Joint Optimization of Distributed Intelligent Content Caching and User Association for CF-mMIMO

LI Yun; ZHANG Cheng-yu; YAO Zhi-xiu; XIA Shi-chao; TAN Zhen

doi:10.12263/DZXB.20240867

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Joint Optimization of Distributed Intelligent Content Caching and User Association for CF-mMIMO

PAPERS | 更新时间：2025-10-16

- Joint Optimization of Distributed Intelligent Content Caching and User Association for CF-mMIMO
- ACTA ELECTRONICA SINICA Vol. 53, Issue 6, Pages: 1932-1942(2025)
- 作者机构：
  
  1.重庆邮电大学通信与信息工程学院，重庆 400065
  2.重庆邮电大学计算机科学与技术学院，重庆 400065
  3.中国电子科技集团公司第十五研究所，北京 100083
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62301099;62071077);China Postdoctoral Science Foundation(2023MD734137)
- DOI：10.12263/DZXB.20240867
  CLC： TN929.5
- Received：20 September 2024，
  
  Revised：2025-05-22，
  
  Published：25 June 2025
- 稿件说明：
移动端阅览
李云, 张承宇, 姚枝秀, 等. 面向无蜂窝大规模MIMO的分布式智能内容缓存与用户关联联合优化方法[J]. 电子学报, 2025, 53(06): 1932-1942.

LI Yun, ZHANG Cheng-yu, YAO Zhi-xiu, et al. Joint Optimization of Distributed Intelligent Content Caching and User Association for CF-mMIMO[J]. Acta Electronica Sinica, 2025, 53(06): 1932-1942.
李云, 张承宇, 姚枝秀, 等. 面向无蜂窝大规模MIMO的分布式智能内容缓存与用户关联联合优化方法[J]. 电子学报, 2025, 53(06): 1932-1942. DOI：10.12263/DZXB.20240867

LI Yun, ZHANG Cheng-yu, YAO Zhi-xiu, et al. Joint Optimization of Distributed Intelligent Content Caching and User Association for CF-mMIMO[J]. Acta Electronica Sinica, 2025, 53(06): 1932-1942. DOI：10.12263/DZXB.20240867

摘要

在无蜂窝大规模MIMO（Cell-Free massive Multiple-Input Multiple-Output， CF-mMIMO）网络环境中，业务需求差异化、环境高度动态化以及资源部署去中心化等特征，制约了CF-mMIMO缓存部署和分发过程中多维网络资源的分配效率.为此，本文对去中心化CF-mMIMO场景中的多样化内容缓存和多用户关联问题展开研究.首先，基于CF-mMIMO场景中内容缓存与用户关联间的耦合关系，研究并建立了内容缓存、用户关联和多维资源分配模型.其次，针对随机时变的网络环境和不完备的网络状态观测，以最大化网络能效为目标，将内容缓存、用户关联和资源分配问题抽象为分布式部分可观测马尔科夫决策过程.而考虑到多样化内容缓存需求和广域差异化网络空间特征，进一步提出一种基于图注意力网络的多智能体深度强化学习算法对内容缓存、用户关联和多维资源分配进行策略学习和优化.最后，仿真结果验证了所提算法在网络能效、系统吞吐量、缓存命中率方面具有明显的性能提升.

Abstract

In the cell-free massive MIMO (CF-mMIMO) networks

characterized by differentiated service requirements

highly dynamic conditions

and decentralized resource deployment

the efficiency of distributing multi-dimensional network resources during CF-mMIMO caching deployment is constrained. To address this

this paper conducts research on the problem of diverse content caching and multi-user association in decentralized CF-mMIMO scenarios. First

based on the coupling relationship between content caching and user association

models for content caching

user association

and multi-dimensional resource allocation are studied and established. Second

given the stochastic and time-varying network environment and incomplete network state observations

the content caching

user association

and resource allocation problem are abstracted as a distributed partially observable Markov decision process (POMDP) with the objective of maximizing network efficiency. Taking into account the diverse content caching requirements and wide spatial differentiation

a multi-agent deep reinforcement learning algorithm based on graph attention network is further proposed for strategic learning and optimization of content caching

user association

and multi-dimensional resource allocation. Finally

simulation results confirm that the proposed algorithm significantly enhances performance in terms of network efficiency

system throughput and cache hit rate.

关键词

Keywords

references

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