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.


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    Title :

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


    Contributors:
    Yang, Zhong (author) / Li, Yaxing (author) / Liu, Hongbo (author) / He, Fangmin (author)


    Publication date :

    2022-09-01


    Size :

    1151221 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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