RIS-Assisted UAV-D2D Communications Exploiting Deep Reinforcement Learning
Sensors（2023）SCI 3区
西北工业大学
Abstract
Device-to-device(D2D)communications underlying cellular networks enabled by unmanned aerial vehicles(UAV)have been re-garded as promising techniques for next-generation communications.To mitigate the strong interference caused by the line-of-sight(LoS)air-to-ground channels,we deploy a reconfigurable intelligent surface(RIS)to rebuild the wireless channels.A joint optimization problem of the transmit power of UAV,the transmit power of D2D users and the RIS phase configuration are investigated to maximize the achievable rate of D2D users while satisfying the quality of service(QoS)requirement of cellular users.Due to the high channel dynamics and the coupling among cellular users,the RIS,and the D2D users,it is challenging to find a proper solution.Thus,a RIS softmax deep double deterministic(RIS-SD3)policy gradient method is proposed,which can smooth the optimization space as well as reduce the number of local optimizations.Specifically,the SD3 algorithm maximizes the reward of the agent by training the agent to maximize the value function after the softmax opera-tor is introduced.Simulation results show that the proposed RIS-SD3 algorithm can significantly improve the rate of the D2D users while con-trolling the interference to the cellular user.Moreover,the proposed RIS-SD3 algorithm has better robustness than the twin delayed deep de-terministic(TD3)policy gradient algorithm in a dynamic environment.
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Key words
device-to-device communications,reconfigurable intelligent surface,deep reinforcement learning,softmax deep double determin-istic policy gradient
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