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

In this article, 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 nonlinear energy harvesting model and outage probability criterion. Since the coupling of optimization variables, the whole optimization problem is nonconvex and cannot be solved directly. Therefore, the alternating optimization (AO) framework is implemented to decompose the nonconvex original problem. In detail, the whole problem is divided into three subproblems to solve. For the nonconvexity of the objective function, successive convex approximation (SCA) is used to transform it, and the penalty function method and difference-of-convex (DC) programming are applied to deal with the nonconvex constraints. Finally, we alternately solve the three subproblems until the entire optimization problem converges. Numerical results show that our proposed algorithm has convergence and better performance than other benchmark algorithms.

Automated summary

In this article, a state-of-the-art downlink communication transceiver design is proposed for transmissive reconfigurable metasurface (RMS)-enabled simultaneous wireless information and power transfer (SWIPT) networks. The design includes a feed antenna for beamforming and builds spatial propagation models for plane and spherical waves. A robust system sum-rate maximization problem is formulated considering imperfect channel state information (CSI), and the nonconvex optimization problem is solved using an alternating optimization (AO) framework with successive convex approximation (SCA), penalty function method, and difference-of-convex (DC) programming. Numerical results show the proposed algorithm has convergence and outperforms other benchmark algorithms.