Cognitive radio spectrum sensing approach based on multiple-model hypothesis testing

被引：0
|
作者：
Liu B. [1 ]
Deng J. [2 ]
Wang W.-F. [2 ]
Wang J.-T. [3 ]
Huang M.-T. [1 ]
机构：
[1] College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an
[2] School of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an
[3] Department of Engineering and Technology, Xi'an Fanyi University, Xi'an
来源：
Kongzhi yu Juece/Control and Decision | 2020年 / 35卷 / 08期
关键词：
Cognitive radio; Multiple-model hypothesis testing; Sequential probability ratio test; Spectrum sensing;
D O I：
10.13195/j.kzyjc.2018.1669
中图分类号：
学科分类号：
摘要：
This paper presents a multiple-model hypothesis testing approach based on sequential probability ratio test for cognitive radio spectrum sensing to detect unknown signal that may have multiple possible distributions with different structural or parametric uncertainties. The traditional cognitive radio spectrum sensing scheme (e.g., single model hypothesis testing based on sequential probability ratio test and M-ary hypothesis testing) may be not correct, because it only handles the totally known signal distribution case without considering the uncertainties of signals. The proposed multiple-model hypothesis testing scheme not only copes with the uncertainties of signals, but also has a setting that can provide efficient detection results. Performance of the proposed scheme is evaluated for spectrum sensing in an illustrative scenario. Simulation results demonstrate its detection efficiency compared with the traditional schemes. © 2020, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：1909 / 1915
页数：6
相关论文
共 26 条
  • [1] Mitola J, Maguire G, Cognitive radio: Making software radios more personal, IEEE Personal Communications, 6, 4, pp. 13-18, (1999)
  • [2] Haykin S, Cognitive radio: Brain-empowered wireless communications, IEEE Journal on Selected Areas in Communications, 23, 2, pp. 201-220, (2005)
  • [3] Huang Y, Zang C Z, Yu H B, Multipath error analysis of radio interferometric positioning system in wireless sensor networks, Control and Decision, 24, 2, pp. 231-235, (2009)
  • [4] Tevfik Y, Huseyin A, A survey of spectrum sensing algorithms for cognitive radio applications, IEEE Communications Surveys & Tutorials, 11, 1, pp. 116-130, (2009)
  • [5] Amir G, Elvino S, Spectrum sensing in cognitive radio networks: Requirements, challenges and design trade-offs, IEEE Communications Magazine, 46, 4, pp. 32-39, (2008)
  • [6] Zhi Q, Cui S, Ali H, Optimal linear cooperation for spectrum sensing in cognitive radio networks, IEEE Journal of Selected Topics in Signal Processing, 2, 1, pp. 28-40, (2008)
  • [7] Huang Y H, Chen X, Event-based optimization approach to opportunistic spectrum access problem, Control and Decision, 28, 11, pp. 1643-1649, (2013)
  • [8] Hu Z T, Zhang J, Guo Z, Unscented Kalman probability hypothesis density filter based on interacting multiple model, Control and Decision, 31, 12, pp. 2163-2169, (2016)
  • [9] Leang C, Johnson D, On the asymptotic of $M$-hypothesis Bayesian detection, IEEE Transactions on Information Theory, 43, 1, pp. 280-282, (1997)
  • [10] Nguyen V D, Shin O S, Cooperative prediction-and-sensing-based spectrum sharing in cognitive radio networks, IEEE Transactions on Cognitive Communications and Networking, 4, 1, pp. 108-120, (2018)
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