Power Allocation for Sum Rate Maximization in 5G NOMA System with Imperfect SIC: A Deep Learning Approach

Abstract

Non-orthogonal multiple access (NOMA) is regarded as a promising technology for enhancing the spectral efficiency (SE) in 5G communication system. In this paper, we propose a power allocation scheme for maximizing sum rate for downlink NOMA system in the presence of imperfect successive interference cancellation (SIC). The proposed scheme uses deep learning to predict the optimal power allocation through exhaustive search. Simulation results reveal that the proposed scheme can achieve the sum rate performance close to the optimal scheme but with much lower computational complexity.