Space-Air-Ground Integrated Networks are a promising network architecture, which support seamless, high-rate, and reliable transmission with extremely large coverage. However, the infrastructure, resources, end devices, and applications in communication and network systems have become more complex and heterogeneous.

At the same time, the large amount of terminal equipment and network data poses a serious challenge to the operation and management of Space-Air-Ground Integrated Networks. Machine learning has powerful data processing capabilities and offers viable solutions to a variety of problems. Machine learning can be a promising solution for intelligent and efficient network operation and management, reducing the cost of running the Space-Air-Ground Integrated Networks.

The aim of this Special Issue is to attract original research and review articles that help advance techniques for Space-Air-Ground Integrated Networks-based machine learning, to design efficient mathematic models, transmission strategies, and protocols for Space-Air-Ground Integrated Networks-based machine learning and to efficiently analyze and evaluate the system performance. These topics have carved out a new area rich in research and innovation potential.

Potential topics include but are not limited to the following:

• Resource allocation in Space-Air-Ground Integrated Networks-based machine learning
• Transmission strategies in Space-Air-Ground Integrated Networks-based machine learning
• Mathematical modeling of Space-Air-Ground Integrated Networks-based machine learning
• Security, privacy and trust in Space-Air-Ground Integrated Networks-based machine learning
• New architectures and protocols in Space-Air-Ground Integrated Networks-based machine learning
• Scalable designs in Space-Air-Ground Integrated Networks-based machine learning
• Network management in Space-Air-Ground Integrated Networks-based machine learning
• Network automation in Space-Air-Ground Integrated Networks-based machine learning
• Novel multiple access techniques in Space-Air-Ground Integrated Networks-based machine learning
• Big data analysis in Space-Air-Ground Integrated Networks-based machine learning
• Spectrum sensing in Space-Air-Ground Integrated Networks-based machine learning
• IoT services and applications in Space-Air-Ground Integrated Networks-based machine learning
• Novel machine learning algorithms in Space-Air-Ground Integrated Networks
• Edge intelligence in Space-Air-Ground Integrated Networks
• Application of Cloud/Edge computing in Space-Air-Ground Integrated Networks-based machine learning