Handover algorithm for space-air-ground integrated network based on location prediction model
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-12-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024266/ |
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| _version_ | 1832595471181283328 |
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| author | XIE Jianli CHEN Long ZHANG Zepeng LI Cuiran |
| author_facet | XIE Jianli CHEN Long ZHANG Zepeng LI Cuiran |
| author_sort | XIE Jianli |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6768dac46d044a0fb6f9109c2dbf1718 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-6768dac46d044a0fb6f9109c2dbf17182025-01-18T19:00:06ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-12-014516217880268704Handover algorithm for space-air-ground integrated network based on location prediction modelXIE JianliCHEN LongZHANG ZepengLI CuiranTo 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024266/space-air-ground integrated networknetwork handoverutility functionLSTMdistributional DQN |
| spellingShingle | XIE Jianli CHEN Long ZHANG Zepeng LI Cuiran Handover algorithm for space-air-ground integrated network based on location prediction model Tongxin xuebao space-air-ground integrated network network handover utility function LSTM distributional DQN |
| title | Handover algorithm for space-air-ground integrated network based on location prediction model |
| title_full | Handover algorithm for space-air-ground integrated network based on location prediction model |
| title_fullStr | Handover algorithm for space-air-ground integrated network based on location prediction model |
| title_full_unstemmed | Handover algorithm for space-air-ground integrated network based on location prediction model |
| title_short | Handover algorithm for space-air-ground integrated network based on location prediction model |
| title_sort | handover algorithm for space air ground integrated network based on location prediction model |
| topic | space-air-ground integrated network network handover utility function LSTM distributional DQN |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024266/ |
| work_keys_str_mv | AT xiejianli handoveralgorithmforspaceairgroundintegratednetworkbasedonlocationpredictionmodel AT chenlong handoveralgorithmforspaceairgroundintegratednetworkbasedonlocationpredictionmodel AT zhangzepeng handoveralgorithmforspaceairgroundintegratednetworkbasedonlocationpredictionmodel AT licuiran handoveralgorithmforspaceairgroundintegratednetworkbasedonlocationpredictionmodel |