Revolutionizing Future Connectivity: A Contemporary Survey on AI-Empowered Satellite-Based Non-Terrestrial Networks in 6G

被引:22
|
作者
Mahboob, Shadab [1 ]
Liu, Lingjia [1 ]
机构
[1] Virginia Tech, Bradley Dept Elect & Comp Engn, Blacksburg, VA 24060 USA
来源
基金
美国国家科学基金会;
关键词
Artificial intelligence; 6G mobile communication; Ear; Surveys; Ions; Satellite broadcasting; Tutorials; Non-terrestrial networks (NTN); space-air-ground integrated networks (SAGIN); artificial intelligence (AI); machine learning (ML); deep learning (DL); 5G-advanced; 6G; satellite; beam-hopping; handover; spectrum sharing; doppler shift; resource allocation; computational offloading; network routing; network slicing; channel estimation; security; open radio access network (O-RAN); RAN intelligent controller (RIC); STOCHASTIC GRADIENT DESCENT; RADIO RESOURCE-MANAGEMENT; ARTIFICIAL-INTELLIGENCE; WIRELESS NETWORKS; INTRUSION DETECTION; CELLULAR NETWORKS; NEURAL-NETWORKS; REINFORCEMENT; COMMUNICATION; MACHINE;
D O I
10.1109/COMST.2023.3347145
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th Generation (6G) networks, providing ubiquitous, continuous, and scalable services. Satellites emerge as the primary enabler for NTN, leveraging their extensive coverage, stable orbits, scalability, and adherence to international regulations. However, satellite-based NTN presents unique challenges, including long propagation delay, high Doppler shift, frequent handovers, spectrum sharing complexities, and intricate beam and resource allocation, among others. The integration of NTNs into existing terrestrial networks in 6G introduces a range of novel challenges, including task offloading, network routing, network slicing, and many more. To tackle all these obstacles, this paper proposes Artificial Intelligence (AI) as a promising solution, harnessing its ability to capture intricate correlations among diverse network parameters. We begin by providing a comprehensive background on NTN and AI, highlighting the potential of AI techniques in addressing various NTN challenges. Next, we present an overview of existing works, emphasizing AI as an enabling tool for satellite-based NTN, and explore potential research directions. Furthermore, we discuss ongoing research efforts that aim to enable AI in satellite-based NTN through software-defined implementations, while also discussing the associated challenges. Finally, we conclude by providing insights and recommendations for enabling AI-driven satellite-based NTN in future 6G networks.
引用
收藏
页码:1279 / 1321
页数:43
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