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http://dx.doi.org/10.3837/tiis.2018.07.007

Adaptive Algorithms for Bayesian Spectrum Sensing Based on Markov Model  

Peng, Shengliang (Xiamen Mobile and Multimedia Communications Key Laboratory, Huaqiao University)
Gao, Renyang (Xiamen Mobile and Multimedia Communications Key Laboratory, Huaqiao University)
Zheng, Weibin (Xiamen Mobile and Multimedia Communications Key Laboratory, Huaqiao University)
Lei, Kejun (College of Information Science and Engineering, Jishou University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.7, 2018 , pp. 3095-3111 More about this Journal
Abstract
Spectrum sensing (SS) is one of the fundamental tasks for cognitive radio. In SS, decisions can be made via comparing the test statistics with a threshold. Conventional adaptive algorithms for SS usually adjust their thresholds according to the radio environment. This paper concentrates on the issue of adaptive SS whose threshold is adjusted based on the Markovian behavior of primary user (PU). Moreover, Bayesian cost is adopted as the performance metric to achieve a trade-off between false alarm and missed detection probabilities. Two novel adaptive algorithms, including Markov Bayesian energy detection (MBED) algorithm and IMBED (improved MBED) algorithm, are proposed. Both algorithms model the behavior of PU as a two-state Markov process, with which their thresholds are adaptively adjusted according to the detection results at previous slots. Compared with the existing Bayesian energy detection (BED) algorithm, MBED algorithm can achieve lower Bayesian cost, especially in high signal-to-noise ratio (SNR) regime. Furthermore, it has the advantage of low computational complexity. IMBED algorithm is proposed to alleviate the side effects of detection errors at previous slots. It can reduce Bayesian cost more significantly and in a wider SNR region. Simulation results are provided to illustrate the effectiveness and efficiencies of both algorithms.
Keywords
Spectrum sensing; Markov model; Bayesian cost; Adaptive threshold;
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