Position-Aided Compressive Channel Estimation and Tracking for Millimeter Wave Multi-User MIMO Air-to-Ground Communications

Configuring the antenna arrays from the channel estimates in millimeter wave (mmWave) communications is challenging in the presence of highly mobile users, as in the case of air-to-ground (A2G) networks. In this paper, we address the problem of channel tracking in the context of a multi-user A2G mmWave network. We propose to obtain channel information using the Maximum Likelihood (ML) criterion, while leveraging the sparse nature of the mmWave channel and using prior position information on the UAVs. Simulation results show that, even under small Rician factors of the A2G channel, obtained from ray-tracing, the proposed algorithm is able to keep track of the multi-user channel and maintain near-optimal data rates, thereby making it a suitable candidate for upcoming cellular-supported mmWave A2G deployments.