Content area

Abstract

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.

Details

1009240
Title
Artificial Intelligence for 5G and 6G Networks: A Taxonomy-Based Survey of Applications, Trends, and Challenges
Publication title
Volume
13
Issue
12
First page
559
Number of pages
51
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
e-ISSN
22277080
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-12-01
Milestone dates
2025-10-06 (Received); 2025-11-12 (Accepted)
Publication history
 
 
   First posting date
01 Dec 2025
ProQuest document ID
3286356793
Document URL
https://www.proquest.com/scholarly-journals/artificial-intelligence-5g-6g-networks-taxonomy/docview/3286356793/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-12-24
Database
ProQuest One Academic