Deep Learning Strategies for User Clustering in MIMO-NOMA Networks

The user clustering problem in an uplink MIMO NonOrthogonal Multiple Access (NOMA) scheme is considered here. The receiver is assumed to operate in two sequential stages that employ Linear Minimum Mean Squared Error (LMMSE) receivers. At the first stage, the receiver is designed to recover the transmission from a cluster of selected users. The contribution of these users is then subtracted from the received signal and the remaining user transmissions are then linearly recovered. The determination of which users should be detected during the first stage is formulated as a deep learning based multiple classification problem. In order to guarantee that the selection is robust to fast fading, the input to the neural network is based on second order channel statistics. Furthermore, the training process is simplified by using a large system approximation of the resulting sum-rates. Simulation results indicate that the proposed deep learning-based solution is able to achieve a significant rate advantage with respect to other lazy approaches, such as fixed or random cluster assignments.