User selection and dynamic power allocation in the SWIPT-NOMA relay system

Non-orthogonal multiple access (NOMA) technology provides an effective solution to massive access with a high data rate demand in new-generation mobile networks. The paper combinations with NOMA and simultaneous wireless information and power transfer (SWIPT) relay to maximize the sum rate in the downlink system. To that end, it is critical how to select effectively users access system and power allocation for the access user. This paper proposes a user selection and dynamic power allocation (USDPA) scheme in the NOMA-SWIPT relay system based on neural network because traditional optimization methods have difficulty solving nonlinear and non-convex problems. We establish a user selection network utilizing a deep neural network (DNN) and propose a power allocation network using deep reinforcement learning. The simulation results show that the proposed scheme achieves better performance than other related schemes, especially for high quality of service requirements.