A Deep Learning Based Channel Estimation for High Mobility Vehicular Communications

Abstract

In this paper, a decision-directed (DD)-channel estimation (CE) algorithm by employing deep learning (DL) is proposed for high mobility vehicular environments. The proposed algorithm relies on DL for channel prediction without prior knowledge about channel statistics, such as the channel covariance matrix and its time variation. Therefore, it does not require any prior Doppler rate estimation, which is highly complicated in high mobility environments. Based on the performed simulations, comparing to the Kalman filter-based DD-CE algorithm, our DL-based algorithm results in more reliable communications. It has been shown that comparing to the minimum mean square error (MMSE)-based DD-CE algorithm, our DL-based algorithm imposes much lower complexity to the system and the performance degradation is small compared to the MMSE-based DD-CE algorithm that requires the exact value of the Doppler rate.