Unsupervised User Clustering in Non-orthogonal Multiple Access

Non-orthogonal multiple access (NOMA) is one of the most promising technologies in fifth-generation mobile communication system for its advantages in serving multiuser simultaneously and enhancing spectrum efficiency. In this paper, we investigate the optimization problem of sum-rate maximization for NOMA-based system, and mainly focus on user clustering. Inspired by the correlation features of users, we introduce machine learning in user clustering. We first develop an expectation maximization (EM) based algorithm for fixed user scenario. Then, the dynamic user scenario is considered and an online EM (OLEM) based clustering algorithm is proposed. Simulation results show that the proposed EM-based and OLEM-based algorithms outperform the state-of-the-art algorithms in fixed and dynamic user scenario, respectively.