User Association for Millimeter-Wave Networks: A Machine Learning Approach

Abstract

Millimeter-wave (mmWave) communication has been regarded as one of the most promising means to improve the cellular system capacity in the fifth-generation (5G) era. Compared with the conventional microwave communication networks, mmWave terminals should connect with multiple base stations (BSs) simultaneously to prevent the signal blockage. Meanwhile, the accurate instantaneous channel state information (CSI) is difficult to estimate and collect due to the densification of mmWave BSs. These unique characteristics pose stiff challenges to user association in mmWave networks. To deal with these issues, we develop a novel machine learning based user association approach to support multi-connectivity in mmWave networks. Specifically, we first formulate the mmWave user association problem as a multi-label classification problem, which is then transformed into a series of single-label classification problems through efficient multi-label classification algorithms. To further reduce the requirement on the amount of training samples, we utilize graphical model to represent the user association scenario and adopt novel feature extraction methods to obtain appropriate features from both geographical location information and topological information. With appropriate features, each single-label classification problem can be trained in a supervised manner. Test results show that the proposed approach can achieve a good performance with only a few training samples and without the need of CSI.