Wireless Communications and Mobile Computing

Machine Learning for Space-Air-Ground Integrated Networks


Publishing date
01 Jan 2023
Status
Published
Submission deadline
26 Aug 2022

Lead Editor
Guest Editors

1University of Science and Technology Beijing, Beijing, China

2Google, California, USA

3Northwestern Polytechnical University, Xi'an, China


Machine Learning for Space-Air-Ground Integrated Networks

Description

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

Articles

  • Special Issue
  • - Volume 2022
  • - Article ID 2673024
  • - Research Article

Performance of Spectrum Sharing in Hybrid Satellite Terrestrial Network with Opportunistic Relay Selection

Guanchang Xue | Mingchuan Yang | ... | Shuai Yuan
  • Special Issue
  • - Volume 2022
  • - Article ID 5338876
  • - Research Article

Trusted Data Sharing Mechanism Based on Blockchain and Federated Learning in Space-Air-Ground Integrated Networks

Da Li | Qinglei Guo | ... | Han Yan
  • Special Issue
  • - Volume 2022
  • - Article ID 7730456
  • - Research Article

Resource Allocation in UAV-Assisted Wireless Powered Communication Networks for Urban Monitoring

Ting Lyu | Haiwang Zhang | Haitao Xu
  • Special Issue
  • - Volume 2022
  • - Article ID 3631134
  • - Research Article

Research on Mono-Pulse Beam Angle Tracking Algorithm of Phased Array Antenna Based on CKF

Liu Xuan | Li Ruo-xin | He Yi-feng
  • Special Issue
  • - Volume 2022
  • - Article ID 6783041
  • - Research Article

Multipath Stability Routing in Cognitive UAV Swarm for Emergency Communications: A Hypergraph Matching Approach

Xiaozheng Ma | Yao Wang | Long Zhang
  • Special Issue
  • - Volume 2022
  • - Article ID 5876180
  • - Research Article

Early Cyberspace Emergency Response by Predicting Social-Emotional Security

Si Jiang | Ziwang Fu | ... | Binxing Fang
Wireless Communications and Mobile Computing
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Acceptance rate11%
Submission to final decision194 days
Acceptance to publication66 days
CiteScore2.300
Journal Citation Indicator-
Impact Factor-

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