In order to realize the future Sixth-Generation (6G), the research on Integrated Sensing and Communication (ISAC) channel modeling has attracted considerable attention. In classical cluster-based models, it is difficult to effectively build high density point clouds to outline object features. As an alternative, slightly-distributed-scatterer-based signal models are utilized in this paper, and a first-order generalized array manifold is used to approximate the spread features of channel. By combining this with the space-alternating generalized expectation-maximization (SAGE) algorithm, we achieve a framework for the efficient estimation of the dispersive-path components including the spread on the angle and the Doppler frequency domains. Based on measured data in vehicular environments, the effectiveness of the algorithm is verified, and a time-varying statistical channel model is formulated. The results obtained can help to accurately model vehicular channels and provide guidance for other applications of autonomous driving.


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    Titel :

    Slight-Distributed-Scatterer-Based Time-Varying Channel Modeling for Vehicular Environments


    Beteiligte:
    Zhu, Pengqi (Autor:in) / Rodriguez-Pineiro, Jose (Autor:in) / Yin, Xuefeng (Autor:in) / Li, Gang (Autor:in) / Gutierrez, Carlos A. (Autor:in) / Li, Xuyang (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    4192363 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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