基于多智能体强化学习的大规模灾后用户分布式覆盖优化

许文俊; 吴思雷; 王凤玉; 林兰; 李国军; 张治

doi:10.11959/j.issn.1000-436x.2022131

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基于多智能体强化学习的大规模灾后用户分布式覆盖优化

学术论文 | 更新时间：2024-06-05

- 基于多智能体强化学习的大规模灾后用户分布式覆盖优化
- Large-scale post-disaster user distributed coverage optimization based on multi-agent reinforcement learning
- 通信学报 2022年43卷第8期页码：1-16
- 作者机构：
  
  1. 北京邮电大学人工智能学院，北京 100876
  2. 重庆邮电大学超视距可信信息传输研究所，重庆 400065
  3. 北京邮电大学信息与通信工程学院，北京 100876
- 作者简介：
  
  [ "许文俊（1982- ），男，安徽安庆人，博士，北京邮电大学教授、博士生导师，主要研究方向为 B5G/6G 智能无线网络、语义智能通信网络、无人机通信及组网、认知无线网络等" ]
  [ "吴思雷（1997- ），男，北京人，北京邮电大学硕士生，主要研究方向为智能无线通信、分布式系统设计、机器学习、强化学习等" ]
  [ "王凤玉（1992- ），女，山西朔州人，博士，北京邮电大学讲师，主要研究方向为无线人工智能、通信感知一体化、智简通信等" ]
  [ "林兰（1996- ），女，河北衡水人，北京邮电大学博士生，主要研究方向为应急通信、NOMA、非凸优化方法、深度强化学习等" ]
  [ "李国军（1978- ），男，四川资阳人，博士，重庆邮电大学教授、博士生导师，主要研究方向为复杂恶劣环境超视距无线通信与网络" ]
  [ "张治（1977- ），男，河北安平人，博士，北京邮电大学副教授、硕士生导师，主要研究方向为移动通信、电子信号处理、通信系统设计等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2019YFC1511302);国家自然科学基金资助项目;The National Natural Science Foundation of China(61871057);国家自然科学基金资助项目;The National Natural Science Foundation of China(61790553);中央高校基本科研业务费专项资金资助项目;The Fundamental Research Funds for the Central Universities(2019XD-A13)
- DOI：10.11959/j.issn.1000-436x.2022131
  中图分类号： TN929.5
- 网络出版日期：2022-08，
  
  纸质出版日期：2022-08-25
- 稿件说明：
移动端阅览
许文俊, 吴思雷, 王凤玉, 等. 基于多智能体强化学习的大规模灾后用户分布式覆盖优化[J]. 通信学报, 2022,43(8):1-16.

Wenjun XU, Silei WU, Fengyu WANG, et al. Large-scale post-disaster user distributed coverage optimization based on multi-agent reinforcement learning[J]. Journal on communications, 2022, 43(8): 1-16.
许文俊, 吴思雷, 王凤玉, 等. 基于多智能体强化学习的大规模灾后用户分布式覆盖优化[J]. 通信学报, 2022,43(8):1-16. DOI： 10.11959/j.issn.1000-436x.2022131.

Wenjun XU, Silei WU, Fengyu WANG, et al. Large-scale post-disaster user distributed coverage optimization based on multi-agent reinforcement learning[J]. Journal on communications, 2022, 43(8): 1-16. DOI： 10.11959/j.issn.1000-436x.2022131.

摘要

为了快速恢复大规模受灾用户的应急通信服务，针对接入用户数量众多导致的业务差异性和动态性显著、集中式算法难以扩展等问题，提出了一种基于多智能体强化学习的分布式智简覆盖优化架构。在网络特征层中，设计了考虑用户业务差异性的分布式 k-sums 分簇算法，每个无人机基站从用户需求出发，原生简约地调整局部网络结构，并筛选簇中心用户特征作为多智能体强化学习神经网络的输入状态。在轨迹调控层中，设计了多智能体最大熵强化学习（MASAC）算法，无人机基站作为智能节点以“分布式训练-分布式执行”的框架调控自身飞行轨迹，并融合集成学习和课程学习技术提升了训练稳定性和收敛速度。仿真结果表明，所提分布式 k-sums 分簇算法在平均负载效率和分簇均衡性方面优于k-means算法，基于MASAC的无人机基站轨迹调控算法能够有效减小通信中断的发生频率、提升网络的频谱效率，效果优于现有的强化学习方法。

Abstract

In order to quickly restore emergency communication services for large-scale post-disaster users

a distributed intellicise coverage optimization architecture based on multi-agent reinforcement learning (RL) was proposed

which could address the significant differences and dynamics of communication services caused by a large number of access users

and the difficulty of expansion caused by centralized algorithms.Specifically

a distributed k-sums clustering algorithm considering service differences of users was designed in the network characterization layer

which could make each unmanned aerial vehicle base station (UAV-BS) adjust the local networking natively and simply

and obtain states of cluster center for multi-agent RL.In the trajectory control layer

multi-agent soft actor critic (MASAC) with distributed-training-distributed-execution structure was designed for UAV-BS to control trajectory as intelligent nodes.Furthermore

ensemble learning and curriculum learning were integrated to improve the stability and convergence speed of training process.The simulation results show that the proposed distributed k-sums algorithm is superior to the k-means in terms of average load efficiency and clustering balance

and MASAC based trajectory control algorithm can effectively reduce communication interruptions and improve the spectrum efficiency

which outperforms the existing RL algorithms.

关键词

Keywords

references

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