NOMA-Enhanced Quantized Uplink Multi-user MIMO Communications

This research examines quantized uplink multi-user MIMO communication systems with low-resolution quantizers at users and base stations (BS). In such a system, we employ the non-orthogonal multiple access (NOMA) technique for communication between users and the BS to enhance communication performance. To maximize the number of users that satisfy the quality of service (QoS) requirement while minimizing the user’s transmit power, we jointly optimize the transmit power and precoding matrices at the users and the digital beamforming matrix at the BS. Owing to the non-convexity of the objective function, we transform the problem into a reinforcement learning-based problem and propose a deep reinforcement learning (DRL) framework named QNOMA-DRLPA to overcome the challenge. Because the nature of the action decided by the DRL algorithm may not satisfy the problem constraints, we propose a postactor process to redesign the actions to meet all the problem constraints. In the simulation, we assess the proposed framework’s performance in training convergence and demonstrate its superior performance under various environmental parameters compared with other benchmark schemes.