Human self-blockage can severely attenuate the mmWave signal and degrade the throughput, even in the absence of environmental blockages. Compared with environmental blockages, the human self-blockage is highly related to the direction of human movements, which has strong spatio-temporal correlations, and can be used to reduce beam training overheads meanwhile improve the throughput. In particular, we propose a convolutional long-short term memory (ConvLSTM) based deep spatio-temporal beam training algorithm, which can accurately infer the optimal beam by probing only a small portion of beams. Simulation results demonstrate that the proposed algorithm can provide a higher average throughput than the state of the arts.


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    Titel :

    Deep Spatio-temporal Beam Training for mmWave Communications with Human Self-blockage


    Beteiligte:
    Shan, Wenxing (Autor:in) / Ma, Yiming (Autor:in) / Wang, Zicun (Autor:in) / Zhang, Lin (Autor:in) / Xiao, Ming (Autor:in)


    Erscheinungsdatum :

    10.10.2023


    Format / Umfang :

    1171827 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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