Setting the transmit power setting of 5G cells has been a long-term topic of discussion, as optimized power settings can help reduce interference and improve the quality of service to users. Recently, machine learning (ML)-based, especially reinforcement learning (RL)-based control methods have received much attention. However, there is little discussion about the generalisation ability of the trained RL models. This paper points out that an RL agent trained in a specific indoor environment is room-dependent, and cannot directly serve new heterogeneous environments. Therefore, in the context of Open Radio Access Network (O-RAN), this paper proposes a distributed cell power-control scheme based on Federated Reinforcement Learning (FRL). Models in different indoor environments are aggregated to the global model during the training process, and then the central server broadcasts the updated model back to each client. The model will also be used as the base model for adaptive training in the new environment. The simulation results show that the FRL model has similar performance to a single RL agent, and both are better than the random power allocation method and exhaustive search method. The results of the generalisation test show that using the FRL model as the base model improves the convergence speed of the model in the new environment.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning


    Beteiligte:
    Li, Peizheng (Autor:in) / Erdol, Hakan (Autor:in) / Briggs, Keith (Autor:in) / Wang, Xiaoyang (Autor:in) / Piechocki, Robert (Autor:in) / Ahmad, Abdelrahim (Autor:in) / Inacio, Rui (Autor:in) / Kapoor, Shipra (Autor:in) / Doufexi, Angela (Autor:in) / Parekh, Arjun (Autor:in)


    Erscheinungsdatum :

    2022-09-01


    Format / Umfang :

    2030841 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learning

    Ben Elallid, Badr / Abouaomar, Amine / Benamar, Nabil et al. | IEEE | 2023


    Data-Driven Automotive Development: Federated Reinforcement Learning for Calibration and Control

    Rudolf, Thomas / Schürmann, Tobias / Skull, Matteo et al. | Springer Verlag | 2022


    Communication-efficient and federated multi-agent reinforcement learning

    Krouka, M. (Mounssif) / Elgabli, A. (Anis) / Issaid, C. B. (Chaouki Ben) et al. | BASE | 2022

    Freier Zugriff