Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence
Nei Kato,Zubair Md. Fadlullah,Fengxiao Tang,Bomin Mao,Shigenori Tani,Atsushi Okamura,Jiajia Liu +6 more
TLDR
In this article, a deep learning-based method was proposed to improve the performance of SAGINs, where the authors analyzed several main challenges of SagINs and explained how these problems can be solved by AI.Abstract:
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.read more
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References
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Yong Zeng,Rui Zhang +1 more
TL;DR: In this article, a new design paradigm that jointly considers both the communication throughput and the UAV's energy consumption was proposed to maximize the energy efficiency of UAV communications with a ground terminal.
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Lav Gupta,Raj Jain,Gabor Vaszkun +2 more
TL;DR: This paper surveys the work done toward all of the outstanding issues, relating to this new class of networks, so as to spur further research in these areas.
Journal ArticleDOI
Space-Air-Ground Integrated Network: A Survey
TL;DR: This paper is the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the Integration of all the three network segments.
Journal ArticleDOI
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems
Zubair Md. Fadlullah,Fengxiao Tang,Bomin Mao,Nei Kato,Osamu Akashi,Takeru Inoue,Kimihiro Mizutani +6 more
TL;DR: An overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems, and a new use case, i.e., deep learning based intelligent routing, which is demonstrated to be effective in contrast with the conventional routing strategy.
Journal ArticleDOI
The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective
Nei Kato,Zubair Md. Fadlullah,Bomin Mao,Fengxiao Tang,Osamu Akashi,Takeru Inoue,Kimihiro Mizutani +6 more
TL;DR: Preliminary results are reported that demonstrate the encouraging performance of the proposed deep learning system compared to a benchmark routing strategy (Open Shortest Path First (OSPF)) in terms of significantly better signaling overhead, throughput, and delay.