空天地一体化网络技术:探索与展望

Space-air-ground integrated networks:review and prospect

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。

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.