Fusion Schemes Based on IRS-Enhanced Cooperative Spectrum Sensing for Cognitive Radio Networks

Abstract

The detection performance of cooperative spectrum sensing (CSS) is poor when there are obstacles blocking. Therefore, fusion schemes based on intelligent reflecting surface (IRS)-enhanced CSS are investigated. Existing fusion schemes can be divided into the soft combination and the hard combination. In the soft combination, each secondary user (SU) uploads the decision statistic to a fusion center (FC). Comparatively, during the hard combination, each SU reports the local 0/1 decision result to FC instead of decision statistic. In this paper, the equal gain combination (EGC) and the selection combination (SC) schemes are studied for the soft combination. The weighted hard combination (WHC) scheme is studied for the hard combination, and an optimal set combination (OSC) scheme is proposed based on signal-to-noise ratio (SNR). By using the definition of the non-centrality chi-square distribution and the moment-matching method, the closed-form expressions for the average probability of detection of all the schemes are derived. Simulation results reveal that the detection performance of IRS-enhanced CSS obviously outperforms the decode and forward (DF) relay case and the no IRS case. In addition, our proposed OSC are superior to the K-rank in terms of the detection performance.