Unmanned aerial vehicles (UAVs) aid vehicular networks (V2X) in ensuring efficient edge computation and communication. IRS cited its ability to intelligently regulate wireless channels and low-cost advantages as potential technologies to improve performance in UAV-aided V2X networks. In this paper, a reparameterization-based reinforcement learning scheme is designed jointly to minimize the system energy consumption for continuous UAV trajectory optimization, beam design, edge computation resource allocation, and discrete UAV-vehicle-IRS association. Simulation results verified the efficiency and properties of the proposed learning-based scheme and outlined the advantages of IRS.


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    Titel :

    Intelligent Edge Computation and Trajectory Optimization in IRS-Enhanced UAV-Aided Vehicular Wireless Networks


    Beteiligte:
    Cao, Xueyan (Autor:in) / Wu, Xiaolong (Autor:in) / Zhang, Shuhan (Autor:in) / Ren, Tao (Autor:in)


    Erscheinungsdatum :

    25.10.2024


    Format / Umfang :

    599462 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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