The next generation of satellite constellations is designed to better address the future needs of our connected society: highly-variable data demand, mobile connectivity, and reaching more under-served regions. Artificial Intelligence (AI) and learning-based methods are expected to become key players in the industry, given the poor scalability and slow reaction time of current resource allocation mechanisms. While AI frameworks have been validated for isolated communication tasks or subproblems, there is still not a clear path to achieve fully-autonomous satellite systems. Part of this issue results from the focus on subproblems when designing models, instead of the necessary system-level perspective. In this paper we try to bridge this gap by characterizing the system-level needs that must be met to increase satellite autonomy, and introduce three AI-based components (Demand Estimator, Offline Planner, and Real Time Engine) that jointly address them. We first do a broad literature review on the different subproblems and identify the missing links to the system-level goals. In response to these gaps, we outline the three necessary components and highlight their interactions. We also discuss how current models can be incorporated into the framework and possible directions of future work.
翻译:下一代卫星星座的设计是为了更好地满足我们相联社会的未来需要:高度可变的数据需求、移动连通性,以及覆盖更多的服务不足的区域。鉴于当前资源分配机制的可扩展性差和反应时间缓慢,人工智能和学习方法预计将成为该行业的主要参与者。虽然已经为孤立的通信任务或次级问题验证了AI框架,但是仍然没有实现完全自主的卫星系统的明确途径。这个问题的一部分产生于在设计模型时侧重于次级问题,而不是必要的系统层面观点。在本文中,我们试图弥合这一差距,具体描述为增加卫星自主而必须满足的系统层面需求,并引入三个基于AI的构成部分(Demanand Eminator、Offline Planner和实时引擎),共同解决这些问题。我们首先对不同的子问题进行广泛的文献审查,并找出与系统层面目标的缺失环节。针对这些差距,我们概述了三个必要的组成部分,并强调了它们之间的相互作用。我们还讨论了如何将当前模式纳入未来框架和可能的工作方向。