Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or machine learning (e.g., reinforcement learning). In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL). On one hand, the usage of RL overcomes many challenges from the model-based optimization schemes. On the other hand, federated learning (FL) enables agents to deal with a number of practical issues, such as privacy, communication overhead, and exploration efficiency. The framework of FRL is then implemented by the in-exact alternative direction method of multipliers (ADMM), where subproblems are solved approximately using policy gradients and their second moments. The developed algorithm, FRLPGiA, has a nice numerical performance compared with some baseline methods for solving the resource allocation problem in a V2X network.


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    Titel :

    Federated Reinforcement Learning for Resource Allocation in V2X Networks


    Beteiligte:
    Xu, Kaidi (Autor:in) / Zhou, Shenglong (Autor:in) / Li, Geoffrey Ye (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    485250 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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