Generative AI in Satellite and NTN: Connecting the Unconnected
1h 21mIntermediate2026-04-27
Authors
Rahul Kaundal
Itelcotech
Course details
In this course on Generative AI in Satellite and NTN, you will explore the integration of AI technologies with non-terrestrial networks. Build your understanding of the fundamentals of satellite communication systems, including orbital configurations and the role of 3GPP in standardization. Discover how AI technologies are applied to optimize satellite operations and improve data delivery. Analyze advanced satellite architectures and the complexities of integrating space-based and terrestrial networks. Learn to harness the power of generative AI for creating intelligent satellite networks capable of autonomous anomaly resolution and seamless space-terrestrial connectivity. Ideal for telecom engineers, AI practitioners, and technical enthusiasts, this course helps you develop the skills to evaluate and design AI-driven satellite networks that enhance 5G and 6G frameworks.
Learning objectives
Explain the fundamentals of Non-Terrestrial Networks (NTN), including satellite communication architectures, orbital configurations (LEO, MEO, GEO), and the role of 3GPP in standardization and spectrum allocation.
Differentiate between core AI concepts—including Machine Learning, Deep Learning, and Generative AI—and analyze their application in satellite operations, from voice assistants to cognitive power and thermal management.
Compare NTN deployment models and payload architectures—including transparent, regenerative, and disaggregated RAN—and evaluate their suitability for various use case scenarios.
Analyze the integration of Generative AI and Large Language Models (LLMs) within the satellite ecosystem, and assess techniques for mitigating hallucinations in network AI systems.
Design and justify the application of Generative AI for specialized NTN use cases, including satellite network planning and optimization, autonomous operations with anomaly resolution, and space-terrestrial network integration for 5G and 6G.
Evaluate the transformative potential of AI-driven cognitive operations in satellite networks, including adaptive data delivery, resource management, and intelligent subscriber services.
Learning objectives
Explain the fundamentals of Non-Terrestrial Networks (NTN), including satellite communication architectures, orbital configurations (LEO, MEO, GEO), and the role of 3GPP in standardization and spectrum allocation.
Differentiate between core AI concepts—including Machine Learning, Deep Learning, and Generative AI—and analyze their application in satellite operations, from voice assistants to cognitive power and thermal management.
Compare NTN deployment models and payload architectures—including transparent, regenerative, and disaggregated RAN—and evaluate their suitability for various use case scenarios.
Analyze the integration of Generative AI and Large Language Models (LLMs) within the satellite ecosystem, and assess techniques for mitigating hallucinations in network AI systems.
Design and justify the application of Generative AI for specialized NTN use cases, including satellite network planning and optimization, autonomous operations with anomaly resolution, and space-terrestrial network integration for 5G and 6G.
Evaluate the transformative potential of AI-driven cognitive operations in satellite networks, including adaptive data delivery, resource management, and intelligent subscriber services.
Concepts
Introduction
- Introduction
Satellite and NTN Fundamentals
- Introduction to non-terrestrial networks (NTN)
- The role of 3GPP in global NTN standardization
- Satellite communication architectures - core principles
- Satellite platforms and orbital configurations
- Selecting optimal orbit for NTN services
- NTN spectrum and frequency allocation
Understanding AI and Its Use in NTN
- The AI landscape - from theory to implementation
- Defining intelligence in machines
- Deconstructing voice assistants - the technology behind Siri
- Automated customer support in NTN - chatbots for subscriber services
- Fundamentals of machine learning
- How recommendation engine works
- Context-aware satellite IoT - adaptive data delivery and control
- Introduction to deep learning architectures
- Biometric analysis - inside facial recognition technology
- Cognitive satellite operations - AI for power and thermal management
NTN Architecture and Deployment
- NTN deployment models and use case scenarios
- Understanding transparent payload architecture
- Understanding regenerative payload architecture
- Advanced architecture - regenerative payload with disaggregated RAN
GenAI in IoT Ecosystem and Its Implementation
- Understanding generative AI
- How generative AI works - a step-by-step overview
- Understanding transformers
- Large language models - the foundation of GenAI
- Inside GPT - architecture and function
- Mitigating hallucinations in network AI systems
- Building a generative AI assistant for satellite operations
- Satellite network planning, design & optimization
- Autonomous operations & anomaly resolution
- Space terrestrial network integration (5G 6G NTN)
Conclusion
- Conclusion