Deep Learning-Based Nonstationary Channel Prediction in Tactical Vehicle-to-Vehicle Communication Environments

In this paper, we focus on the vehicle-to-vehicle dynamic channel in tactical communication environments, which shows time-varying and nonstationary characteristics due to the fast mobility, directional antennas, and harsh terrain. These situations present great challenges for the channel state information (CSI) acquisition. To obtain an accurate CSI and reduce pilot overhead, we propose a CSI predictor based on the long short-term memory (LSTM) network. As an improved recurrent neural network (RNN), LSTM units have an excellent learning result on both long- and short-term inputs by adding the gating mechanism. Using the outdated sampling CSI sequence as input data of LSTM units enables the predictor to extract complex data characteristics and capture the temporal law of the nonstationary channel. Simulation results are demonstrated to verify that the LSTM-based predictor has better performance than conventional algorithms in IEEE 802.11p standard. Additionally, the key factors that affect the performance of the proposed predictor are further analyzed.