Joint Optimization of Active and Passive Beamforming in Multi-IRS Aided mmWave Communications

TL;DR

This paper proposes a joint optimization framework for active and passive beamforming in multi-IRS mmWave communication networks, significantly improving system performance through an iterative solution approach.

Contribution

It introduces a novel joint optimization method for active/passive beamforming and IRS selection in multi-IRS mmWave systems, addressing non-convex challenges.

Findings

Performance gains demonstrated through simulations

Effective joint optimization of beamforming and IRS selection

Improved sum rate in multi-IRS mmWave networks

Abstract

Intelligent reflecting surface (IRS) has been considered as a promising technology to alleviate the blockage effect and enhance coverage in millimeter wave (mmWave) communication. To explore the impact of IRS on the performance of mmWave communication, we investigate a multi-IRS assisted mmWave communication network and formulate a sum rate maximization problem by jointly optimizing the active and passive beamforming and the set of IRSs for assistance. The optimization problem is intractable due to the lack of convexity of the objective function and the binary nature of the IRS selection variables. To tackle the complex non-convex problem, an alternating iterative approach is proposed. In particular, utilizing the fractional programming method to optimize the active and passive beamforming and the optimization of IRS selection is solved by enumerating. Simulation results demonstrate the performance gain of our proposed approach.