基于位置预测模型的空天地一体化网络切换算法

谢健骊; 陈龙; 张泽鹏; 李翠然

doi:10.11959/j.issn.1000-436x.2024266

您当前的位置：

首页 >

文章列表页 >

基于位置预测模型的空天地一体化网络切换算法

学术论文 | 更新时间：2025-01-14

- 基于位置预测模型的空天地一体化网络切换算法
- Handover algorithm for space-air-ground integrated network based on location prediction model
- 通信学报 2024年45卷第12期页码：162-178
- 作者机构：
  
  兰州交通大学电子与信息工程学院，甘肃兰州 730070
- 作者简介：
  
  [ "谢健骊（1972- ），男，甘肃陇西人，博士，兰州交通大学教授、博士生导师，主要研究方向为高铁智能通信、认知无线电、铁路物联网技术等。" ]
  [ "陈龙（2001- ），男，甘肃天水人，兰州交通大学硕士生，主要研究方向为空天地一体化网络移动性管理、网络接入选择。" ]
  [ "张泽鹏（1998- ），男，甘肃白银人，兰州交通大学博士生，主要研究方向为智能超表面、轨道交通通信、智能隐蔽通信。" ]
  [ "李翠然（1975- ），女，山西黎城人，博士，兰州交通大学教授、博士生导师，主要研究方向为高速铁路智能无线通信、无线传感器网络、协同通信技术等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62161016)
- DOI：10.11959/j.issn.1000-436x.2024266
  中图分类号： TN929.5
- 收稿：2024-07-31，
  
  修回：2024-12-05，
  
  纸质出版：2024-12-25
- 稿件说明：
移动端阅览
谢健骊,陈龙,张泽鹏等.基于位置预测模型的空天地一体化网络切换算法[J].通信学报,2024,45(12):162-178.

XIE Jianli,CHEN Long,ZHANG Zepeng,et al.Handover algorithm for space-air-ground integrated network based on location prediction model[J].Journal on Communications,2024,45(12):162-178.
谢健骊,陈龙,张泽鹏等.基于位置预测模型的空天地一体化网络切换算法[J].通信学报,2024,45(12):162-178. DOI： 10.11959/j.issn.1000-436x.2024266.

XIE Jianli,CHEN Long,ZHANG Zepeng,et al.Handover algorithm for space-air-ground integrated network based on location prediction model[J].Journal on Communications,2024,45(12):162-178. DOI： 10.11959/j.issn.1000-436x.2024266.

摘要

针对6G空天地一体化网络（SAGIN）中网络环境动态变化和用户终端移动性增强导致的终端切换频繁、网络负载不均衡问题，提出了一种基于终端位置预测模型的SAGIN切换算法。该算法构建了基于麻雀搜索策略优化的长短期记忆（LSTM）网络终端位置预测模型，提升了终端位置预测精度，解决了网络切换时机不合理问题。基于此模型，将SAGIN选择问题建模为马尔可夫决策过程，设计以服务质量（QoS）需求、切换代价和网络负载均衡表征的网络切换算法效用函数，采用分布式深度Q网络（D-DQN）选择能够实现长期目标最大化的网络节点执行切换。与基于Q学习（Q-Learning）、双深度Q网络（DDQN）和竞争双深度Q网络（D3QN）的网络切换算法相比，所提算法在降低切换时延与切换次数、提升网络吞吐量等方面性能较优，验证了所提算法的有效性。

Abstract

To address the issues of frequent handovers and network load imbalance caused by dynamic changes in the network environment and enhanced mobility of user terminals in the 6G space-air-ground integrated network (SAGIN)

a handover algorithm for SAGIN based on a terminal location prediction model was proposed. The algorithm constructed a long short-term memory (LSTM) network terminal location prediction model optimized based on the sparrow search strategy

improving the accuracy of terminal location prediction and resolving the issue of unreasonable handover timing. Based on this model

the SAGIN selection problem was modeled as a Markov decision process. A network handover algorithm utility function characterized by quality of service (QoS) requirements

handover cost

and network load balancing was designed. A distributional deep Q-network (D-DQN) was employed to select the network nodes that could maximize long-term goals for execution handover. Compared with network handover algorithms based on Q-Learning

double deep Q-network (DDQN)

and dueling double deep Q-network (D3QN)

the proposed algorithm performs better in terms of reducing handover delay and frequency

as well as enhancing network throughput

thereby validating the effectiveness of the proposed algorithm.

关键词

Keywords

references

BARAKABITZE A A , WALSHE R . SDN and NFV for QoE-driven multimedia services delivery: the road towards 6G and beyond networks [J ] . Computer Networks , 2022 , 214 : 109133 .

FU I K , CHARBIT G , MEDLES A , et al . Satellite and terrestrial network convergence on the way toward 6G [J ] . IEEE Wireless Communications , 2023 , 30 ( 1 ): 6 - 8 .

WANG C X , YOU X H , GAO X Q , et al . On the road to 6G: visions, requirements, key technologies, and testbeds [J ] . IEEE Communications Surveys & Tutorials , 2023 , 25 ( 2 ): 905 - 974 .

RAY P P . A review on 6G for space-air-ground integrated network: key enablers, open challenges, and future direction [J ] . Journal of King Saud University-Computer and Information Sciences , 2022 , 34 ( 9 ): 6949 - 6976 .

张海君 , 陈安琪 , 李亚博 , 等 . 6G移动网络关键技术 [J ] . 通信学报 , 2022 , 43 ( 7 ): 189 - 202 .

ZHANG H J , CHEN A Q , LI Y B , et al . Key technologies of 6G mobile network [J ] . Journal on Communications , 2022 , 43 ( 7 ): 189 - 202 .

ZHU X M , JIANG C X . Integrated satellite-terrestrial networks toward 6G: architectures, applications, and challenges [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 1 ): 437 - 461 .

SHENG J , CAI X Q , LI Q Y , et al . Space-air-ground integrated network development and applications in high-speed railways: a survey [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 8 ): 10066 - 10085 .

钱志鸿 , 冯一诺 , 孙佳妮 , 等 . 基于DA优化模糊神经网络的异构无线网络接入选择算法 [J ] . 通信学报 , 2020 , 41 ( 12 ): 118 - 127 .

QIAN Z H , FENG Y N , SUN J N , et al . Access selection algorithm for heterogeneous wireless network based on DA optimized fuzzy neural network [J ] . Journal on Communications , 2020 , 41 ( 12 ): 118 - 127 .

GUO C , GONG C , XU H T , et al . A dynamic handover software-defined transmission control scheme in space-air-ground integrated networks [J ] . IEEE Transactions on Wireless Communications , 2022 , 21 ( 8 ): 6110 - 6124 .

胡欣 , 宋航宇 , 刘帅军 , 等 . 基于时间演进图的LEO星间切换实时预测及更新方法 [J ] . 通信学报 , 2018 , 39 ( 10 ): 43 - 51 .

HU X , SONG H Y , LIU S J , et al . Real-time prediction and updating method for LEO satellite handover based on time evolving graph [J ] . Journal on Communications , 2018 , 39 ( 10 ): 43 - 51 .

ZHOU Y T , MENG R , XIONG H C , et al . Evolutionary game-based vertical handover strategy for space-air-ground integrated network [C ] // Proceedings of the 2023 IEEE Wireless Communications and Networking Conference (WCNC) . Piscataway : IEEE Press , 2023 : 1 - 6 .

FAN K X , FENG B W , ZHANG X L , et al . Network selection based on evolutionary game and deep reinforcement learning in space-air-ground integrated network [J ] . IEEE Transactions on Network Science and Engineering , 2022 , 9 ( 3 ): 1802 - 1812 .

杨喆 , 邓立宝 , 狄原竹 , 等 . 超密集异构无线网络中基于移动轨迹预测的网络切换算法 [J ] . 电子与信息学报 , 2023 , 45 ( 12 ): 4280 - 4291 .

YANG Z , DENG L B , DI Y Z , et al . Network switching algorithm based on mobile trajectory prediction in ultra-dense heterogeneous wireless networks [J ] . Journal of Electronics & Information Technology , 2023 , 45 ( 12 ): 4280 - 4291 .

马彬 , 王双双 , 陈海波 . 基于区间二型模糊神经网络的垂直切换算法 [J ] . 电子学报 , 2021 , 49 ( 5 ): 928 - 935 .

MA B , WANG S S , CHEN H B . Vertical handover algorithm based on interval type-2 fuzzy neural network [J ] . Acta Electronica Sinica , 2021 , 49 ( 5 ): 928 - 935 .

XU H H , LI D S , LIU M L , et al . QoE-driven intelligent handover for user-centric mobile satellite networks [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 9 ): 10127 - 10139 .

WANG F , JIANG D D , WANG Z H , et al . Seamless handover in LEO based non-terrestrial networks: service continuity and optimization [J ] . IEEE Transactions on Communications , 2023 , 71 ( 2 ): 1008 - 1023 .

ZHU A Q , GUO S T , LIU B , et al . Adaptive multiservice heterogeneous network selection scheme in mobile edge computing [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 4 ): 6862 - 6875 .

SUN J N , QIAN Z H , WANG X , et al . ES-DQN-based vertical handoff algorithm for heterogeneous wireless networks [J ] . IEEE Wireless Communications Letters , 2020 , 9 ( 8 ): 1327 - 1330 .

TAVANA M , SOROOSHIAN S , REZAEI H , et al . A novel fuzzy scenario-based stochastic general best-worst method [J ] . Expert Systems with Applications , 2024 , 252 : 124246 .

HONARVAR R , ZOLGHADRASLI A , MONEMI M . Context-oriented performance evaluation of network selection algorithms in 5G heterogeneous networks [J ] . Journal of Network and Computer Applications , 2022 , 202 : 103358 .

XIE J L , ZHU B H , LI C R . Research of 5G HUDN network selection algorithm based on dueling-DDQN [J ] . EURASIP Journal on Wireless Communications and Networking , 2023 , 2023 ( 1 ): 113 .

唐宏 , 刘小洁 , 甘陈敏 , 等 . 超密集网络中基于改进DQN的接入选择算法 [J ] . 哈尔滨工业大学学报 , 2023 , 55 ( 5 ): 107 - 113 .

TANG H , LIU X J , GAN C M , et al . Access selection algorithm based on improved DQN for ultra-dense networks [J ] . Journal of Harbin Institute of Technology , 2023 , 55 ( 5 ): 107 - 113 .

XUE J K , SHEN B . A novel swarm intelligence optimization approach: sparrow search algorithm [J ] . Systems Science & Control Engineering , 2020 , 8 ( 1 ): 22 - 34 .

XU J P , AI B . Deep reinforcement learning for handover-aware MPTCP congestion control in space-ground integrated network of railways [J ] . IEEE Wireless Communications , 2021 , 28 ( 6 ): 200 - 207 .

LI R X , SHANG Z W , ZHENG C H , et al . Efficient distributional reinforcement learning with Kullback-Leibler divergence regularization [J ] . Applied Intelligence , 2023 , 53 ( 21 ): 24847 - 24863 .

UPPOOR S , TRULLOLS-CRUCES O , FIORE M , et al . Generation and analysis of a large-scale urban vehicular mobility dataset [J ] . IEEE Transactions on Mobile Computing , 2014 , 13 ( 5 ): 1061 - 1075 .

WU D F , HUANG C H , YIN Y B , et al . LB-DDQN for handover decision in satellite-terrestrial integrated networks [J ] . Wireless Communications and Mobile Computing , 2021 , 2021 ( 1 ): 5871114 .

浏览量

1401

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于网络演算的LEO卫星网络时延上界分析研究

空天地一体化网络中基于联邦深度强化学习的边缘协作缓存策略

基于深度强化学习的空天地一体化网络信息物理系统垂直切换策略

基于信道建模的多层星地融合数据分配策略

基于细粒度切片的6G网络弹性切换算法研究