Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks

TL;DR

This paper develops algorithms to optimize both active and passive beamforming in IRS-assisted wireless networks, maximizing weighted sum-rate under practical discrete phase shift constraints, with verified effectiveness through simulations.

Contribution

It introduces a joint optimization framework for active and passive beamforming in IRS-aided systems considering discrete phase shifts, using novel algorithms with closed-form solutions.

Findings

Proposed algorithms outperform benchmarks in sum-rate maximization.

Discrete phase shift constraints are effectively handled.

Simulation results confirm the efficiency of the algorithms.

Abstract

Intelligent reflecting surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems. In practice, an IRS consists of massive low-cost elements, which can steer the incident signal in fully customizable ways by passive beamforming. In this paper, we consider an IRS-aided multiuser multiple-input single-output (MISO) downlink communication system. In particular, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. In addition, we consider a practical IRS assumption, in which the passive elements can only shift the incident signal to discrete phase levels. This non-convex problem is firstly decoupled via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. The active beamforming at BS is optimized based on the fractional programming method. Then, three efficient algorithms with closed-form expressions are proposed for the passive beamforming at IRS. Simulation results have verified the effectiveness of the proposed algorithms as compared to different benchmark schemes.