MmWave Beam Management in Urban Vehicular Networks
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DOI: 10.1109/jsyst.2020.2996909
Abstract: Millimeter-wave (mmWave) communication represents a potential solution to capacity shortage in vehicular networks. However, effective beam alignment between senders and receivers requires accurate knowledge of the vehicles’ position for fast beam steering, which is often… read more here.
Keywords: management urban; beam management; urban vehicular; mmwave beam ... See more keywords
Continuous-Time mmWave Beam Prediction With ODE-LSTM Learning Architecture
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DOI: 10.1109/lwc.2022.3221159
Abstract: In order to avoid frequent millimeter-wave (mmWave) beam training in high-speed scenarios, in this letter, we propose to exploit the neural ordinary differential equation (ODE) to predict the arbitrary-instant optimal beam between the current and… read more here.
Keywords: continuous time; mmwave beam; beam; lstm ... See more keywords
Deep Learning Assisted mmWave Beam Prediction for Heterogeneous Networks: A Dual-Band Fusion Approach
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DOI: 10.1109/tcomm.2022.3222345
Abstract: In this paper, motivated by the inter-base station (BS) channel dependence due to the shared wireless environment, we propose to fuse sub-6 GHz channel information and mmWave low-overhead measurement to predict the optimal mmWave beam… read more here.
Keywords: mmwave beam; sub ghz; prediction; deep learning ... See more keywords
LIDAR and Position-Aided mmWave Beam Selection With Non-Local CNNs and Curriculum Training
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Vehicular Technology"
DOI: 10.1109/tvt.2022.3142513
Abstract: Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility. To reduce the search overhead of iterative beam discovery… read more here.
Keywords: mmwave beam; beam; beam selection; non local ... See more keywords