Subchannel Allocation in Massive Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiple Access and Hybrid Beamforming Systems with Deep Reinforcement Learning

In this study, we emphasize that the maximum sum rate can be achieved through AI-based subchannel allocation, while taking into account all users’ quality of service (QoS) requirements in data rates for hybrid beamforming systems. We assume a limited number of radio frequency (RF) chains in practical hybrid beamforming architectures. This constraint makes subchannel allocation a critical aspect of hybrid beamforming in massive multiple-input multiple-output (MIMO) systems with orthogonal frequency division multiple access (MIMO-OFDMA), as it enables the system to serve more users within a single time slot. Unlike conventional subcarrier allocation methods, we employ a deep reinforcement learning (DRL)-based algorithm to address real-time decision-making challenges. Specifically, we propose a dueling double deep Q-network (Dueling-DDQN) to implement dynamic subchannel allocation. Simulation results demonstrate that the performance of the proposed algorithm gradually approaches that of the greedy method. Furthermore, both the average sum rate and the average spectral efficiency per user improve with a reasonable variation in outage probability.