Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design

TLDR

Intelligent reflecting surfaces (IRS) are envisioned as a key technology for future wireless networks, using large arrays of low‑cost passive elements to reconfigure signal propagation for beamforming and interference suppression. This study investigates a point‑to‑point MISO system where an IRS assists a multi‑antenna access point in communicating with a single‑antenna user, aiming to maximize the user’s received signal power through joint optimization of active transmit beamforming and passive IRS phase shifts. The authors first propose a centralized semidefinite‑relaxation algorithm assuming global CSI, then a low‑complexity distributed scheme where the access point and IRS iteratively adjust their beamformers until convergence. Simulations demonstrate that both algorithms yield significant performance gains over benchmark schemes, with the IRS markedly improving link quality and coverage compared to conventional setups without IRS.

Abstract

Intelligent reflecting surface (IRS) is envisioned to have abundant applications in future wireless networks by smartly reconfiguring the signal propagation for performance enhancement. Specifically, an IRS consists of a large number of low-cost passive elements each reflecting the incident signal with a certain phase shift to collaboratively achieve beamforming and suppress interference at one or more designated receivers. In this paper, we study an IRS-enhanced point-to-point multiple-input single-output (MISO) wireless system where one IRS is deployed to assist in the communication from a multi-antenna access point (AP) to a single-antenna user. As a result, the user simultaneously receives the signal sent directly from the AP as well as that reflected by the IRS. We aim to maximize the total received signal power at the user by jointly optimizing the (active) transmit beamforming at the AP and (passive) reflect beamforming by the phase shifters at the IRS. We first propose a centralized algorithm based on the technique of semidefinite relaxation (SDR) by assuming the global channel state information (CSI) available at the IRS. Since the centralized implementation requires excessive channel estimation and signal exchange overheads, we further propose a low-complexity distributed algorithm where the AP and IRS independently adjust the transmit beamforming and the phase shifts in an alternating manner until the convergence is reached. Simulation results show that significant performance gains can be achieved by the proposed algorithms as compared to benchmark schemes. Moreover, it is verified that the IRS is able to drastically enhance the link quality and/or coverage over the conventional setup without the IRS.