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Cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO)is regarded as a promising technology for enabling future wirelesscommunication systems. Significant attention has been generated by itsconsiderable advantages in augmenting degrees of freedom. In this paper, wefirst investigate a CF XL-MIMO system with base stations equipped with XL-MIMOpanels under a dynamic environment. Then, we propose an innovative multi-agentreinforcement learning (MARL)-based power control algorithm that incorporatespredictive management and distributed optimization architecture, which providesa dynamic strategy for addressing high-dimension signal processing problems.Specifically, we compare various MARL-based algorithms, which shows that theproposed MARL-based algorithm effectively strikes a balance between spectralefficiency (SE) performance and convergence time. Moreover, we consider adouble-layer power control architecture based on the large-scale fadingcoefficients between antennas to suppress interference within dynamic systems.Compared to the single-layer architecture, the results obtained unveil that theproposed double-layer architecture has a nearly24% SE performance improvement,especially with massive antennas and smaller antenna spacing.

Double-Layer Power Control for Mobile Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning