An EM-Based User Clustering Method in Non-Orthogonal Multiple Access

Power domain non-orthogonal multiple access (NOMA), with the ability to serve multiple users within one resource block, is one of the most promising technologies for the fifth generation. In this paper, we study the downlink millimeter wave (mmWave) NOMA-based system, where the base station sends messages to multiple clusters and serves multiple users simultaneously, and sum-rate maximization problem is investigated. Since users are multiplexed on one resource block, user clustering is important for NOMA and has a great influence on sum-rate optimization problem. Inspired by correlation features of users' spatial distributions in mmWave NOMA-based system, we introduce unsupervised learning method into user clustering. We first develop an Expectation Maximization (EM)-based algorithm in fixed user scenario. Then, the dynamic user scenario is introduced, which includes user reduction, increment and movement situations. After that, an online EM-based clustering algorithm is proposed to fast update user distribution parameters with lower computational complexity compared to the conventional complete re-clustering methods. Simulation results show that the proposed EM-based algorithm can improve the performance of NOMA-based system in fixed user scenario. In addition, the proposed online EM-based algorithm can achieve similar performance as the complete EM-based algorithm with less computational complexity in dynamic user scenario.