Artificial Intelligence for 5G and 6G Networks: A Taxonomy-Based Survey of Applications, Trends, and Challenges
Αποθηκεύτηκε σε:
| Εκδόθηκε σε: | Technologies vol. 13, no. 12 (2025), p. 559-609 |
|---|---|
| Κύριος συγγραφέας: | |
| Άλλοι συγγραφείς: | , |
| Έκδοση: |
MDPI AG
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| Θέματα: | |
| Διαθέσιμο Online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 024 | 7 | |a 10.3390/technologies13120559 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231637 |2 nlm | ||
| 100 | 1 | |a Nouri, Omheni | |
| 245 | 1 | |a Artificial Intelligence for 5G and 6G Networks: A Taxonomy-Based Survey of Applications, Trends, and Challenges | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The mobile network ecosystem is undergoing profound change driven by Artificial Intelligence (AI), Network Function Virtualization (NFV), and Software-Defined Networking (SDN). These technologies are well positioned to enable the essential transformation of next-generation networks, delivering significant improvements in efficiency, flexibility, and sustainability. AI is expected to impact the entire lifecycle of mobile networks, including design, deployment, service implementation, and long-term management. This article reviews the key characteristics of 5G and the anticipated technology enablers of 6G, focusing on the integration of AI within mobile networks. This study addresses several perspectives, including network optimization, predictive analytics, and security enhancement. A taxonomy is proposed to classify AI applications into 5G and 6G according to their role in network operations and their impact across vertical domains such as the Internet of Things (IoT), healthcare, and transportation. Furthermore, emerging trends are discussed, including federated learning, advanced AI models, and explainable AI, along with major challenges related to data privacy, adaptability, and interoperability. This paper concludes with future research directions, emphasizing the importance of ethical AI policies and cross-sector collaborations to ensure effective and sustainable AI-enabled mobile networks. | |
| 653 | |a Wireless communications | ||
| 653 | |a Network function virtualization | ||
| 653 | |a Interoperability | ||
| 653 | |a Collaboration | ||
| 653 | |a Deep learning | ||
| 653 | |a Internet of Things | ||
| 653 | |a Network security | ||
| 653 | |a 5G mobile communication | ||
| 653 | |a Mobile communications networks | ||
| 653 | |a Real time | ||
| 653 | |a Taxonomy | ||
| 653 | |a Architecture | ||
| 653 | |a Ethics | ||
| 653 | |a Machine learning | ||
| 653 | |a Ethical standards | ||
| 653 | |a Federated learning | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Virtual reality | ||
| 653 | |a Technology | ||
| 653 | |a Cognition & reasoning | ||
| 653 | |a Data integrity | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Trends | ||
| 653 | |a 6G mobile communication | ||
| 653 | |a Networks | ||
| 700 | 1 | |a Koubaa Hend | |
| 700 | 1 | |a Zarai Faouzi | |
| 773 | 0 | |t Technologies |g vol. 13, no. 12 (2025), p. 559-609 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286356793/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286356793/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286356793/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |