In this paper, we investigate a cell-free massive multiple-input multiple-output system, which exhibits great potential in enhancing the capabilities of next-generation mobile communication networks. We first study the distributed positioning problem to lay the groundwork for solving resource allocation and interference management issues. Instead of relying on computationally and spatially complex fingerprint positioning methods, we propose a novel two-stage distributed collaborative positioning architecture with multi-agent reinforcement learning (MARL) network, consisting of a received signal strength-based preliminary positioning network and an angle of arrival-based auxiliary correction network. Our experimental results demonstrate that the two-stage distributed collaborative user positioning architecture can outperform conventional fingerprint positioning methods in terms of positioning accuracy.

翻译：本文研究无蜂窝大规模多输入多输出系统，该系统在提升下一代移动通信网络能力方面展现出巨大潜力。我们首先研究分布式定位问题，为解决资源分配与干扰管理问题奠定基础。区别于依赖计算复杂度高且空间复杂度大的指纹定位方法，我们提出一种基于多智能体强化学习网络的新型两阶段分布式协同定位架构，该架构包含基于接收信号强度的初步定位网络与基于到达角度的辅助校正网络。实验结果表明，所提出的两阶段分布式协同用户定位架构在定位精度方面优于传统指纹定位方法。