Reconfigurable Intelligent Surface-Aided Spectrum Sharing Coexisting with Multiple Primary Networks

TL;DR

This paper proposes a reconfigurable intelligent surface (RIS) aided spectrum sharing system with multiple primary networks, optimizing beamforming and RIS coefficients to enhance secondary user spectral efficiency through convex optimization and low-complexity algorithms.

Contribution

It introduces a novel RIS-assisted spectrum sharing framework with joint optimization of beamforming and RIS phase shifts, including practical discrete phase control methods.

Findings

Both GLD and NPSP algorithms achieve high performance.

RIS significantly improves spectrum efficiency in multi-primary network scenarios.

The proposed methods are suitable for fast deployment in real systems.

Abstract

Considering the spectrum sharing system (SSS) coexisting with multiple primary networks, we have employed a well-designed reconfigurable intelligent surface (RIS) to control the radio environments of wireless channels and relieve the scarcity of the spectrum resource in this work. Specifically, the enhancement of the spectral efficiency of the secondary user in the considered SSS is decomposed into two subproblems which are a second-order cone programming (SOCP) and a fractional programming of the convex quadratic form (CQFP), respectively, to optimize alternatively the beamforming vector at the secondary access point (S-AP) and the reflecting coefficients at the RIS. The SOCP subproblem is shown as a concave problem, which can be solved optimally using standard convex optimization tools. The CQFP subproblem can be solved by a low-complexity method of gradient-based linearization with domain (GLD), providing a sub-optimal solution for fast deployment. Taking the discrete phase control at the RIS into account, a nearest point searching with penalty (NPSP) method is also developed, realizing the discretization of the phase shifts of the RIS in practice. The simulation results indicate that both GLD and NPSP can achieve an excellent performance.