Location-assisted Subspace-based Beam Alignment in LOS/NLOS mm-wave V2X Communications

This paper proposes techniques for Beam Alignment (BA) in millimeter wave (mm-wave) Vehicle-to-Everything (V2X) communications with realistic modeling of the dynamic space-time multipath channels. Starting from existing mm-wave channel models, an extension to simulate consistent dynamics of the multipath parameters as vehicles move is introduced. Different BA techniques are then presented, where side location information is used to assist the selection of the optimal pair of beam-pointers. We claim the possibility to exploit the low-rank (LR) structure of the sparse mm-wave channel matrix, jointly with location-related long-term statistics, to avoid time-consuming scanning of the beamformer codebook. The proposed method uses pre-computed eigen-beamformers based on predicted vehicle location and pre-acquired dataset of geo-referenced long-term channel state information to align the beams. Performance analysis in realistic dynamic channel scenarios indicate that the proposed method outperforms conventional BA strategies, avoiding time-consuming beam sweeping procedures.