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- W4288055505 abstract "Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status. In this paper, we propose a novel beam alignment framework that leverages images taken by cameras installed at the mobile user. Specifically, we utilize 3D object detection techniques to extract the size and location information of the dynamic vehicles around the mobile user, and design a deep neural network (DNN) to infer the optimal beam pair for transceivers without any pilot signal overhead. Moreover, to avoid performing beam alignment too frequently or too slowly, a beam coherence time (BCT) prediction method is developed based on the vision information. This can effectively improve the transmission rate compared with the beam alignment approach with the fixed BCT. Simulation results show that the proposed vision based beam alignment methods outperform the existing LIDAR and vision based solutions, and demand for much lower hardware cost and communication overhead." @default.
- W4288055505 created "2022-07-28" @default.
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- W4288055505 date "2022-07-23" @default.
- W4288055505 modified "2023-10-16" @default.
- W4288055505 title "Computer Vision Aided mmWave Beam Alignment in V2X Communications" @default.
- W4288055505 doi "https://doi.org/10.48550/arxiv.2207.11409" @default.
- W4288055505 hasPublicationYear "2022" @default.
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