Beamforming for Transmissive RIS Transceiver Enabled Simultaneous Wireless Information and Power Transfer Systems

TL;DR

This paper presents a novel beamforming approach for a transmissive RIS transceiver in SWIPT systems, optimizing sum-rate and energy harvesting with efficient algorithms.

Contribution

It introduces a new optimization framework and algorithms for joint beamforming and energy harvesting in RIS-enabled SWIPT systems, addressing non-convex constraints.

Findings

Proposed algorithms converge reliably and efficiently.

Low-complexity algorithm maintains performance with reduced computational load.

Validated effectiveness through numerical simulations.

Abstract

This paper investigates a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered simultaneous wireless information and power transfer (SWIPT) system with multiple information decoding (ID) and energy harvesting (EH) users. Under the considered system model, we formulate an optimization problem that maximizes the sum-rate of all ID users via the design of the TRIS transceiver's active beamforming. The design is constrained by per-antenna power limits at the TRIS transceiver and by the minimum harvested energy demand of all EH users. Due to the non-convexity of the objective function and the energy harvesting constraint, the sum-rate problem is difficult to tackle. To solve this challenging optimization problem, by leveraging the weighted minimum mean squared error (WMMSE) framework and the majorization-minimization (MM) method, we propose a second-order cone programming (SOCP)-based algorithm. Per-element power constraints introduce a large number of constraints, making the problem considerably more difficult. By applying the alternating direction method of multipliers (ADMM) method, we successfully develop an analytical, computationally efficient, and highly parallelizable algorithm to address this challenge. Numerical results are provided to validate the convergence and effectiveness of the proposed algorithms. Furthermore, the low-complexity algorithm significantly reduces computational complexity without performance degradation.