# Artificial Intelligence for Satellite Communication: A Review

arXiv: https://arxiv.org/abs/2101.10899 · HTML: https://mlsanity.com/paper/2101.10899/

## 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 (most cited first, 30 of 33)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Accelerating Eulerian Fluid Simulation With Convolutional Networks](https://mlsanity.com/paper/1607.03597/) (2016)
- [Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition](https://mlsanity.com/paper/1811.00751/) (2018)
- [Satellite Pose Estimation Challenge: Dataset, Competition Design and Results](https://mlsanity.com/paper/1911.02050/) (2019)
- [Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement](https://mlsanity.com/paper/1908.11542/) (2019)
- [Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism](https://mlsanity.com/paper/1708.02843/) (2017)
- [A Very Brief Introduction to Machine Learning With Applications to Communication Systems](https://mlsanity.com/paper/1808.02342/) (2018)
- [Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/1901.07947/) (2019)
- [The Final Frontier: Deep Learning in Space](https://mlsanity.com/paper/2001.10362/) (2020)
- [A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends](https://mlsanity.com/paper/1505.07919/) (2015)
- [Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence](https://mlsanity.com/paper/1808.01053/) (2018)
- [Artificial Intelligence for UAV-enabled Wireless Networks: A Survey](https://mlsanity.com/paper/2009.11522/) (2020)
- [CubeSat Communications: Recent Advances and Future Challenges](https://mlsanity.com/paper/1908.09501/) (2019)
- [Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection](https://mlsanity.com/paper/2006.05923/) (2020)
- [Survey of Inter-satellite Communication for Small Satellite Systems: Physical Layer to Network Layer View](https://mlsanity.com/paper/1609.08583/) (2016)
- [Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks](https://mlsanity.com/paper/2005.12521/) (2020)
- [Deep learning based inverse method for layout design](https://mlsanity.com/paper/1806.03182/) (2018)
- [Spectrum Prediction and Interference Detection for Satellite Communications](https://mlsanity.com/paper/1912.04716/) (2019)
- [Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learning](https://mlsanity.com/paper/1712.06793/) (2017)
- [Deep Learning For Experimental Hybrid Terrestrial and Satellite Interference Management](https://mlsanity.com/paper/1906.03012/) (2019)

## Built on by (most cited first, 14 of 14)

- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Continual Deep Reinforcement Learning for Decentralized Satellite Routing](https://mlsanity.com/paper/2405.12308/) (2024)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Stochastic Q-learning for Large Discrete Action Spaces](https://mlsanity.com/paper/2405.10310/) (2024)
- [Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning](https://mlsanity.com/paper/2402.00910/) (2024)
- [LimitNet: Progressive, Content-Aware Image Offloading for Extremely Weak Devices & Networks](https://mlsanity.com/paper/2504.13736/) (2025)
- [Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks](https://mlsanity.com/paper/2202.12683/) (2022)
- [Image-based Detection of Segment Misalignment in Multi-mirror Satellites using Transfer Learning](https://mlsanity.com/paper/2407.20582/) (2024)
- [Heterogeneity: An Open Challenge for Federated On-board Machine Learning](https://mlsanity.com/paper/2408.06903/) (2024)
- [Preserving Domain Generalization in Fine-Tuning via Joint Parameter Selection](https://mlsanity.com/paper/2508.16976/) (2025)
- [Intelligent Spectrum Management in Satellite Communications](https://mlsanity.com/paper/2509.00286/) (2025)
- [Exploiting In-Sensor Computing for Energy-Efficient Earth Observation](https://mlsanity.com/paper/2606.01271/) (2026)

## 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.