Channel Assignment for Hybrid NOMA Systems With Deep Reinforcement Learning

Abstract

The Hybrid Non-Orthogonal Multiple Access (NOMA) is a promising candidate for multiple access techniques of future wireless communication, which integrates orthogonal multiple access and traditional NOMA. The performance of hybrid NOMA systems depends on resource allocation including power and channel. In this letter, we focus on the channel assignment. Since channel assignment needs to be adapted to a real-time changing environment and accomplished in a restricted time slot, we treat the optimization of the dynamic channel assignment problem as a deep reinforcement learning task, to achieve better environmental adaptability with low time complexity. Simulation results show that the proposed method achieves better performance in terms of sum rate and spectral efficiency, compared to conventional methods.