equation 22 can be obtained.
Declarations
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (NRF2012R1A1A2038831 and NRF2013R1A2A2A05004535).
Authors’ Affiliations
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.