Decoupling or Learning: Joint Power Splitting and Allocation in MC-NOMA With SWIPT

Abstract

Non-orthogonal multiple access (NOMA) is one of the most significant technologies to meet the demand of high spectral efficiency (SE) in the fifth generation (5G) cellular networks. The utilization of simultaneous wireless information and power transfer (SWIPT) contributes to prolonging the battery life of the mobile users (MUs) and enhancing the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). In this paper, we study the achievable data rate maximization problem for the downlink multi-carrier NOMA (MC-NOMA) network with power splitting (PS)-based SWIPT, in which power allocation and PS control are jointly optimized with the limitation of available power budget as well as the requirement for EH. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of the coupled variables and the inter-user interference. To cope with the problem, a decoupled approach is developed, in which the power allocation and PS control are separated and the corresponding sub-problems are respectively solved through Lagrangian duality method. Furthermore, an alternative approach based on deep learning is proposed, which is capable of effectively obtaining the approximate optimal solution according to the empirical data. Simulation results confirm the effectiveness of the proposed schemes, and demonstrate the superiority of the combination of PS-based SWIPT with MC-NOMA over SWIPT-aided single-carrier NOMA (SC-NOMA) and SWIPT-aided orthogonal multiple access (OMA).