Joint Cooperative Clustering and Power Control for Energy-Efficient Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning
<|reference_start|>Joint Cooperative Clustering and Power Control for Energy-Efficient Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning: 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.<|reference_end|>
@article{liu2024joint,
    title={Joint Cooperative Clustering and Power Control for Energy-Efficient
  Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning},
    author={Ziheng Liu, Jiayi Zhang, Zhilong Liu, Derrick Wing Kwan Ng, and Bo Ai},
    journal={arXiv preprint arXiv:2406.05481},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05481},
    primaryClass={cs.IT math.IT}
}