Radar and sensing assisted beamforming in vehicular communication

Radar-assisted beamforming in millimeter-wave (mmWave) massive MIMO (multiple-input multiple-output) systems helps minimize beam tracking overhead, making it highly effective for communication between roadside units (RSUs) and vehicles. While most studies focus on a single RSU and Line-of-Sight (LoS) conditions, this research explores multiple RSUs performing mmWave distributed MIMO transmission, where temporary Non-Line-of-Sight (NLoS) conditions arise due to obstacles. The work incorporates Bayesian NLoS detection with Extended Kalman Filtering (EKF) to ensure consistent coverage and high data rates. Additionally, in vehicle-to-everything (V2X) systems, RSUs can handle both sensing and communication tasks, enhancing communication by utilizing distance, angle, and velocity data from sensing to reduce beam tracking overhead. This study extends these ideas to scenarios involving distributed RSUs and MIMO systems, deriving a state evolution model, formulating both Bayesian NLoS detection and EKF equations, and implementing predictive beamforming. Simulation results show that, compared to colocated massive MIMO antennas, distributed antennas provide more uniform, robust sensing, broader coverage, and higher data rates, particularly for moving vehicles.