Deep Learning-based Channel Estimation and Tracking for Millimeter-wave Vehicular Communications

Sangmi Moon, Hyunsung Kim, and Intae Hwang

108 177.177-184


Abstract :The application of millimeter-wave (mmWave) frequencies is a potential technology for satisfying the continuously increasing need for handling data traffic in highly advanced wireless communications. A substantial challenge presented in mmWave communications is the high path loss. mmWave systems adopt beamforming techniques to overcome this issue. These require robust channel estimation and tracking algorithm for maintenance of an adequate quality of service. In this study, we propose a deep learning-based channel estimation and tracking algorithm for vehicular mmWave communications. More specifically, a deep neural network is leveraged to learn the mapping function between the received omni-beam patterns and mmWave channel with negligible overhead. Following the channel estimation, long short-term memory is leveraged to track the channel. The simulation results demonstrate that the proposed algorithm estimates and tracks the mmWave channel efficiently with negligible training overhead. 

Index terms :Channel estimation, channel tracking, deep learning, deep neural network, long short-term memory, mmWave.