Machine Learning-based Channel Tracking for Next-Generation 5G Communication System

Abstract

The use of millimeter-wave (mmWave) frequencies is a promising technology for meeting the ever-growing data traffic in next-generation wireless communications. A major challenge of mmWave communications is the high path loss. To overcome this issue, mmWave systems adopt beamforming techniques, which require robust channel estimation and tracking algorithms to maintain an adequate quality of service. In this study, we propose the machine learning-based channel tracking algorithm for vehicular mmWave communications. In this paper, we propose a long short-term memory (LSTM)-based channel tracking algorithm for vehicle-to-infrastructure mmWave communications. The bidirectional LSTM is leveraged to track the channel. Simulation results demonstrate that the proposed algorithm efficiently tracks the mmWave channel with negligible training overhead.