Effective beam alignment is essential for vehicle-to-infrastructure (V2I) millimeter wave (mmWave) communication systems, particularly in high-mobility vehicle scenarios. This paper explores a three-dimensional (3D) vehicle environment and introduces a novel deep learning (DL)-based beam search method that incorporates an image-based coding (IBC) technique. The mmWave beam search is approached as an image processing problem based on situational awareness. We propose IBC to leverage the locations, sizes, and information of vehicles, and utilize convolutional neural network (CNN) to train the image dataset. Consequently, the optimal beam pair index(BPI)can be determined. Simulation results demonstrate that the proposed beam search method achieves satisfactory performance in terms of accuracy and robustness compared to conventional methods.
Image-Based Beam Tracking With Deep Learning for mmWave V2I Communication Systems
IEEE Transactions on Intelligent Transportation Systems ; 25 , 11 ; 19110-19116
01.11.2024
3935541 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch