In this paper, we investigate the uplink transmit power optimization problem in cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Instead of applying the traditional methods, we propose two signal processing architectures: the centralized training and centralized execution with fuzzy logic as well as the centralized training and decentralized execution with fuzzy logic, respectively, which adopt the amalgamation of multi-agent reinforcement learning (MARL) and fuzzy logic to solve the design problem of power control for the maximization of the system spectral efficiency (SE). Furthermore, the uplink performance of the system adopting maximum ratio (MR) combining and local minimum mean-squared error (L-MMSE) combining is evaluated. Our results show that the proposed methods with fuzzy logic outperform the conventional MARL-based method and signal processing methods in terms of computational complexity. Also, the SE performance under MR combining is even better than that of the conventional MARL-based method.

翻译：本文研究了无小区（CF）超大规模多输入多输出（XL-MIMO）系统中的上行发射功率优化问题。不同于传统方法，我们提出了两种信号处理架构：基于模糊逻辑的集中式训练集中式执行架构与基于模糊逻辑的集中式训练分布式执行架构，分别融合了多智能体强化学习（MARL）与模糊逻辑，以解决最大化系统频谱效率（SE）的功率控制设计问题。此外，我们评估了采用最大比（MR）合并和局部最小均方误差（L-MMSE）合并的系统上行性能。结果表明，所提出的基于模糊逻辑的方法在计算复杂度方面优于传统的基于MARL的方法和信号处理方法。同时，采用MR合并时的SE性能甚至优于传统基于MARL的方法。