In this paper, we investigate the amalgamation of cell-free (CF) and extremely large-scale multiple-input multiple-output (XL-MIMO) technologies, referred to as a CF XL-MIMO, as a promising advancement for enabling future mobile networks. To address the computational complexity and communication power consumption associated with conventional centralized optimization, we focus on user-centric dynamic networks in which each user is served by an adaptive subset of access points (AP) rather than all of them. We begin our research by analyzing a joint resource allocation problem for energy-efficient CF XL-MIMO systems, encompassing cooperative clustering and power control design, where all clusters are adaptively adjustable. Then, we propose an innovative double-layer multi-agent reinforcement learning (MARL)-based scheme, which offers an effective strategy to tackle the challenges of high-dimensional signal processing. In the section of numerical results, we compare various algorithms with different network architectures. These comparisons reveal that the proposed MARL-based cooperative architecture can effectively strike a balance between system performance and communication overhead, thereby improving energy efficiency performance. It is important to note that increasing the number of user equipments participating in information sharing can effectively enhance SE performance, which also leads to an increase in power consumption, resulting in a non-trivial trade-off between the number of participants and EE performance.

翻译：本文研究了无小区网络与超大规模多输入多输出技术的融合架构（称为无小区XL-MIMO），该架构作为支撑未来移动网络发展的前瞻性技术而备受关注。为应对传统集中式优化带来的计算复杂度与通信功耗问题，我们聚焦于以用户为中心的动态网络，其中每个用户由自适应选择的接入点子集（而非全部接入点）提供服务。我们首先分析了能效型无小区XL-MIMO系统中的联合资源分配问题，该问题涵盖协作聚类与功率控制设计，且所有聚类簇均具备自适应调整能力。随后，我们提出了一种创新的双层多智能体强化学习方案，该方案为解决高维信号处理挑战提供了有效策略。在数值结果部分，我们比较了不同网络架构下的多种算法。对比结果表明，所提出的基于多智能体强化学习的协作架构能在系统性能与通信开销之间实现有效平衡，从而提升能效性能。需要指出的是，增加参与信息共享的用户设备数量可有效提升频谱效率性能，但这也会导致功耗上升，从而在参与者数量与能效性能之间形成重要的权衡关系。