Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence

By Nei Kato, Zubair Muhammad Fadlullah, Fengxiao Tang, Bomin Mao, S. Tani, A. Okamura, Jiajia Liu. IEEE wireless communications, 2018

353 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/af552bb68f5f0ae5d1dbef254d27781469e3bd35

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In Aerospace Engineering

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Citations in the corpus

Integrated Satellite-Terrestrial Networks Toward 6G: Architectures, Applications, and Challenges (2022, background): “In [211], a deep learning-based routing strategy was proposed to reduce the routing delay in the integrated satellite-terrestrial network.”
Non-Terrestrial Networks in 5G & Beyond: A Survey (2020, background): “The authors in [11] were the first to provide a review on Space-Air-Ground Integrated Networks (SAGIN), where the system performance has been improved by exploiting deep learningmethods for traffic balancing purposes [64].”
Artificial-Intelligence-Enabled Intelligent 6G Networks (2019, methodology): “Although the references [5, 7, 8] applied AI to enable intelligent wireless networks, they did not investigate how to systematically sense data from environments, analyze collected data, and then apply the discovered knowledge to optimize network performance for 6G.”
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey (2021, background): “Several important challenges of multi-segment networking that significantly influence the performance of overall network have been recognized in [218], including network control,”
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, background): “Besides the simple V2V and V2I scenarios, by considering the advanced vehicular network that is heterogeneous with various devices and equipment, the deeplearning-based network traffic control for complex vehicular environment, including high-speed UAVs, long-distance roadside unit (RSU), and satell”
The Roadmap to 6G - AI Empowered Wireless Networks (2019, background): “As such, 6G will unleash the full potential of mobile communications, computing, and control in a host of exciting applications, including smart cities, connected infrastructure, wearable computers, autonomous driving, UAVs [7], seamless virtual and augmented reality, Internet of Things, space-airgr”