Traditional path loss methods typically employ statistical or empirical models, without fully considering the dynamic propagation environment. In this paper, we introduce a method called Visual Sensing-Based Path Loss Prediction (VSB-PLM), which predicts path loss using visual data obtained from multi-view sensing cameras. Specifically, we deploy multi-view cameras in real-world scenarios. Then, a Convolutional Neural Network (CNN) is designed to integrate environmental image features, the existence of the Line-Of-Sight (LOS) path, and the distance between the Transmitter (Tx) and Receiver (Rx) for path loss prediction. Finally, optimal path loss prediction results are obtained utilizing a multi-view selection algorithm. Simulation results demonstrate that the proposed algorithm has successfully improved path loss prediction accuracy by 9% compared to single-view sensing, achieving a Root Mean Squared Error (RMSE) of 3.66 dB.


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    Titel :

    Visual Sensing-Based Path Loss Prediction Method


    Beteiligte:
    Tian, Yixuan (Autor:in) / Sun, Yutong (Autor:in) / Yu, Li (Autor:in) / Zhang, Jianhua (Autor:in) / Zhang, Yuxiang (Autor:in) / Liu, Guangyi (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    1066933 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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