A Cooperative Spectrum Sensing Scheme Based on Compressive Sensing for Cognitive Radio Networks

TL;DR

This paper introduces a cooperative spectrum sensing scheme using compressive sensing in cognitive radio networks, achieving high detection probability and low processing time compared to traditional methods.

Contribution

It proposes a novel clustering-based cooperative sensing scheme utilizing compressive sensing and evaluates its performance against existing methods.

Findings

Achieves 97% detection probability

Processing time of 2ms

Low compression ratio of 10%

Abstract

In this paper, a cooperative spectrum sensing scheme based on compressive sensing is proposed. In this scheme, secondary users (SUs) are organized in clusters. In each cluster, SUs forward their compressed signals to the cluster head. Then, the cluster heads report the local sensing data to the fusion center to make the final decision. Analog to information converter is adopted to acquire the received SUs signals. The proposed scheme is evaluated using several metrics, including probability of detection, compression ratio, probability of error, and processing time. The simulation results are compared with those of other cooperative sensing schemes. With our proposed scheme, cooperative spectrum sensing achieves a high probability of detection of 97%, a processing time of 2ms, a low compression ratio of 10%, and low error rate of 35 %. These results show that our proposed method outperforms the spectrum sensing without compressive sensing.