Digital Twin Edge Network (DITEN) is a new paradigm that constructs virtual models for resource allocation, system optimization, and fault prediction through real-time information sharing between physical entities and virtual environments. However, the existing DITEN faces challenges such as high computing requirements and complex network layouts. IRS is a novel type of artificial microstructured material that controls the angle and intensity of signals by manipulating reflecting units. Therefore, we address the limitations of computing capabilities and complex communication environments by incorporating IRS into the construction of DITEN. We get help from IRS to offload training tasks to edge servers at the physical network layer and create digital twin models to capture the real-time state changes of the physical network layer. Furthermore, we formulate a problem aimed at minimizing system latency and propose a federated deep reinforcement learning (DRL) algorithm to jointly optimize the system’s computing resources, offloading coefficients, and reflection link configurations. Experimental results demonstrate the convergence and performance improvements of our proposed algorithm under various scenarios and parameters.


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    Title :

    Intelligent Reflecting Surfaces aided Task Offloading in Digital Twin Edge Networks


    Contributors:
    Dai, Yueyue (author) / Wu, Jian (author) / Zhao, Jintang (author) / Gong, Baichuan (author) / Lu, Yunlong (author)


    Publication date :

    2023-10-10


    Size :

    2353874 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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