基于分层联邦学习的无人机小基站RAN切片方法

殷珉; 沈航; 王天荆; 白光伟

doi:10.12263/DZXB.20221083

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基于分层联邦学习的无人机小基站RAN切片方法

学术论文 | 更新时间：2025-12-08

- 基于分层联邦学习的无人机小基站RAN切片方法
- Hierarchical Federated Learning-Based RAN Slicing for Drone-Small-Cells
- 电子学报 2023年51卷第7期页码：1774-1780
- 作者机构：
  
  南京工业大学计算机与信息工程学院，江苏南京 210000
- 作者简介：
  
  [ "殷珉男，1998年10月出生于江苏省盐城市.现为南京工业大学计算机与信息工程学院硕士研究生.主要研究方向为无人机通信网络、分布式机器学习.E-mail： 202061220036@njtech.edu.cn" ]
  [ "沈航（通讯作者）男，1984年3月出生于江苏省南京市.现为南京工业大学计算机与信息工程学院副教授.主要研究方向为空天地一体化网络.E-mail： hshen@njtech.edu.cn" ]
  [ "王天荆　女，1977年7月出生于江苏省南通市.现为南京工业大学计算机与信息工程学院副教授.主要研究方向为无线网络、分布式机器学习.E-mail： wangtianjing@njtech.edu.cn" ]
  [ "白光伟　男，1961年11月出生于辽宁省沈阳市.现为南京工业大学计算机与信息工程学院教授.主要研究方向为移动互联网.E-mail： bai@njtech.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61502230;61501224);江苏省自然科学基金(BK20201357);江苏省六大人才高峰高层次人才资助项目(RJFW-020)
- DOI：10.12263/DZXB.20221083
  中图分类号： TP393
- 收稿：2022-09-22，
  
  修回：2023-04-28，
  
  纸质出版：2023-07-25
- 稿件说明：
移动端阅览
殷珉,沈航,王天荆等.基于分层联邦学习的无人机小基站RAN切片方法[J].电子学报,2023,51(07):1774-1780.

YIN Min,SHEN Hang,WANG Tian-jing,et al.Hierarchical Federated Learning-Based RAN Slicing for Drone-Small-Cells[J].ACTA ELECTRONICA SINICA,2023,51(07):1774-1780.
殷珉,沈航,王天荆等.基于分层联邦学习的无人机小基站RAN切片方法[J].电子学报,2023,51(07):1774-1780. DOI： 10.12263/DZXB.20221083.

YIN Min,SHEN Hang,WANG Tian-jing,et al.Hierarchical Federated Learning-Based RAN Slicing for Drone-Small-Cells[J].ACTA ELECTRONICA SINICA,2023,51(07):1774-1780. DOI： 10.12263/DZXB.20221083.

摘要

针对多架无人机共同为地面用户提供差异化服务的场景，本文提出一种基于分层联邦学习的动态RAN（Radio Access Network）切片框架，目的是提升切片性能隔离效果、减少协同训练过程的通信代价.考虑到无人机动态部署和数据不足等特点，本文通过数据增广促进本地模型训练.为了使得距离地面基站较远的无人机有更多机会参与联邦学习并降低通信代价，本文根据位置和数据分布信息设计支持边缘模型聚合的无人机分簇策略.在此基础上，本文探索基于注意力机制的边缘和全局模型聚合方案，以增强全局模型的泛化能力.仿真结果表明，与联邦平均和分布式LSTM（Long Short-Term Memory）相比，所提方案在切片性能隔离的时长占比上分别有8.4%和16.5%的提升，并降低了无人机协同训练的通信代价.

Abstract

For the scenario where multiple drone-small-cell provide differentiated services for ground users

a dynamic radio access network (RAN) slicing framework based on hierarchical federated learning is proposed. The goal is to improve slice performance isolation and reduce the communication cost in collaborative model training. Data augmentation is introduced to promote local model training and improve model performance

considering the dynamic deployment and insufficient data of drones. Then

a clustering strategy for drone-small-cell depending on geographic location and data distribution is designed to support edge model aggregation. Hence

member drones at the edge have more opportunities to participate in federated learning with reduced communication costs. On this basis

an attention mechanism-based aggregation scheme for edge and global models is explored to improve the generalization ability of the global model. Simulation results show that compared with federated averaging and distributed long short-term memory (LSTM)

the duration of slice performance isolation of the proposed scheme increases by 8.4% and 16.5%

respectively

with reduced communication cost of drone collaborative model training.

关键词

Keywords

references

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