RIS-Enhanced Spectrum Sensing: How Many Reflecting Elements Are Required to Achieve a Detection Probability Close to 1?

TL;DR

This paper introduces a RIS-enhanced spectrum sensing system that uses large dimensional random matrix theory to analytically determine the number of reflecting elements needed for near-perfect detection probability, improving detection performance.

Contribution

It develops an analytical framework based on random matrix theory for RIS-assisted spectrum sensing and predicts the required number of reflecting elements for high detection probability.

Findings

The proposed method accurately predicts detection probability in asymptotic regimes.

RIS significantly enhances spectrum sensing performance.

Theoretical predictions match Monte Carlo simulation results.

Abstract

In this paper, we propose an reconfigurable intelligent surface (RIS) enhanced spectrum sensing system, in which the primary transmitter is equipped with single antenna, the secondary transmitter is equipped with multiple antennas, and the RIS is employed to improve the detection performance. Without loss of generality, we adopt the maximum eigenvalue detection approach, and propose a corresponding analytical framework based on large dimensional random matrix theory, to evaluate the detection probability in the asymptotic regime. Besides, the phase shift matrix of the RIS is designed with only the statistical channel state information (CSI), which is shown to be quite effective when the RIS-related channels are of Rician fading or line-of-sight (LoS). With the designed phase shift matrix, the asymptotic distributions of the equivalent channel gains are derived. Then, we provide the theoretical predictions about the number of reflecting elements (REs) required to achieve a detection probability close to 1. Finally, we present the Monte-Carlo simulation results to evaluate the accuracy of the proposed asymptotic analytical framework for the detection probability and the validity of the theoretical predictions about the number of REs required to achieve a detection probability close to 1. Moreover, the simulation results show that the proposed RIS-enhanced spectrum sensing system can substantially improve the detection performance.