电信科学 ›› 2014, Vol. 30 ›› Issue (3): 100-104.doi: 10.3969/j.issn.1000-0801.2014.03.018

• 研究与开发 • 上一篇    下一篇

稀疏度自适应的宽带压缩频谱感知方法

赵知劲1,2,胡俊伟1   

  1. 1 杭州电子科技大学通信工程学院 杭州 310018
    2 中国电子科技集团第36研究所通信系统信息控制技术国家级重点实验室 嘉兴 314001
  • 出版日期:2014-03-20 发布日期:2017-06-16
  • 基金资助:
    电科院预研基金资助项目

A Sparsity Adaptive Algorithm for Wideband Compressive Spectrum Sensing

Zhijin Zhao1,2,Junwei Hu1   

  1. 1 School of Telecommunication Engineering of Hangzhou Dianzi University, Hangzhou 310018, China
    2 State Key Lab of Information Control Technology in Communication System of No.36 Research Institute, China Electronic Technology Corporation, Jiaxing 314001, China
  • Online:2014-03-20 Published:2017-06-16

摘要:

针对基于压缩感知的传统频谱感知方法通常假设稀疏度已知,而实际频谱感知中信道稀疏度是未知且时变的这一问题,提出一种稀疏度自适应的宽带频谱感知算法。首先采用分布式压缩感知和RIP 性质预估计稀疏度,然后通过置信系数更新估计得到频谱支撑集,即主用户正在使用的频谱。仿真结果表明,在低信噪比条件下,本方法的检测概率高于稀疏度已知的频谱感知方法,而仅损失极少的频谱利用率,且计算复杂度低。

关键词: 压缩感知, 频谱感知, 稀疏度估计, 约束等距性, 置信系数

Abstract:

Traditional spectrum sensing based on compressed sensing assumes that the sparsity is known, in fact,it is unknown and time-varying. To solve the problem, a sparsity adaptive algorithm for wideband spectrum sensing was proposed. First, the distributed compressed sensing and restricted isometry property principle were adopted to estimate an initial sparsity value. Then the confidence coefficient was used to update the sparsity and the spectrum support set was obtained, which was occupied by a primary user. Simulation results show that the proposed method has better spectrum detection performance than the spectrum sensing method with a known sparsity, and losses spectrum availability a little in low SNR, and its complexity is small.

Key words: compressed sensing, spectrum sensing, sparsity estimation, restricted isometry property, confidence coefficient

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