稀疏度自适应的宽带压缩频谱感知方法

赵知劲; 胡俊伟

doi:10.3969/j.issn.1000-0801.2014.03.018

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稀疏度自适应的宽带压缩频谱感知方法

研究与开发 | 更新时间：2024-06-05

- 稀疏度自适应的宽带压缩频谱感知方法
- A Sparsity Adaptive Algorithm for Wideband Compressive Spectrum Sensing
- 电信科学 2014年30卷第3期页码：100-104
- 作者机构：
  
  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
- 作者简介：
  
  [ "赵知劲，女，博士，杭州电子科技大学教授、博士生导师，杭州电子科技大学通信工程学院党委书记，主要研究方向为认知无线电、通信信号处理、自适应信号处理等。" ]
  [ "胡俊伟，男，杭州电子科技大学硕士生，主要研究方向为压缩感知在通信系统中的应用。" ]
- 基金信息：
  
  电科院预研基金资助项目(41101040102)
- DOI：10.3969/j.issn.1000-0801.2014.03.018
  中图分类号：
- 网络出版日期：2014-03，
  
  纸质出版日期：2014-03-20
- 稿件说明：
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赵知劲, 胡俊伟. 稀疏度自适应的宽带压缩频谱感知方法[J]. 电信科学, 2014,30(3):100-104.

Zhijin Zhao, Junwei Hu. A Sparsity Adaptive Algorithm for Wideband Compressive Spectrum Sensing[J]. Telecommunications science, 2014, 30(3): 100-104.
赵知劲, 胡俊伟. 稀疏度自适应的宽带压缩频谱感知方法[J]. 电信科学, 2014,30(3):100-104. DOI： 10.3969/j.issn.1000-0801.2014.03.018.

Zhijin Zhao, Junwei Hu. A Sparsity Adaptive Algorithm for Wideband Compressive Spectrum Sensing[J]. Telecommunications science, 2014, 30(3): 100-104. DOI： 10.3969/j.issn.1000-0801.2014.03.018.

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。

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.

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references

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