Vertical Beamforming in Reconfigurable Intelligent Surface-aided Cognitive Radio Networks

TL;DR

This paper explores the combined use of reconfigurable intelligent surfaces and vertical beamforming to enhance spectral efficiency in cognitive radio networks, proposing an optimization approach for joint design.

Contribution

It introduces a novel joint optimization framework for RIS phase shifts and beamforming parameters in CRNs, improving network performance.

Findings

RIS and orientation optimization significantly boost spectral efficiency

Proposed solution effectively handles non-convex optimization problem

Numerical results demonstrate substantial performance gains

Abstract

In this letter, we investigate joint application of reconfigurable intelligent surface (RIS) and vertical beamforming in cognitive radio networks (CRN). After properly modeling the network, an optimization problem is formed to jointly design the beamforming vector and tilt angle at the secondary base station (BS) as well as the phase shifts at the RIS with the objective of maximizing spectral efficiency of the secondary network. The optimization problem is non-convex; thus, we propose an efficient solution method for it. Numerical results show that adding a RIS and optimizing the radiation orientation, can significantly improve performance of the CRNs.