Multi-modality Sensing Aided Beam Prediction for mmWave V2V Communications

To ensure the transmission reliability of vehicular communication network, precisely aligned beamforming of millimeter-wave communication using massive multi-input multi-output(mMIMO) technology is urgently required. In highly dynamic vehicular communication scenarios, traditional beam alignment schemes incur significant resource overhead and struggle to establish reliable links within the coherence time. To address this critical challenge, a scheme of multi-modality sensing aided beam prediction for mmWave V2V communications is proposed. Two non-RF sensing modalities, vision and ranging (LiDAR) point cloud, are integrated, and deep neural networks are employed for feature extraction and integration of multi-modal information. Accurate matching and deep fusion of image space semantic information and physical space location information are achieved through perspective projection. By collaborative sensing coordinate calibration and vehicle position prediction, the features of physical environment are accurately mapped to the angular-domain channel, enabling real-time and precise beam prediction. The experimental results on the mixed multi-modal sensing-communication dataset (M3 SC) show that the proposed scheme achieves high angle tracking accuracy and achievable communication rate. © 2023 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.