mlsanityData to 14 Oct 2026

Artificial Intelligence for Satellite Communication: A Review

arXiv 2101.10899 ↗First posted 25 Jan 2021eess.SP cs.AI cs.SYcited by 14

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Abstract (arXiv)

Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing. This work thus provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. Several challenges facing diverse aspects of satellite communication systems are then discussed, and their proposed and potential AI-based solutions are presented. Finally, an outlook of field is drawn, and future steps are suggested.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 33 of its 249 references that are in the map. First posted on arXiv 25 Jan 2021.

Builds on

33 of 249 references.

References · 33

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21 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 6; 2025: 6; 2026: 1.

14 papers.

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  1. 2025UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobilitycited by 49
  2. 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
  3. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Artcited by 14
  4. 2024Continual Deep Reinforcement Learning for Decentralized Satellite Routingcited by 6
  5. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
  6. 2024Stochastic Q-learning for Large Discrete Action Spacescited by 5
  7. 2024Addressing Bias Through Ensemble Learning and Regularized Fine-Tuningcited by 2
  8. 2025LimitNet: Progressive, Content-Aware Image Offloading for Extremely Weak Devices & Networkscited by 1
  9. 2022Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networkscited by 0
  10. 2024Image-based Detection of Segment Misalignment in Multi-mirror Satellites using Transfer Learningcited by 0
  11. 2024Heterogeneity: An Open Challenge for Federated On-board Machine Learningcited by 0
  12. 2025Preserving Domain Generalization in Fine-Tuning via Joint Parameter Selectioncited by 0
2 more
  1. 2025Intelligent Spectrum Management in Satellite Communicationscited by 0
  2. 2026Exploiting In-Sensor Computing for Energy-Efficient Earth Observationcited by 0