OPTIMIZING SPACE-AIR-GROUND INTEGRATED NETWORKS BY ARTIFICIAL INTELLIGENCE
OPTIMIZING SPACE-AIR-GROUND INTEGRATED NETWORKS BY ARTIFICIAL INTELLIGENCE
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DOI：
10.1109/mwc.2018.1800365
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发表时间：
2019-08-01
影响因子：
12.9
通讯作者：
Liu, Jiajia
中科院分区：
文献类型：
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作者：
Kato, Nei;Fadlullah, Zubair Md.;Liu, Jiajia
人们普遍认为，传统地面通信技术的发展，由于网络资源稀缺，覆盖区域有限，无法为所有用户提供公平、高质量的服务。为了补充地面连接，特别是对于农村、受灾地区或其他难以服务的地区的用户，已经利用卫星、UAV和气球来中继通信信号。在此基础上，提出了SAGIN来提高用户的QoE。然而，与现有的网络，如ad hoc网络和蜂窝网络相比，SAGIN是复杂得多，由于三个网段的各种特性。为了提高SAGIN的性能，研究人员面临着许多前所未有的挑战。在这篇文章中，我们提出了人工智能技术来优化SAGIN，因为人工智能技术在许多应用中显示出其突出的优势。我们首先分析了SAGIN的几个主要挑战，并解释了AI如何解决这些问题。然后，我们以卫星流量平衡为例，提出了一种基于深度学习的方法来提高流量控制性能。仿真结果表明，深度学习技术可以有效地提高SAGIN的性能。
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.