Robust Sum-Rate Maximization in Transmissive RMS Transceiver-Enabled SWIPT Networks

TL;DR

This paper introduces a robust optimization framework for maximizing sum-rate in transmissive RMS-enabled SWIPT networks, addressing channel uncertainties and non-linear energy harvesting.

Contribution

It develops a novel joint optimization method using AO, SCA, and DC programming for transceiver design under imperfect CSI in RMS SWIPT systems.

Findings

Proposed algorithm converges reliably.

Achieves higher sum-rate than benchmark methods.

Effective in handling channel uncertainties.

Abstract

In this paper, we propose a state-of-the-art downlink communication transceiver design for transmissive reconfigurable metasurface (RMS)-enabled simultaneous wireless information and power transfer (SWIPT) networks. Specifically, a feed antenna is deployed in the transmissive RMS-based transceiver, which can be used to implement beamforming. According to the relationship between wavelength and propagation distance, the spatial propagation models of plane and spherical waves are built. Then, in the case of imperfect channel state information (CSI), we formulate a robust system sum-rate maximization problem that jointly optimizes RMS transmissive coefficient, transmit power allocation, and power splitting ratio design while taking account of the non-linear energy harvesting model and outage probability criterion. Since the coupling of optimization variables, the whole optimization problem is non-convex and cannot be solved directly. Therefore, the alternating optimization (AO) framework is implemented to decompose the non-convex original problem. In detail, the whole problem is divided into three sub-problems to solve. For the non-convexity of the objective function, successive convex approximation (SCA) is used to transform it, and penalty function method and difference-of-convex (DC) programming are applied to deal with the non-convex constraints. Finally, we alternately solve the three sub-problems until the entire optimization problem converges. Numerical results show that our proposed algorithm has convergence and better performance than other benchmark algorithms.