Multi-beam Beamforming in RIS-aided MIMO Subject to Reradiation Mask Constraints -- Optimization and Machine Learning Design

TL;DR

This paper presents a joint optimization framework for multi-beam beamforming in RIS-aided MIMO systems, integrating machine learning and discrete phase shift schemes to enhance efficiency and practical applicability.

Contribution

It introduces a novel combined optimization and neural network approach for multi-beam RIS beamforming under reradiation constraints, including discrete phase shift handling.

Findings

Neural network optimization reduces computation time.

Discrete phase shifts with four levels maintain near-optimal beamforming.

Proposed methods effectively shape radiation patterns within constraints.

Abstract

Reconfigurable intelligent surfaces (RISs) are an emerging technology for improving spectral efficiency and reducing power consumption in future wireless systems. This paper investigates the joint design of the transmit precoding matrices and the RIS phase shift vector in a multi-user RIS-aided multiple-input multiple-output (MIMO) communication system. We formulate a max-min optimization problem to maximize the minimum achievable rate while considering transmit power and reradiation mask constraints. The achievable rate is simplified using the Arimoto-Blahut algorithm, and the problem is broken into quadratic programs with quadratic constraints (QPQC) sub-problems using an alternating optimization approach. To improve efficiency, we develop a model-based neural network optimization that utilizes the one-hot encoding for the angles of incidence and reflection. We address practical RIS limitations by using a greedy search algorithm to solve the optimization problem for discrete phase shifts. Simulation results demonstrate that the proposed methods effectively shape the multi-beam radiation pattern towards desired directions while satisfying reradiation mask constraints. The neural network design reduces the execution time, and the discrete phase shift scheme performs well with a small reduction of the beamforming gain by using only four phase shift levels.