A Deep Q-Network Based-Resource Allocation Scheme for Massive MIMO-NOMA

Abstract

In this letter, a deep Q-learning network (DQN) based resource allocation (RA) scheme is proposed for the massive multiple-input multiple-output (MIMO)- nonorthogonal multiple access (NOMA) systems. The reinforcement learning (RL) frame is developed to build an iterative optimization structure for user clustering, power allocation and beamforming. Specifically, a DQN is designed to group the users based on the reward item calculated after power allocation and beamforming. The objective is to maximize the reward item, i.e., the system throughput. Then, a back propagation neural network (BPNN) is used to realize the power allocation. During the training of BPNN, the exhaustive search results in the quantized power set are taken as the output labels. Simulation experiments show that the proposed scheme can achieve high system spectrum efficiency approximating to the exhaustive search based on user clustering and power allocation.