The Application of Machine Learning in mmWave-NOMA Systems

Machine learning has been used to develop efficiently optimizing algorithms for practical communication systems. This paper investigates the user clustering and power allocation problem in the millimeter wave non-orthogonal multiple access (mmWave-NOMA) transmission scenario, where we assume that the users' locations of different clusters follows a Poisson cluster process (PCP). Specifically, we develop a machine learning based user clustering algorithm for the application of NOMA. Moreover, to investigate the performance of the proposed mmWave-NOMA system, we derive the optimal power allocation coefficients in closed-form by assuming equal power on each beam. In the simulation results, we firstly investigate the impact of the number of clusters on the system performance. We further show the validation of the proposed machine-learning based user clustering algorithm in the mmWave-NOMA system.