CS-Feature detection spectrum sensing algorithm for cognitive radio

被引：0

作者：

Xuan, Sun ^{[1
]}

Zhou, Zheng ^{[1
]}

Li, Bin ^{[1
]}

机构：

[1] Key Lab of Universal Wireless Communications, MOE Wireless Network Lab, Beijing University of Posts and Telecommunications

来源：

Advances in Information Sciences and Service Sciences | 2012年 / 4卷 / 01期

关键词：

Cognitive radio; Compressed sensing; CS-Feature detection; Orthogonal matching pursuit;

D O I：

10.4156/aiss.vol4.issue1.5

中图分类号：

学科分类号：

摘要：

A CS-Feature detection spectrum sensing algorithms for cognitive radio is proposed in this paper. The traditional feature detection algorithm based on cyclic spectrum density has a very high accuracy, but it can't be widely used because of the high complexity and very long detection period. The CS-Feature detection in this paper is designed based on the sparsity of the cyclic autocorrelation. Because most of the man-made signals are cyclostationary, so the values in cyclic autocorrelation domain is sparse. From the measurements based on the compressed sensing of the cyclic autocorrelation, the actual cyclic autocorrelation of the signal can be recovered. From the simulation, the dissertation can be made that a very simple OMP algorithm can get a high probability of detection.

引用

页码：37 / 45

页数：8

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