Joint Optimization for Multi-User Transmissive RIS-MIMO Systems

TL;DR

This paper introduces a joint optimization framework for multi-user transmissive RIS-MIMO systems, enhancing sum-rate performance through an efficient AO algorithm that handles complex non-convex constraints.

Contribution

It proposes a novel optimization method for joint RIS coefficient, power, and beamforming design in transmissive RIS-MIMO systems, addressing non-convex challenges.

Findings

The proposed algorithm converges rapidly.

Achieves significant sum-rate improvements.

Validates transmissive RIS as a promising technology.

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.