Elsevier

Physical Communication

Volume 25, Part 2, December 2017, Pages 445-453
Physical Communication

Full length article
Joint optimal fair cooperative spectrum sensing and transmission in cognitive radio

https://doi.org/10.1016/j.phycom.2017.08.015Get rights and content

Abstract

Since the sensing power consumption of cooperative spectrum sensing (CSS) will decrease the throughput of secondary users (SU) in cognitive radio (CR), a joint optimal model of fair CSS and transmission is proposed in this paper, which can compensate the sensing overhead of cooperative SUs. The model uses the periodic listen-before-transmission method, where each SU is assigned a portion of channel bandwidth, when the primary user (PU) is estimated to be free by the coordinator. Then, a joint optimization problem of local sensing time, number of cooperative SUs, transmission bandwidth and power is formulated, which can compensate the sensing overhead of cooperative SUs appropriately through choosing suitable compensating parameter. The proposed optimization problem can be solved by the Polyblock algorithm. Simulation results show that compared with the traditional model, the total system throughput of the fairness cooperation model decreases slightly, but the total throughput of the cooperative SUs improves obviously.

Introduction

Recently, cognitive Radio (CR) is proposed to alleviate spectrum resource shortage. CR adopts the opportunistic spectrum access by admitting secondary user (SU) to utilize the unlicensed spectrum or the licensed spectrum without primary user (PU), in order to improve the spectrum efficiency [1], [2]. Therefore, SU has to sense the status of PU continually to avoid interfering PU. Energy detection has been widely used as an effective spectrum sensing method in CR network, because it does not need any prior information of the PU and also owns high reliability [3]. However, the “hidden terminal problem” caused by multipath fading and shadow effect may decrease the sensing performance greatly. Cooperative spectrum sensing (CSS) is proposed to overcome this problem by combining the independent sensing information of different SUs locating in different sensing areas. The cooperative diversity gain can be achieved through making a final decision in a fusion center [4].

The sensing parameters (e.g., sensing duration and the number of cooperative users, etc.) and the transmission parameters (e.g., transmission bandwidth and power, etc.) will affect the transmission performance of CR system [5], [6]. The system performance can be improved obviously by jointly optimizing these system parameters. Liang proposed an optimization problem that seeks a tradeoff between the sensing duration and the SU throughput in the listening-before-transmission model. The optimization problem maximizes the SU throughput by optimizing the sensing duration, but the spectrum allocation was lack of consideration, which can improve the throughput effectively [7]. Liu studied the joint optimization algorithm of sensing parameters and transmission power under the single-user condition by using alternating directing optimization (ADO) [8], but this algorithm had a low computational efficiency and failed to consider the multiuser condition. Fan gave the joint optimization of CSS duration, transmission bandwidth and power [9], however, this optimization ignored the affection of the user overhead and power overhead on the CR system.

This paper proposes a joint optimization of multiuser fairness and transmission to improve sensing and transmission efficiency, while compensating the transmission loss caused by CSS. Polyblock algorithm is used to achieve the joint optimal allocation of sensing duration, the number of cooperative users, transmission bandwidth and transmission power. Though the total system throughput of the proposed fairness model decreases slightly, the total throughput of all cooperative SUs improves and the sensing overhead is also compensated properly.

Section snippets

Energy detection

The energy detection is widely used in CR because SU cannot obtain any prior information of PU signal. As shown in Fig. 1, in energy detection, the received signal will first be processed by a band-pass filter to obtain the pure PU signal, the energy statistic is then computed by squaring the amplitude of the PU signal samples, and the achieved energy statistic is finally compared with a preset threshold to make a decision on the presence of PU. SUs will only access the spectrum if the energy

Model optimization

The constraints of optimization problem (19) are firstly simplified. According to (10), false alarm probability Qfj can be formulated by detection probability Qdj as Qfj=QQ1Qdj2γ¯j+1+γ¯jkτfs.

Since Q is a monotonously decreasing function, Qfj and Qdj have the same gradient. As Qfj increases, Qdj also increases. From (16), the greater Qfj and Qdj, the smaller the objective function value. Hence, the optimal value of (19) should be achieved only when Qfj and Qdj reach their lower

Simulation analysis

Simulation parameters are set as follows: the total number of SUs N=6, the total number of licensed channels L=16, the bandwidth of each subchannel W=1kHz, the sampling frequency fs=10kHz, the sensing period T=20ms, the cooperative timeslot length ξ=0.5ms, the transmission power of PU signal ps=10mW, the CSS loss power Pv=2mW, the maximum transmission power of SU Pmax=10mW, the probabilities of H0 and H1 PH0=PH1=0.5, the upper limit of false alarm probability α=0.5, the lower

Conclusion

In this paper, a fairness CSS and transmission model based on listen-before-transmission are proposed, which seeks to maximize the CR throughput by jointly optimizing local sensing time, cooperative user number, transmission bandwidth and power, while compensating the losses caused by CSS through allocating certain compensation weights to the cooperative users. Simulation results have shown that though the CR throughput of the fairness cooperation model slightly decreases compared with the

Acknowledgments

This work was supported by the National Natural Science Foundations of China under Grant Nos. 61601221 and 61671183,the Natural Science Foundation of Jiangsu Province under Grant No. BK20140828, the Fundamental Research Funds for the Central Universities under Grant No. DUT16RC(3)045, and the Chinese Postdoctoral Science Foundation under Grant No. 2015M580425.

Xin Liu, received his M.Sc. degree and Ph.D. degree in communication engineering from Harbin Institute of Technology in 2008 and 2012 respectively. He is currently an associate professor in School of Information and Communication Engineering, Dalian University of Technology, China. His research interests focus on communication signal processing, cognitive radio, spectrum resource allocation and broadband satellite communication.

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    Xin Liu, received his M.Sc. degree and Ph.D. degree in communication engineering from Harbin Institute of Technology in 2008 and 2012 respectively. He is currently an associate professor in School of Information and Communication Engineering, Dalian University of Technology, China. His research interests focus on communication signal processing, cognitive radio, spectrum resource allocation and broadband satellite communication.

    Min Jia, received her M.Sc. degree and Ph.D. degree in information and communication engineering from Harbin Institute of Technology in 2006 and 2010 respectively. She is currently an associate professor in School of Electronic and Information Engineering, Harbin Institute of Technology, China. Her research interests focus on advanced mobile communication technology for 3G and LTE, cognitive radio, digital signal processing and advanced broadband satellite communication system.

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