The intelligent reflecting surface (IRS) is an artificial metasurface making the communication environment smart and controllable. The IRS on an aerial platform (AIRS) expands the wireless network to the three-dimensional space, thus improving the degree of freedom (DoF) for the signal adjustment. Since the AIRS-enabled wireless channel is generally time-variant in practice, herein, this paper considers the time-varying characteristic of the downlink channels, and proposes a complex-valued ResNet-based deep Q-learning (DQN) algorithm to maximize the sum-rate at user equipment (UE) side, by jointly designing the transmit beamforming at base station (BS) side and the reconfigurable phase shifts at AIRS side. Our results reveal that the proposed complex-valued deep reinforcement learning (DRL) approach shows stronger generalization ability in comparison with the real-valued DRL algorithms, and is validated to be able to mitigate the problem of gradient vanishing and improve the performance over the time-varying downlink channels.


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    Title :

    Complex-valued Reinforcement Learning Based Dynamic Beamforming Design for IRS Aided Time-Varying Downlink Channel


    Contributors:
    Liu, Mengfan (author) / Wang, Rui (author) / Xing, Zhe (author) / Yu, Jun (author)


    Publication date :

    2022-06-01


    Size :

    3650623 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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