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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    Titel :

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


    Beteiligte:
    Luo, Qinpei (Autor:in) / Di, Boya (Autor:in) / Han, Zhu (Autor:in)


    Erscheinungsdatum :

    2023-06-01


    Format / Umfang :

    2247443 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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