Design and Optimization for Transmissive RIS Transceiver Enabled Uplink Communication Systems

TL;DR

This paper proposes a novel uplink communication system using transmissive RIS and OFDMA, optimizing resource allocation and RIS coefficients to maximize sum-rate, with an iterative algorithm demonstrating improved performance.

Contribution

Introduces a new transmissive RIS transceiver architecture and a joint optimization framework for uplink OFDMA systems, solving a complex non-convex problem with an alternating optimization algorithm.

Findings

The proposed algorithm converges effectively.

System sum-rate is significantly improved.

Numerical results outperform benchmark algorithms.

Abstract

In this paper, a novel transmissive reconfigurable intelligent surface (RIS) enabled uplink communication system with orthogonal frequency division multiple access (OFDMA) is investigated. Specifically, a non-conventional receiver architecture equipped with a single receiving horn antenna and a transmissive RIS is first proposed, and a far-near field channel model based on planar waves and spherical waves is also given. Then, in order to maximize the system sum-rate of uplink communications, we formulate a joint optimization problem over subcarrier allocation, power allocation and RIS transmissive coefficient design while taking account of user quality-of-service (QoS) constraint. Due to the coupling of optimization variables, the optimization problem is non-convex, so it is challenging to solve it directly. In order to tackle this problem, the alternating optimization (AO) algorithm is utilized to decouple the optimization variables and divide the problem into two sub-problems to solve. First, the problem of joint subcarrier allocation and power allocation is solved via the Lagrangian dual decomposition method. Then, the RIS transmissive coefficient design scheme can be obtained by applying difference-of-convex (DC) programming, successive convex approximation (SCA) and penalty function methods. Finally, the two sub-problems are iterated alternately until convergence is achieved. Numerical results verify that the proposed algorithm has good convergence performance and can improve sum-rate of the proposed system compared with other benchmark algorithms.