Unmanned aerial vehicles (UAVs) have found expanding utilization in smart agriculture. Path loss (PL) is of significant importance in the link budget of UAV-aided air-to-ground (A2G) communications. This paper proposes a machine-learning-based PL model for A2G communication in agricultural scenarios. On this basis, a double-weight neurons-based artificial neural network (DWN-ANN) is proposed, which can strike a fine equilibrium between the amount of measurement data and the accuracy of predictions by using ray tracing (RT) simulation data for pre-training and measurement data for optimization training. Moreover, an RT pre-correction module is introduced into the DWN-ANN to optimize the impact of varying farmland materials on the accuracy of RT simulation, thereby improving the accuracy of RT simulation data. Finally, channel measurement campaigns are carried out over a farmland area at 3.6 GHz, and the measurement data are used for the training and validation of the proposed DWN-ANN. The prediction results of the proposed PL model demonstrate a fine concordance with the measurement data and are better than the traditional empirical models.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Air-to-Ground Path Loss Model at 3.6 GHz under Agricultural Scenarios Based on Measurements and Artificial Neural Networks


    Beteiligte:
    Hanpeng Li (Autor:in) / Kai Mao (Autor:in) / Xuchao Ye (Autor:in) / Taotao Zhang (Autor:in) / Qiuming Zhu (Autor:in) / Manxi Wang (Autor:in) / Yurao Ge (Autor:in) / Hangang Li (Autor:in) / Farman Ali (Autor:in)


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    757 Path Loss Measurements

    Horton, Kent / Huffman, Mitch / Eppic, Brian et al. | NTRS | 2005



    Path-following control of autonomous ground vehicles based on input convex neural networks

    Jiang, Kai / Hu, Chuan / Yan, Fengjun | SAGE Publications | 2022


    Gas turbine gas-path fault identification using nested artificial neural networks

    Fentaye, Amare D. / Baheta, Aklilu T. / Gilani, Syed Ihtsham Ul-Haq | Emerald Group Publishing | 2018


    Path Loss Measurements on Cylindrical Sections

    Fuller, G. / IEEE / AIAA | British Library Conference Proceedings | 2000