A novel reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access mobile edge computing (NOMA-MEC) framework is proposed to release the heavy transmission delay of the edge devices (EDs) in next-generation wireless communication networks. We formulate a stochastic optimization problem that jointly optimizes the phase-shifter design of the RIS, the task offloading decision and the computing resource allocation of MEC, to minimize the overall transmission delay of all EDs in a long-term manner. The mathematical solution for the formulated optimization problem is a long-term offline policy which is non-trivial for conventional optimization approaches due to high computational complexity and stringent delay constraint. Therefore, we propose a federated reinforcement learning (FRL) approach for the formulated optimization problem to obtain the optimal solution taking advantages of the computing resource of all EDs. Moreover, a reputation-enabled ED selection scheme is proposed in the FRL approach that takes the task offloading history into consideration. The proposed RIS-aided NOMA-MEC framework is capable of outperforming conventional orthogonal multiple access (OMA) enabled RIS-MEC networks. The proposed FRL scheme achieves a near-optimal performance when the computional task is image classification in the MNIST and the IRIS dataset.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Federated Reinforcement Learning for RIS-Aided Non-Orthogonal Multiple Access MEC


    Beteiligte:
    Yang, Zhong (Autor:in) / Li, Yaxing (Autor:in) / Liu, Hongbo (Autor:in) / He, Fangmin (Autor:in)


    Erscheinungsdatum :

    01.09.2022


    Format / Umfang :

    1151221 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Enhancing WiFi Multiple Access Performance with Federated Deep Reinforcement Learning

    Zhang, Lyutianyang / Yin, Hao / Zhou, Zhanke et al. | IEEE | 2020



    Cache-Aided Device-to-Device Non-Orthogonal Multiple Access

    Shen, Kevin Z. / Alharbi, Turki E. A. / So, Daniel K. C. | IEEE | 2020



    Non-Orthogonal Multiple Access for Dual-Hop Decode-and-Forward Relay-Aided X Channel

    Wan, Dehuan / Wen, Miaowen / Ji, Fei et al. | IEEE | 2017