Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey
6G networks can support global, ubiquitous and seamless connectivity through the convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios, NTNs pose unique challenges including propagation characteristics, latency and mobility, owing to the operations in spaceborn...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10250790/ |
| _version_ | 1797676715276763136 |
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| author | Amjad Iqbal Mau-Luen Tham Yi Jie Wong Ala'a Al-Habashna Gabriel Wainer Yong Xu Zhu Tasos Dagiuklas |
| author_facet | Amjad Iqbal Mau-Luen Tham Yi Jie Wong Ala'a Al-Habashna Gabriel Wainer Yong Xu Zhu Tasos Dagiuklas |
| author_sort | Amjad Iqbal |
| collection | DOAJ |
| description | 6G networks can support global, ubiquitous and seamless connectivity through the convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios, NTNs pose unique challenges including propagation characteristics, latency and mobility, owing to the operations in spaceborne and airborne platforms. To overcome all these technical hurdles, this survey paper presents the use of artificial intelligence (AI) techniques in learning and adapting to the complex NTN environments. We begin by providing an overview of NTNs in the context of 6G, highlighting the potential security and privacy issues. Next, we review the existing AI methods adopted for 6G NTN optimization, starting from machine learning (ML), through deep learning (DL) to deep reinforcement learning (DRL). All these AI techniques have paved the way towards more intelligent network planning, resource allocation (RA), and interference management. Furthermore, we discuss the challenges and opportunities in AI-powered NTN for 6G networks. Finally, we conclude by providing insights and recommendations on the key enabling technologies for future AI-powered 6G NTNs. |
| first_indexed | 2024-03-11T22:34:27Z |
| format | Article |
| id | doaj.art-d41937599e2648e5ac86b4dadf47ed73 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T22:34:27Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d41937599e2648e5ac86b4dadf47ed732023-09-22T23:00:41ZengIEEEIEEE Access2169-35362023-01-011110098610100610.1109/ACCESS.2023.331473210250790Empowering Non-Terrestrial Networks With Artificial Intelligence: A SurveyAmjad Iqbal0https://orcid.org/0009-0009-3614-1554Mau-Luen Tham1https://orcid.org/0000-0003-4600-9839Yi Jie Wong2https://orcid.org/0000-0003-4598-2653Ala'a Al-Habashna3https://orcid.org/0000-0003-2721-970XGabriel Wainer4https://orcid.org/0000-0003-3366-9184Yong Xu Zhu5https://orcid.org/0000-0002-5413-1968Tasos Dagiuklas6https://orcid.org/0000-0002-8101-1208Department of Electrical and Electronic Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long Campus, Selangor, MalaysiaDepartment of Electrical and Electronic Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long Campus, Selangor, MalaysiaDepartment of Electrical and Electronic Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long Campus, Selangor, MalaysiaDepartment of Systems and Computer Engineering, Carleton University, Ottawa, CanadaDepartment of Electrical and Electronic Engineering, University of Warwick, Coventry, U.KDepartment of Electrical and Electronic Engineering, University of Warwick, Coventry, U.KDivision of Computer Science and Informatics, School of Engineering, London South Bank University, London, U.K6G networks can support global, ubiquitous and seamless connectivity through the convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios, NTNs pose unique challenges including propagation characteristics, latency and mobility, owing to the operations in spaceborne and airborne platforms. To overcome all these technical hurdles, this survey paper presents the use of artificial intelligence (AI) techniques in learning and adapting to the complex NTN environments. We begin by providing an overview of NTNs in the context of 6G, highlighting the potential security and privacy issues. Next, we review the existing AI methods adopted for 6G NTN optimization, starting from machine learning (ML), through deep learning (DL) to deep reinforcement learning (DRL). All these AI techniques have paved the way towards more intelligent network planning, resource allocation (RA), and interference management. Furthermore, we discuss the challenges and opportunities in AI-powered NTN for 6G networks. Finally, we conclude by providing insights and recommendations on the key enabling technologies for future AI-powered 6G NTNs.https://ieeexplore.ieee.org/document/10250790/Non-terrestrial networks (NTNs)artificial intelligence (AI)5G/6Gunmanned aircraft system (UAS)resource allocation (RA)reinforcement learning (RL) |
| spellingShingle | Amjad Iqbal Mau-Luen Tham Yi Jie Wong Ala'a Al-Habashna Gabriel Wainer Yong Xu Zhu Tasos Dagiuklas Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey IEEE Access Non-terrestrial networks (NTNs) artificial intelligence (AI) 5G/6G unmanned aircraft system (UAS) resource allocation (RA) reinforcement learning (RL) |
| title | Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey |
| title_full | Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey |
| title_fullStr | Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey |
| title_full_unstemmed | Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey |
| title_short | Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey |
| title_sort | empowering non terrestrial networks with artificial intelligence a survey |
| topic | Non-terrestrial networks (NTNs) artificial intelligence (AI) 5G/6G unmanned aircraft system (UAS) resource allocation (RA) reinforcement learning (RL) |
| url | https://ieeexplore.ieee.org/document/10250790/ |
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