@article{oai:muroran-it.repo.nii.ac.jp:02000228,
 author = {Li He and 李　鶴 and Ota Kaoru and 太田　香 and Dong Mianxiong and 董　冕雄},
 issue = {2},
 journal = {IEEE Network},
 month = {Aug},
 note = {In next-generation mobile communications, spaceair-
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.},
 pages = {154--159},
 title = {AI in SAGIN: Building Deep Learning Service-Oriented Space-Air-Ground Integrated Networks},
 volume = {37},
 year = {2022}
}