AI in SAGIN: Building Deep Learning Service-Oriented Space-Air-Ground Integrated Networks

In next-generation mobile communications, space-air-ground integrated networks (SAGINs) is an emerging infrastructure in future wireless access networks. Since artificial intelligence (AI) applications become more and more important, it is essential to build a deep learning service-oriented SAGINs....

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Bibliographic Details
Published in:IEEE network 2023-03, Vol.37 (2), p.154-159
Main Authors: Li, He, Ota, Kaoru, Dong, Mianxiong
Format: Article
Language:English
Subjects:
Artificial intelligence
Cognitive tasks
Deep learning
Low earth orbit satellites
Machine learning
Mobile handsets
Optimization
Satellites
Space-air-ground integrated networks
Task analysis
Wireless networks
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Summary:In next-generation mobile communications, space-air-ground integrated networks (SAGINs) is an emerging infrastructure in future wireless access networks. Since artificial intelligence (AI) applications become more and more important, it is essential to build a deep learning service-oriented SAGINs. In this article, we present a hierarchical intelligent computing structure focusing on processing deep learning tasks in future SAGINs. An optimization strategy is also proposed to improve the quality-of-service (QoS) of deep learning tasks in the proposed structure. We test our work in small testbed and simulations. The evaluation results show that the proposed work outperforms other offloading strategies in a SAGIN environment.
ISSN:0890-8044
1558-156X
DOI:10.1109/MNET.001.2000512