Joint Block Support Recovery for Sub-Nyquist Sampling Cooperative Spectrum Sensing

被引：7

作者：

Ma, Hui ^{[1
]}

Yuan, Xiaobing ^{[2
]}

Zhou, Leilei ^{[2
]}

Li, Baoqing ^{[2
]}

Qin, Ronghua ^{[2
]}

机构：

[1] Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Key Lab Sci & Technol Microsyst, Shanghai 201800, Peoples R China

[2] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2023年 / 12卷 / 01期

关键词：

Sensors; Sparse matrices; Upper bound; Radio spectrum management; Micromechanical devices; Indexes; Coherence; Joint block sparsity; simultaneous orthogonal matching pursuit; exact recovery condition; centralized cooperative spectrum sensing;

D O I：

10.1109/LWC.2022.3217783

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This letter aims to improve the efficiency and reliability of joint block sparse support recovery by exploiting block features in the MMV problem. For the block MMV problem, we first propose a Block SOMP algorithm extended from SOMP, which can reduce the number of iterations of the original method. Then, we perform an in-depth theoretical analysis of the proposed algorithm using Exact Recovery Condition (ERC). Both theoretical analysis and simulation experiments confirm that the proposed algorithm can provide reliable recovery for higher sparsity. However, the premise is that the design of the cooperative compressed sensing matrix requires special attention.

引用

页码：85 / 88

页数：4

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