Joint Energy and Information Precoding for NOMA-Based WPCNs Aided by Reconfigurable Intelligent Surface

Abstract

In a wireless powered communication network (WPCN), wireless energy acquisition is weak due to the fading characteristic of wireless link, which makes it hard to supply the energy. Reconfigurable intelligent surface (RIS) is a promising performance enhancement technology for WPCNs, which can improve the energy and spectral efficiency, expand the network coverage, and thus improve the throughput. This article proposes a new framework for energy harvesting and information transmission of non-orthogonal multiple access (NOMA)-based WPCNs aided by RIS. The total transmission capacity is maximized by joint optimization of energy precoding, information precoding, and the phase shift of RIS, which turns out to be a non-convex problem. Under the multi user scenario, we propose a joint optimization algorithm combing alternating optimization (AO), semidefinite relaxation (SDR), and successive convex approximation (SCA) methods, which is called ASS algorithm for short. Numerical results prove that compared with the non-RIS system, the ASS can increase the throughput of the system under multiuser scenario by 502.49%. For the system under single scenario, the energy precoding and information precoding are determined respectively by using the beamforming and the traditional maximal ratio combining (MRC) algorithm. The optimization problem is transformed into a complex higher order form optimization problem. Finally, a closed-form expression of the channel capacity is derived.