Joint Optimization for Multi-User Transmissive RIS-MIMO Systems

2025-11-14

AI Summary

This work investigates a transmissive reconfigurable intelligent surface (RIS)-aided downlink multi-user MIMO system, aiming to jointly optimize RIS transmissive coefficients, transmit power allocation, and user-side receive beamforming to maximize the weighted sum rate. The problem is highly challenging due to its non-convex objective, strong coupling among variables, and the constant-modulus constraint on RIS coefficients. To address this, we propose a novel alternating optimization framework that decomposes the original problem into three tractable subproblems, solved efficiently via convex approximation, difference-of-convex programming (DCP), and closed-form solutions, respectively. Convergence is theoretically guaranteed. Simulation results demonstrate rapid convergence, substantial improvements in spectral and energy efficiency—particularly under low-power constraints—and significant weighted sum-rate gains over benchmark schemes. These findings validate the practical potential of transmissive RISs for 6G large-scale MIMO systems.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Non-convex Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Transmissive reconfigurable intelligent surfaces (RIS) represent a transformative architecture for future wireless networks, enabling a paradigm shift from traditional costly base stations to low-cost, energy-efficient transmitters. This paper explores a downlink multi-user MIMO system where a transmissive RIS, illuminated by a single feed antenna, forms the core of the transmitter. The joint optimization of the RIS coefficient vector, power allocation, and receive beamforming in such a system is critical for performance but poses significant challenges due to the non-convex objective, coupled variables, and constant modulus constraints. To address these challenges, we propose a novel optimization framework. Our approach involves reformulating the sum-rate maximization problem into a tractable equivalent form and developing an efficient alternating optimization (AO) algorithm. This algorithm decomposes the problem into subproblems for the RIS coefficients, receive beamformers, and power allocation, each solved using advanced techniques including convex approximation and difference-of-convex programming. Simulation results demonstrate that our proposed method converges rapidly and achieves substantial sum-rate gains over conventional schemes, validating the effectiveness of our approach and highlighting the potential of transmissive RIS as a key technology for next-generation wireless systems.

Problem

• Joint optimization of RIS coefficients, power allocation, and beamforming
• Addressing non-convex objective with coupled variables and constraints
• Maximizing sum-rate in multi-user transmissive RIS-MIMO systems

Innovation

• Alternating optimization algorithm for joint variables
• Convex approximation handles non-convex constraints
• Difference-of-convex programming solves RIS coefficients