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  • Graph neural network enabled propagation graph method for channel modeling [Elektronski vir]
    Wang, Xiping, telekomunikacije ...
    Channel modeling is considered as a fundamental step in the design, deployment, and optimization of vehicular wireless communication systems. For typical vehicular communication scenarios in urban ... areas, dense multipath may exist in the wireless channels. The propagation graph (PG) method is an efficient approach to simulate multipath radio propagation. In this paper, we extend the PG method into a Graph Neural Network (GNN) enabled data-driven method for calculating channel transfer function (CTF) and channel impulse response (CIR) in a given space. ChebNet, a classical GNN, is utilized for estimating the scattering coefficients of the edge gains in the PG method. The proposed GNN-enabled method performs better than baseline algorithms, such as multilayer perceptron (MLP), simulated annealing (SA) algorithm, and genetic algorithm (GA) in effectively estimating a large number of scattering coefficients in PG. Mean absolute errors of the proposed method are provided and evaluated in this paper. Additionally, the potential future research directions of the GNN-enabled PG method for channel modeling are discussed.
    Vir: IEEE transactions on vehicular technology. - ISSN 1939-9359 (Vol. 73, iss. , [in press] 2024, str. 1-11)
    Vrsta gradiva - e-članek ; neleposlovje za odrasle
    Leto - 2024
    Jezik - angleški
    COBISS.SI-ID - 191254787

    Povezava(-e):

    https://ieeexplore.ieee.org/document/10484975
    DOI

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