基于二项分布改进的宽带压缩频谱检测方案

doi:10.3969/j.issn.0372-2112.2020.02.003

电子学报 ›› 2020, Vol. 48 ›› Issue (2): 243-248.DOI: 10.3969/j.issn.0372-2112.2020.02.003

基于二项分布改进的宽带压缩频谱检测方案

马彬¹, 王宏明^1,2, 谢显中¹

1. 重庆邮电大学移动通信技术重庆市重点实验室, 重庆 400065;
2. 重庆邮电大学通信与信息工程学院, 重庆 400065

收稿日期:2019-01-28 修回日期:2019-05-15 出版日期:2020-02-25
- 通讯作者:
- 王宏明
- 作者简介:
- 马彬男,1978年生于四川宜宾,现为重庆邮电大学教授,主要研究方向为异构无线网络、认知无线电网络等.E-mail:mabin@cqupt.edu.cn;谢显中 男,1966年生于四川通江,现为重庆邮电大学教授,主要研究方向为干扰对齐与MIMO技术.E-mail:xiexzh@cqupt.edu.cn
- 基金资助:
- 重庆市教委科学技术研究重点项目 (No.KJZD-K201800603）; 重庆市自然科学基金 (No.CSTC2018jcyjAX0432）; 重庆市研究生科研创新项目 (No.CYS19252）

An Improved Wideband Compressed Spectrum Sensing Scheme Based on Binomial Distribution

MA Bin¹, WANG Hong-ming^1,2, XIE Xian-zhong¹

1. Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2019-01-28 Revised:2019-05-15 Online:2020-02-25 Published:2020-02-25
- Corresponding author:
- WANG Hong-ming
- Supported by:
- Science and Technology Research Major Program of Chongqing Municipal Education Commission (No.KJZD-K201800603); Natural Science Foundation of Chongqing Municipality, China (No.CSTC2018jcyjAX0432); Chongqing Postgraduate Research and Innovation Project (No.CYS19252)

摘要/Abstract

摘要： 宽带压缩频谱检测存在依赖稀疏度先验信息和信号重构时延较高的问题.因此，本文提出了一种高效可靠的宽带压缩频谱检测方案.首先，推导出了基于二项分布精确置信区间改进的稀疏度估计模型.其次，利用稀疏度估计上下界改进了稀疏度自适应匹配追踪算法.最后，提出了一种宽带压缩频谱检测方案.仿真结果表明，本文所提出方法可以同时精确的估计信号稀疏度的上下界，提高了频谱检测的效率和可靠性，加快了算法的收敛速度.

关键词: 宽带频谱检测, 压缩感知, 稀疏度估计, 置信区间, 信号重构

Abstract: Wideband compressed spectrum sensing has the problem of relying on sparsity prior information and high signal reconstruction delay. Therefore, this paper proposes an efficient and reliable wideband compressed spectrum sensing scheme. Firstly, the sparsity estimation model based on the improved confidence interval of binomial distribution is derived. Secondly, using the sparsity estimation upper and lower bounds improves the sparsity adaptive matching pursuit algorithm. Finally, a wideband compressed spectrum sensing scheme is proposed. The simulation results show that the proposed method can accurately estimate the upper and lower bounds of signal sparsity at the same time, improve the efficiency and reliability of spectrum sensing, and accelerate the convergence speed of the algorithm.

Key words: wideband spectrum sensing, compressed sensing, sparsity estimation, confidence interval, signal reconstruction

中图分类号:

TN929.5

马彬, 王宏明, 谢显中. 基于二项分布改进的宽带压缩频谱检测方案[J]. 电子学报, 2020, 48(2): 243-248.

MA Bin, WANG Hong-ming, XIE Xian-zhong . An Improved Wideband Compressed Spectrum Sensing Scheme Based on Binomial Distribution[J]. Acta Electronica Sinica, 2020, 48(2): 243-248.

参考文献

[1] Khalfi B,Zaid A,Hamdaoui B.When machine learning meets compressive sampling for wideband spectrum sensing[A].Proceedings of the 13th International Wireless Communications and Mobile Computing Conference [C]. Valencia,Spain:IEEE,2017.1120-1125.
[2] Ali A,Hamouda W.Advances on spectrum sensing for cognitive radio networks:theory and applications[J].IEEE Communications Surveys & Tutorials,2017,19(2): 1277-1304.
[3] Sun H,Nallanathan A,Wang C,et al.Wideband spectrum sensing for cognitive radio networks:a survey[J].IEEE Wireless Communications,2013,20(2):74-81.
[4] Quan Z,Cui S,Sayed A H,et al.Optimal multiband joint detection for spectrum sensing in cognitive radio networks[J].IEEE Transactions on Signal Processing,2009, 57(3):1128-1140.
[5] Tian Z,Giannakis G B.Compressed sensing for wideband cognitive radios[A].Proceedings of the 2007 IEEE International Conference on Acoustics,Speech and Signal Processing[C].Honolulu,HI,USA:IEEE,2007.1357-1360.
[6] Lopes M E.Unknown sparsity in compressed sensing:denoising and inference[J].IEEE Transactions on Information Theory,2016,62(9):5145-5166.
[7] Weiqiang H,Jianlin Z,Zhiqiang L,et al.Sparsity and step-size adaptive regularized matching pursuit algorithm for compressed sensing[A].Proceedings of the IEEE 7th Joint International Information Technology and Artificial Intelligence Conference[C].Chongqing,China:IEEE,2014.536-540.
[8] Sharma S K,Chatzinotas S,Ottersten B.Compressive sparsity order estimation for wideband cognitive radio receiver[J].IEEE Transactions on Signal Processing,2014,62(19):4984-4996.
[9] Gao Y,Si Y,Zhu B,et al.Sparsity order estimation algorithm in compressed sensing by exploiting slope analysis[A].Proceedings of the 14th International Wireless Communications & Mobile Computing Conference[C].Limassol,Cyprus:IEEE,2018.753-756.
[10] Bioglio V,Bianchi T,Magli E.On the fly estimation of the sparsity degree in compressed sensing using sparse sensing matrices[A].Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing[C].Brisbane,QLD,Australia:IEEE,2015.3801-3805.
[11] Wang Y,Tian Z,Feng C.Sparsity order estimation and its application in compressive spectrum sensing for cognitive radios[J].IEEE Transactions on Wireless Communications,2012,11(6):2116-2125.
[12] Malioutov D M,Sanghavi S,Willsky A S.Compressed sensing with sequential observations[A].Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing[C].Las Vegas,NV,USA:IEEE,2008.3357-3360.
[13] Arias-Castro E,Candes E J,Davenport M A.On the fundamental limits of adaptive sensing[J].IEEE Transactions on Information Theory,2013,59(1):472-481.
[14] Candes E J,Tao T.Decoding by linear programming[J].IEEE Transactions on Information Theory,2005,51(12):4203-4215.
[15] Lorenzi L,Mercier G,Melgani F.Adaptive basis pursuit compressive sensing reconstruction with histogram matching[A].Proceedings of the IEEE International Geoscience and Remote Sensing Symposium[C].Melbourne,VIC,Australia:IEEE,2013.872-875.
[16] Mallat S,Zhang Z.Adaptive time-frequency decomposition with matching pursuits[A].Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis.Victoria,BC,Canada:IEEE,1992.7-10.
[17] Sahoo S K,Makur A.Signal recovery from random measurements via extended orthogonal matching pursuit[J].IEEE Transactions on Signal Processing,2015,63(10):2572-2581.
[18] Do T T,Gan L,Nguyen N,et al.Sparsity adaptive matching pursuit algorithm for practical compressed sensing[A].Proceedings of the 42nd Asilomar Conference on Signals,Systems and Computers[C].Pacific Grove,CA,USA:IEEE,2008.581-587.
[19] 张波,刘郁林,王开.稀疏随机矩阵有限等距性质分析[J].电子与信息学报,2014,36(1):169-174. Zhang Bo,Liu Yu-lin,Wang Kai.Restricted isometry property analysis for sparse random matrices[J].Journal of Electronics and Information Technology,2014,36(1):169-174.(in Chinese)

基于二项分布改进的宽带压缩频谱检测方案

An Improved Wideband Compressed Spectrum Sensing Scheme Based on Binomial Distribution

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