Joint Optimization for Multi-User Transmissive RIS-MIMO Systems

TL;DR

This work addresses the challenge of jointly designing a transmissive RIS-based downlink MIMO transmitter by formulating a non-convex sum-rate maximization problem over the RIS coefficients, power allocation, and receive beamforming. It develops an alternating-optimization framework that transforms the problem into tractable subproblems, employing real-valued representations, successive convex approximation, and DC programming to derive convex surrogates for the RIS and receive-beamforming subproblems, while keeping the power-allocation subproblem convex. The proposed method demonstrates fast convergence and substantial sum-rate gains over conventional schemes in simulations, validating transmissive RIS as a viable direction for next-generation wireless networks. The results offer a concrete co-design methodology for RIS-based transmitters and establish the performance benefits of joint optimization in RIS-MIMO systems.

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.

Figures (4)

• Figure 1: Transmissive RIS MIMO Networks.
• Figure 2: Convergence performance.
• Figure 3: Sum rates versus transmit power threshold.
• Figure 4: Average sum rates versus RIS elements.