Bayesian Compressive Sensing with Circulant Matrix for Spectrum Sensing in Cognitive Radio Networks

TL;DR

This paper introduces a Bayesian compressive sensing method using circulant matrices for spectrum sensing in cognitive radio networks, demonstrating improved speed and efficiency over traditional methods.

Contribution

It combines circulant matrices with Bayesian models for robust, efficient spectrum sensing, outperforming l1 norm minimization in various metrics.

Findings

Faster reconstruction and sampling times.

Lower mean square and reconstruction errors.

Higher correlation and efficiency.

Abstract

For wideband spectrum sensing, compressive sensing has been proposed as a solution to speed up the high dimensional signals sensing and reduce the computational complexity. Compressive sensing consists of acquiring the essential information from a sparse signal and recovering it at the receiver based on an efficient sampling matrix and a reconstruction technique. In order to deal with the uncertainty, improve the signal acquisition performance, and reduce the randomness during the sensing and reconstruction processes, compressive sensing requires a robust sampling matrix and an efficient reconstruction technique. In this paper, we propose an approach that combines the advantages of a Circulant matrix with Bayesian models. This approach is implemented, extensively tested, and its results have been compared to those of l1 norm minimization with a Circulant or random matrix based on several metrics. These metrics are Mean Square Error, reconstruction error, correlation, recovery time, sampling time, and processing time. The results show that our technique is faster and more efficient.