Reinforcement Learning for Satellite Communications: From LEO to Deep Space Operations
Abstract
The National Aeronautics and Space Administration (NASA) is in the midst of defining and developing the future space and ground architecture for the coming decades to return science and exploration discovery data back to investigators on Earth. Optimizing the data return from these missions requires planning, design, standards, and operations coordinated from formulation and development throughout the mission. The use of automation enhanced by cognition and machine learning are potential methods for optimizing data return, reducing costs of operations, and helping manage the complexity of the automated systems. In this article, we discuss the potential role of machine learning in the linkto- link aspect of the communication systems. An experiment using NASA's Space Communication and Navigation Testbed onboard the International Space Station and the ground station located at NASA John H. Glenn Research Center demonstrates for the first time the benefits and challenges of applying machine learning to space links in the actual flight environment. The experiment used machine learning decisions to configure a space link from the ISS-based testbed to the ground station to achieve multiple objectives related to data throughput, bandwidth, and power. Aspects of the specific neural-network-based reinforcement learning algorithm formation and on-orbit testing are discussed.
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ELMDR strategy was developed for distributed routing to address ever-increasing traffic distribution on earth.
“Considering the ever-increasing traffic distribution on earth the authors in [181] developed an Extreme Learning Machine (ELM) Distributed Routing (ELMDR) strategy.”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [181]
Simulation results demonstrate that ELMDR sufficiently uses underutilized link and reduces delay compared to conventional ACO algorithm.
“Simulation results demonstrate that in comparison to the conventional ACO algorithm ELMDR not only sufficiently uses underutilized link, but also reduces delay.”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [181]
Adding ML and cognitive algorithms will open up entire new fields of research with the potential to reduce complexity, enhance performance, and undoubtedly minimize the cost of space operations.
“Although autonomous control systems without humans in the loop already exist for ground station network management, adding ML and cognitive algorithms will open up entire new fields of research with the potential to reduce complexity, enhance performance, and undoubtedly minimize the cost of space operations [181] [180].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [181]
Effective and reliable routing for LEO satellite network is a difficult task due to time-varying topology, imbalanced communication load and frequent link handover.
“Notwithstanding, the effective and reliable routing for LEO satellite network is a difficult task due to time-varying topology, imbalanced communication load and frequent link handover [181].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [181]
Cited by
Sorted by citations
A practical guide to multi-objective reinforcement learning and planning
Hayes, C. F., Rădulescu, R., Bargiacchi, E., Källström, J., Macfarlane, M., Reymond, M., Verstraeten, T., Zintgraf, L. M., Dazeley, R., Heintz, F., et al. (2022) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-022-09552-y · Cited by 400 · see citing works
“In addition to the motivating examples discussed above, recent years have seen multiobjective learning and planning methods applied across a wide range of problem domains including: distributed computing [27, 124], drug and molecule design [62, 214], cybersecurity [162], simulation [132], job shop scheduling [98], cog…”
BackgroundThe Final Frontier: Deep Learning in Space
Kothari, V., Liberis, E., & Lane, N. D. (2020) · Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications · doi:10.1145/3376897.3377864 · Cited by 86 · see citing works
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“Cognitive communication architectures will undoubtedly play a critical role in future space missions, providing seamless internetworking services for communication and navigation from within Earth‘s orbit out [175], [25, 180].”
BackgroundMethodInfluentialRapidly Deployable Satellite-Based Emergency Communications Infrastructure
Kagai, F., Branch, P., But, J., Allen, R., & Rice, M. (2024) · IEEE Access · doi:10.1109/access.2024.3465512 · Cited by 49 · see citing works
- Authors
- Ferreira, P. V. R., Paffenroth, R., Wyglinski, A. M., Hackett, T. M., Bilen, S. G., Reinhart, R. C., & Mortensen, D. J.
- Affiliations
- Worcester Polytechnic Institute (US)
- Pennsylvania State University (US)
- Glenn Research Center (US)
- Venue
- IEEE Communications Magazine
- Published
- 2019-05
- Volume
- 57
- Issue
- 5
- Pages
- 70-75
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W2945875572
- Cited by
- 82
- References
- 0
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 13 · 2023: 13 · 2022: 15 · 2021: 18 · 2020: 4 · 2019: 3
- FWCI
- 7.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Communications Magazine (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Satellite Communication Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Space Technology and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- reinforcement learning, satellite communication, deep space communication, low Earth orbit, artificial neural networks, International Space Station, ground station, data throughput, bandwidth allocation, power allocation, in-orbit testing
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