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

• 学术论文 • 上一篇    下一篇

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. 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-ming1,2, XIE Xian-zhong1   

  1. 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: 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

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