  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.
