Space-air-ground integrated networks:review and prospect

Xuemin(Sherman) SHEN; Nan CHENG; Haibo ZHOU; Feng LYU; Wei QUAN; Weisen SHI; Huaqing WU; Conghao ZHOU

doi:10.11959/j.issn.2096-3750.2020.00142

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Space-air-ground integrated networks:review and prospect

Topic:IoT in Intelligent Transportation | 更新时间：2024-06-05

- Space-air-ground integrated networks:review and prospect
- Chinese Journal on Internet of Things Vol. 4, Issue 3, Pages: 3-19(2020)
- 作者机构：
  
  1. 滑铁卢大学，加拿大滑铁卢 N2L 3G1
  2. 西安电子科技大学，陕西西安 710071
  3. 南京大学，江苏南京 210023
  4. 中南大学，湖南长沙 410083
  5. 北京交通大学，北京 100044
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2020.00142
  CLC： TN92
- Published：30 September 2020，
  
  Published Online：2020-09，
- 稿件说明：
移动端阅览
XUEMIN(SHERMAN) SHEN, NAN CHENG, HAIBO ZHOU, et al. Space-air-ground integrated networks:review and prospect. [J]. Chinese journal on internet of things, 2020, 4(3): 3-19.
DOI：

XUEMIN(SHERMAN) SHEN, NAN CHENG, HAIBO ZHOU, et al. Space-air-ground integrated networks:review and prospect. [J]. Chinese journal on internet of things, 2020, 4(3): 3-19. DOI： 10.11959/j.issn.2096-3750.2020.00142.

摘要

随着信息技术的不断发展，信息服务的空间范畴不断扩大，各种天基、空基、海基、地基网络服务不断涌现，对多维综合信息资源的需求也逐步提升。空天地一体化网络可以为陆海空天用户提供无缝信息服务，满足未来网络对全时全域全空通信和网络互联互通的需求。首先，对空天地一体化网络技术及协议体系的发展趋势进行了分析，探讨了低轨卫星通信系统以及空地网络融合的研究进展。针对网络结构复杂、动态性高、资源高度约束等问题，提出了基于强化学习（RL

reinforcement learning）的空天地一体化网络设计与优化框架，以进行高效快速的网络设计、分析、优化与管控。同时给出了实例分析，阐明了利用深度强化学习（DRL

deep RL）进行空天地一体化网络智能接入选择的方法。并通过搭建空天地一体化网络仿真平台，解决了网络观测稀疏与训练数据难以获取的问题，极大地提升了RL的训练效率。最后，对空天地一体化网络中的潜在研究方向进行了探讨。

Abstract

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.

关键词

空天地一体化网络强化学习低轨卫星星座仿真平台车联网

Keywords

space-air-ground integrated networkreinforcement learningLEO constellationsimulation platformInternet of vehicles

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