Beam Aggregation-Based mmWave MIMO-NOMA: An AI-Enhanced Approach

Millimeter wave (mmWave) multiple-input-multiple-output (MIMO) with hybrid beamforming enables high data rate broadband services for 5G. To further improve the spectral efficiency and connectivity, non-orthogonal multiple access (NOMA) has been considered to be integrated with mmWave MIMO. Nonetheless, the existing power-domain mmWave MIMO-NOMA with narrow analog beams suffers from beam misalignments which deteriorate the data rates of the misaligned users as well as the system fairness. Thereby, we propose a beam aggregation-based mmWave MIMO-NOMA scheme to loosen the requirement of beam alignment and improve the system fairness. The proposed scheme generates virtual beams with wider beamwidth by aggregating adjacent analog beams. NOMA transmissions are utilized within each aggregated virtual beam. Then, we propose a non-orthogonal multiuser precoding scheme to ensure the fairness within the aggregated beam by maximizing the minimum achievable rates of the grouped users. To address this issue, a max-min problem is proposed and artificial intelligence (AI) technology is exploited to solve this non-trivial problem. The problem is firstly converted to an equivalent penalized minimization problem and then an unsupervised deep neural network (DNN) is trained to map the instantaneous channel coefficients to the precoders in a data-driven fashion. Specifically, we explicitly introduce the transmit power as the DNN input to achieve better generalization in different signal-to-noise ratio regions. Performance evaluations reveal that the proposed scheme can achieve significant gain on the max-min data rate compared with the conventional scheme.