AI-enabled Resource Allocation with PHY-Layer Twin for RIS-Aided Cell-Free Wireless Systems

In this paper, we design a physical (PHY)-layer digital twin model, called PHY-Layer twin by leveraging ray-tracing (RT) and deep learning (DL) for a reconfigurable intelligent surface (RIS) assisted cell-free wireless communication system. Specifically, a recurrent neural network (RNN) driven low-complexity resource allocation scheme is studied to optimize system resources by interfacing with the proposed PHY-Layer twin. The proposed PHY-Layer twin-assisted resource allocation scheme jointly learns the correlation between channel gains obtained from RT and the real-world environment, and it allocates optimal transmit power for access points (APs) of the cell-free wireless networks. Numerical results demonstrate that the proposed scheme outperforms the related state-of-the-art approaches while operating with lower computational overhead.