Digital twin (DT), which integrates physical networks and digital space by using advanced technologies of sensing, communication and computation, has been envisioned as a promising paradigm for improving the quality of service in physical systems. In this paper, we propose a federated learning (FL)-enabled DT system consisting of the physical layer and DT layer. With FL, all wireless devices (WDs) can collaborate to update a universal DT model, after the DT server cluster (DSC) aggregates all the local models sent by the WDs with non-orthogonal multiple access (NOMA). Moreover, an action model based on the DT system is also updated to optimize the operations of WDs. To increase the energy efficiency, we formulate a problem to minimize the cost of the total energy consumption of the system by optimizing the time allocation of local training, uploading the local models, generating the action model as well as broadcasting the action model and DT model. The numerical results validate the effectiveness and efficiency of our proposed algorithm.


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    Title :

    Energy Efficient Digital Twin with Federated Learning via Non-orthogonal Multiple Access Transmission


    Contributors:
    Wang, Tianshun (author) / Huang, Ning (author) / Dai, Minghui (author) / Wu, Yuan (author) / Qian, Liping (author) / Lin, Bin (author)


    Publication date :

    2022-06-01


    Size :

    1121202 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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