基于多智能体强化学习的可移动基站智能规划与优化

赵欣然; 陈美娟; 袁志伟; 朱晓荣

doi:10.11959/j.issn.1000-0801.2025035

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基于多智能体强化学习的可移动基站智能规划与优化

研究与开发 | 更新时间：2025-03-11

- 基于多智能体强化学习的可移动基站智能规划与优化
- Intelligent deployment and optimization of movable base stations based on multi-agent reinforcement learning
- 电信科学 2025年41卷第2期页码：68-83
- 作者机构：
  
  南京邮电大学通信与信息工程学院，江苏南京 210003
- 作者简介：
  
  [ "赵欣然（1999- ），男，南京邮电大学通信与信息工程学院硕士生，主要研究方向为无线网络规划优化、网络故障诊断等。" ]
  [ "陈美娟（1971- ），女，博士，南京邮电大学通信与信息工程学院副教授，主要研究方向为移动通信网络资源分配优化算法。" ]
  [ "袁志伟（2000- ），男，南京邮电大学通信与信息工程学院硕士生，主要研究方向为联邦学习、区块链等。" ]
  [ "朱晓荣（1977- ），女，博士，南京邮电大学通信与信息工程学院教授、博士生导师，主要研究方向为5G/6G网络、智能物联网、网络大数据、区块链、群体智能等。" ]
- 基金信息：
  
  江苏省重点研发计划项目(BE2021013-2)
- DOI：10.11959/j.issn.1000-0801.2025035
  中图分类号： TN925
- 收稿日期：2024-10-03，
  
  修回日期：2025-01-27，
  
  纸质出版日期：2025-02-20
- 稿件说明：
移动端阅览
赵欣然,陈美娟,袁志伟等.基于多智能体强化学习的可移动基站智能规划与优化[J].电信科学,2025,41(02):68-83.

ZHAO Xinran,CHEN Meijuan,YUAN Zhiwei,et al.Intelligent deployment and optimization of movable base stations based on multi-agent reinforcement learning[J].Telecommunications Science,2025,41(02):68-83.
赵欣然,陈美娟,袁志伟等.基于多智能体强化学习的可移动基站智能规划与优化[J].电信科学,2025,41(02):68-83. DOI： 10.11959/j.issn.1000-0801.2025035.

ZHAO Xinran,CHEN Meijuan,YUAN Zhiwei,et al.Intelligent deployment and optimization of movable base stations based on multi-agent reinforcement learning[J].Telecommunications Science,2025,41(02):68-83. DOI： 10.11959/j.issn.1000-0801.2025035.

摘要

为了在城市环境中快速部署可移动基站并实现运维优化，针对终端用户移动带来的网络覆盖率下降问题与密集部署基站带来的干扰问题，提出了一种基于多智能体强化学习的网络覆盖规划与优化方法。在部署阶段，使用粒子群与果蝇混合优化算法，在建站成本最小化的情况下确定基站最优站址；在运维阶段，设计了多智能体深度确定性策略梯度算法与轻量级梯度提升机算法的联合优化算法，根据终端接收信号强度优化站址，在性能指标仍无法达到要求时，能自动在合适位置新增基站。仿真结果表明，所提出的站址规划算法在覆盖率与服务率方面均优于传统启发式算法；所设计的联合运维优化算法在网络覆盖率恢复能力方面优于传统

均值（

-means）聚类算法，并且能适应更多场景。

Abstract

To enable the rapid deployment of mobile base stations and optimize operations in urban environments

a network coverage planning and optimization method based on multi-agent reinforcement learning was proposed. This method was designed to address the issue of reducing network coverage due to user mobility and the interference caused by densely deployed base stations. During the deployment phase

a hybrid optimization algorithm combining parti

cle swarm and fruit fly optimization was employed to determine the optimal base station locations while minimizing construction costs. In the operational phase

a joint optimization algorithm featuring multi-agent deep deterministic policy gradient and lightweight gradient boosting algorithms was designed to optimize base station locations based on terminal signal strength. Additionally

when performance indicators failed to meet requirements

new base stations were automatically added in suitable locations. Simulation results demonstrate that the proposed algorithm outperforms traditional heuristic algorithms in terms of coverage and service rates

while the designed joint operational optimization algorithm shows superior recovery capability in network coverage compared to the traditional

-means clustering algorithm

adapting to a wider range of scenarios.

关键词

Keywords

references

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