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

翻译：无蜂窝（CF）超大规模多输入多输出（XL-MIMO）被视为未来无线通信系统的关键技术，其在提升自由度方面的显著优势引起了广泛关注。本文首先研究了基站在动态环境下配备超大规模MIMO面板的CF XL-MIMO系统，随后提出了一种创新的基于多智能体强化学习（MARL）的功率控制算法，该算法融合了预测管理与分布式优化架构，为处理高维信号处理问题提供了动态策略。具体而言，我们比较了多种基于MARL的算法，结果表明所提出的MARL算法能够有效平衡频谱效率（SE）性能与收敛时间。此外，我们基于天线间的大尺度衰落系数设计了一种双层功率控制架构，以抑制动态系统中的干扰。与单层架构相比，结果表明所提出的双层架构在频谱效率性能上实现了近24%的提升，尤其是在天线数量庞大且天线间距较小时更为显著。