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

2025-07-21

AI Summary

This paper addresses the problem of maximizing the minimum user rate in RIS-aided multi-user MIMO systems under both transmit power and re-radiation masking (beam mask) constraints, via joint optimization of the base station precoding matrix and the RIS phase-shift vector. To solve this non-convex problem, we propose an alternating optimization framework that decomposes it into quadratically constrained quadratic programming (QCQP) subproblems. For efficient phase optimization, we introduce a model-based neural network whose input is a one-hot encoding of incident and reflected angles. Additionally, we design a greedy search algorithm tailored to discrete-phase RIS configurations. The Arimoto–Blahut algorithm is employed for accurate and efficient rate computation. Simulation results demonstrate that the proposed scheme achieves precise multi-beam steering while strictly satisfying the beam mask constraints; the neural network reduces runtime significantly; and 4-bit discrete phase resolution attains performance close to that of continuous phase shifting—achieving an excellent trade-off between practicality and efficiency.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Optimize RIS phase shifts and MIMO precoding under power constraints
• Maximize minimum user rate with reradiation mask limitations
• Develop efficient neural network for multi-beam pattern shaping

Innovation

• Alternating optimization for QPQC sub-problems
• Model-based neural network with one-hot encoding
• Greedy search for discrete phase shifts