Handover algorithm for space-air-ground integrated network based on location prediction model

XIE Jianli; CHEN Long; ZHANG Zepeng; LI Cuiran

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

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Handover algorithm for space-air-ground integrated network based on location prediction model

Papers | 更新时间：2025-01-14

- Handover algorithm for space-air-ground integrated network based on location prediction model
- Journal on Communications Vol. 45, Issue 12, Pages: 162-178(2024)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(62161016)
- DOI：10.11959/j.issn.1000-436x.2024266
  CLC： TN929.5
- Received：31 July 2024，
  
  Revised：2024-12-05，
  
  Published：25 December 2024
- 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

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