Space-air-ground integrated networks:review and prospect
With the advance of the information technologies,the scale of the information services gradually expands,from ground services,to aerial,maritime,and spatial services,with the soaring requirements on multi-dimensional comprehensive information resources.The space-air-ground integrated networks (SAGIN...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2020-09-01
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| Series: | 物联网学报 |
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| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2020.00142/ |
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| _version_ | 1841531154415484928 |
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| author | Xuemin(Sherman) SHEN Nan CHENG Haibo ZHOU Feng LYU Wei QUAN Weisen SHI Huaqing WU Conghao ZHOU |
| author_facet | Xuemin(Sherman) SHEN Nan CHENG Haibo ZHOU Feng LYU Wei QUAN Weisen SHI Huaqing WU Conghao ZHOU |
| author_sort | Xuemin(Sherman) SHEN |
| collection | DOAJ |
| description | With the advance of the information technologies,the scale of the information services gradually expands,from ground services,to aerial,maritime,and spatial services,with the soaring requirements on multi-dimensional comprehensive information resources.The space-air-ground integrated networks (SAGINs) are envisioned to provide seamless network services to spatial,aerial,maritime,and ground users,satisfying the future network requirements on all-time,all-domain,and all-space communications and interconnected networking.Firstly,we reviewed the current research development of SAGINs,discussing the research trends on the low-earth orbiting (LEO) satellite constellation and space-ground network integration.Then,the reinforcement learning (RL) framework was proposed in SAGINs to address the problems of complex architecture,high dynamics,and resource constraints in SAGINs,which facilitated efficient and fast network design,analysis,optimization,and management.As a case study,the method of applying deep RL (DRL) was showed for the intelligent access network selection in SAGINs.To improve the RL training efficiency,a comprehensive SAGINs simulation platform was established,through which the agent-environments interaction was accelerated and training samples could be obtained more cost-effectively.Finally,some open research directions were presented. |
| format | Article |
| id | doaj-art-3f2fc813616a4fbc9578bc513290152d |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2020-09-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-3f2fc813616a4fbc9578bc513290152d2025-01-15T02:52:54ZzhoChina InfoCom Media Group物联网学报2096-37502020-09-01431959645900Space-air-ground integrated networks:review and prospectXuemin(Sherman) SHENNan CHENGHaibo ZHOUFeng LYUWei QUANWeisen SHIHuaqing WUConghao ZHOUWith the advance of the information technologies,the scale of the information services gradually expands,from ground services,to aerial,maritime,and spatial services,with the soaring requirements on multi-dimensional comprehensive information resources.The space-air-ground integrated networks (SAGINs) are envisioned to provide seamless network services to spatial,aerial,maritime,and ground users,satisfying the future network requirements on all-time,all-domain,and all-space communications and interconnected networking.Firstly,we reviewed the current research development of SAGINs,discussing the research trends on the low-earth orbiting (LEO) satellite constellation and space-ground network integration.Then,the reinforcement learning (RL) framework was proposed in SAGINs to address the problems of complex architecture,high dynamics,and resource constraints in SAGINs,which facilitated efficient and fast network design,analysis,optimization,and management.As a case study,the method of applying deep RL (DRL) was showed for the intelligent access network selection in SAGINs.To improve the RL training efficiency,a comprehensive SAGINs simulation platform was established,through which the agent-environments interaction was accelerated and training samples could be obtained more cost-effectively.Finally,some open research directions were presented.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2020.00142/space-air-ground integrated networkreinforcement learningLEO constellationsimulation platformInternet of vehicles |
| spellingShingle | Xuemin(Sherman) SHEN Nan CHENG Haibo ZHOU Feng LYU Wei QUAN Weisen SHI Huaqing WU Conghao ZHOU Space-air-ground integrated networks:review and prospect 物联网学报 space-air-ground integrated network reinforcement learning LEO constellation simulation platform Internet of vehicles |
| title | Space-air-ground integrated networks:review and prospect |
| title_full | Space-air-ground integrated networks:review and prospect |
| title_fullStr | Space-air-ground integrated networks:review and prospect |
| title_full_unstemmed | Space-air-ground integrated networks:review and prospect |
| title_short | Space-air-ground integrated networks:review and prospect |
| title_sort | space air ground integrated networks review and prospect |
| topic | space-air-ground integrated network reinforcement learning LEO constellation simulation platform Internet of vehicles |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2020.00142/ |
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