An Efficient Convex-Hull Relaxation Based Algorithm for Multi-User Discrete Passive Beamforming

TL;DR

This paper introduces a convex-hull relaxation approach and an efficient algorithm for discrete passive beamforming in IRS-assisted wireless networks, significantly improving performance in maximizing minimum SINR among users.

Contribution

It proposes a novel convex-hull relaxation method for discrete phase-shift constraints and an efficient alternating projection algorithm, advancing IRS passive beamforming optimization.

Findings

The proposed algorithm outperforms existing methods in simulations.

Convex-hull relaxation effectively transforms the discrete problem into a continuous one.

Significant improvement in minimum SINR among users achieved.

Abstract

Intelligent reflecting surface (IRS) is an emerging technology to enhance spatial multiplexing in wireless networks. This letter considers the discrete passive beamforming design for IRS in order to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among multiple users in an IRS-assisted downlink network. The main design difficulty lies in the discrete phase-shift constraint. Differing from most existing works, this letter advocates a convex-hull relaxation of the discrete constraints which leads to a continuous reformulated problem equivalent to the original discrete problem. This letter further proposes an efficient alternating projection/proximal gradient descent and ascent algorithm for solving the reformulated problem. Simulation results show that the proposed algorithm outperforms the state-of-the-art methods significantly.