Platoon-based vehicular network in NR-V2X has been considered as a promising technology to assist reducing traffic congestion, saving vehicle fuel, and enhancing driving experience. Resource allocation is the basis for ensuring stable and safety vehicular networks. In this paper, we propose a Distributed Resource Allocation algorithm using Federated Multi agent Deep Reinforcement Learning (DRAFRL), which mathematically utilize the federated averaging (FedAvg) mechanism to reduce the variance between agents and achieve better transmission performance. The proposed algorithm consists of four steps: Firstly, each agent updates local model by deep deterministic policy gradient (DDPG) algorithm. Secondly, the agents upload local model parameters to the base station (BS) for federated aggregation. Thirdly, the BS performs weight aggregation using the FedAvg method and updates the global model. Finally, the BS distributes the optimized global model parameters to each agent. The simulation results show that the proposed algorithm outperforms other baseline algorithms while reducing the variance between agents by 93.5% and 99.1% compared with two baselines.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Federated Multi-Agent Deep Reinforcement Learning Approach for Resource Allocation in Platoon-Based NR-V2X


    Beteiligte:
    Liu, Yiming (Autor:in) / Wang, Qiang (Autor:in) / Chen, Jiaao (Autor:in) / Zhang, Wenqi (Autor:in) / Sun, Chen (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    4825994 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    AVDDPG: Federated reinforcement learning applied to autonomous platoon control

    Boin, Christian / Lei, Lei / Yang, Simon X. | ArXiv | 2022

    Freier Zugriff




    Federated Reinforcement Learning for Resource Allocation in V2X Networks

    Xu, Kaidi / Zhou, Shenglong / Li, Geoffrey Ye | IEEE | 2024