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arXiv 2406.05481cs.ITmath.IT
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基于多智能体强化学习的节能无蜂窝XL-MIMO联合协作分簇与功率控制

Joint Cooperative Clustering and Power Control for Energy-Efficient Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning

Ziheng Liu, Jiayi Zhang, Zhilong Liu, Derrick Wing Kwan Ng, Bo Ai

AI总结:

针对节能CF XL-MIMO,提出双层MARL方案联合优化自适应协作分簇与功率控制,在性能、开销和能效间实现有效平衡。

摘要:

本文研究无蜂窝(CF)与超大规模多输入多输出(XL-MIMO)技术的融合,称为CF XL-MIMO,将其作为实现未来移动网络的一项有前景的进展。为解决传统集中式优化所伴随的计算复杂度和通信功耗问题,我们聚焦于以用户为中心的动态网络,其中每个用户由接入点(AP)的自适应子集而非全部AP提供服务。我们的研究首先分析节能CF XL-MIMO系统的联合资源分配问题,涵盖协作分簇和功率控制设计,其中所有簇均可自适应调整。随后,我们提出一种新颖的基于双层多智能体强化学习(MARL)的方案,为应对高维信号处理挑战提供有效策略。在数值结果部分,我们比较了具有不同网络架构的多种算法。这些比较表明,所提出的基于MARL的协作架构能够有效平衡系统性能与通信开销,从而提升能效性能。需要注意的是,增加参与信息共享的用户设备数量可有效提升SE性能,但这也会导致功耗增加,从而在参与者数量与EE性能之间产生不可忽视的折中关系。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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