Distributed Collaborative User Positioning for Cell-Free Massive MIMO with Multi-Agent Reinforcement Learning
<|reference_start|>Distributed Collaborative User Positioning for Cell-Free Massive MIMO with Multi-Agent Reinforcement Learning: 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.<|reference_end|>
@article{liu2024distributed,
    title={Distributed Collaborative User Positioning for Cell-Free Massive MIMO
  with Multi-Agent Reinforcement Learning},
    author={Ziheng Liu, Jiayi Zhang, Enyu Shi, Yiyang Zhu, Derrick Wing Kwan Ng,
  and Bo Ai},
    journal={arXiv preprint arXiv:2410.04871},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04871},
    primaryClass={cs.IT eess.SP math.IT}
}