Capable of reflecting and refracting the incident signals on both sides simultaneously, the intelligent omnidirectional surface (IOS) has recently been proposed as a promising solution to enhance the capacity of wireless networks. However, the large number of IOS elements brings a heavy burden to the beamforming scheme design, especially for applications that require a fast response to varying environments. In this paper, aiming to maximize the sum rate of an IOS-aided multi-user system via IOS-enabled beamforming design that can rapidly adapt to dynamic channel states and user mobility, we develop a novel meta-critic reinforcement learning framework where a meta-critic network recognizes the environment change and automatically re-trains of the learning model. A stochastic Explore and Reload procedure is tailored to reduce the high-dimensional action space problem. Simulation results show the proposed scheme can converge to a higher sum rate more rapidly compared to the benchmark methods in dynamic settings. The robustness of our scheme against different IOS sizes is also verified.


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    Title :

    Meta-Critic Reinforcement Learning for IOS-Assisted Multi-User Communications in Dynamic Environments


    Contributors:
    Luo, Qinpei (author) / Di, Boya (author) / Han, Zhu (author)


    Publication date :

    2023-06-01


    Size :

    2247443 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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