Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence

Nei Kato, Zubair Md. Fadlullah, Fengxiao Tang, Bomin Mao, Shigenori Tani, Atsushi Okamura, Jiajia Liu

Research output: Contribution to journalArticle

34 Citations (Scopus)


It is widely acknowledged that the development of traditional terrestrial communication technologies cannot provide all users with fair and high quality services due to scarce network resources and limited coverage areas. To complement the terrestrial connection, especially for users in rural, disaster-stricken, or other difficult-to-serve areas, satellites, UAVs, and balloons have been utilized to relay communication signals. On this basis, SAGINs have been proposed to improve the users' QoE. However, compared with existing networks such as ad hoc networks and cellular networks, SAGINs are much more complex due to the various characteristics of three network segments. To improve the performance of SAGINs, researchers are facing many unprecedented challenges. In this article, we propose the AI technique to optimize SAGINs, as the AI technique has shown its predominant advantages in many applications. We first analyze several main challenges of SAGINs and explain how these problems can be solved by AI. Then, we consider the satellite traffic balance as an example and propose a deep learning based method to improve traffic control performance. Simulation results evaluate that the deep learning technique can be an efficient tool to improve the performance of SAGINs.

Original languageEnglish
Article number8612450
Pages (from-to)140-147
Number of pages8
JournalIEEE Wireless Communications
Issue number4
Publication statusPublished - 2019 Aug 1

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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