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Towards Autonomous Satellite Communications: An AI-based Framework to Address System-level Challenges

arXiv 2112.06055 ↗First posted 11 Dec 2021cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 78 references that are in the map. First posted on arXiv 11 Dec 2021.

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16 of 78 references.

References · 16

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4 more
  1. 2017Divide-and-Conquer Reinforcement Learningcited by 48
  2. 2019Value constrained model-free continuous controlcited by 34
  3. 2020Q-Learning in enormous action spaces via amortized approximate maximizationcited by 30
  4. 2020Joint Satellite Gateway Placement and Routing for Integrated Satellite-Terrestrial Networkscited by 1

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