Adaptive Beam Prediction for Enhancing mmWave V2I Communication Performance in Complex Real-World Scenarios

Traditional beam prediction approaches are suitable for dealing with point-like targets rather than extended targets, which are commonly detected by the millimeter-wave (mmWave) radar. To solve this problem, a novel adaptive beam prediction approach for complex real-world mmWave vehicle-to-infrastructure (V2I) communications is proposed in this paper. By adopting a vehicle kinematic model based on the axle load point and combining it with an adaptive extended Kalman filter (AEKF) algorithm, the proposed approach can accurately predict and track vehicle motion states and beam directions in time division duplexing (TDD) mode. The AEKF algorithm is employed to dynamically update the process and measurement noise covariance matrices to reduce the impact of various noises in real-world scenarios, which can enhance the accuracy of beam prediction, and then improve the received signal-to-noise ratio (SNR) and communication rate. During the downlink phase, the roadside unit (RSU) formulates transmit beam by using predicted beam direction, and then transmits integrated sensing and communication (ISAC) signals to the vehicle. After receiving the echo point clouds reflected by the vehicle body, the RSU can estimate its relative direction and motion states through the AEKF algorithm. In the uplink phase, the RSU uses the above estimated states to further predict the direction and vehicle states, and then estimate and predict them through the AEKF algorithm by using the received uplink signal. Simulation results and DeepSense 6G dataset-based test results show that the proposed approach offers significant improvements in both sensing and communication performance compared with traditional approaches.