Unveiling the future: A comprehensive analysis of 6G technology and its transformative potential
Jain, Megha Verma, Ravi Kumar, Sunil Kumar, Gyanendra Chamola, Vinay Guizani, Mohsen
Jain, Megha
Verma, Ravi
Kumar, Sunil
Kumar, Gyanendra
Chamola, Vinay
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Journal article
Date
2025
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English
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Abstract
Sixth-generation (6G) technology signifies a major leap in mobile communications, offering ultra-reliable, low-latency, and high-throughput connectivity. This review investigates the foundational technologies underpinning 6G, including Terahertz (THz) communication and ultra-massive multiple input, multiple output (MIMO), and explores their capabilities in enabling high-speed, consistent, and scalable communication infrastructures. A key focus of this study is the application of machine learning (ML) and deep learning (DL) in optimizing network slicing and addressing security challenges within 6G networks. While network slicing allows for flexible, service-specific logical network partitions, it also introduces technical challenges such as dynamic resource allocation, secure slice isolation, and real-time threat detection. To mitigate these, we assess ML-driven approaches—including reinforcement learning (RL), federated learning, and anomaly detection models—that facilitate intelligent orchestration and adaptive security. Furthermore, we highlight practical deployment barriers such as data privacy concerns, computational limitations at the edge, and the need for interpretable models and standardization. This comprehensive review provides insights into the current state, challenges, and potential solutions for integrating ML-based mechanisms to enhance the efficiency, scalability, and security of next-generation communication systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Citation
M. Jain, R. Verma, S. Kumar, G. Kumar, V. Chamola, and M. Guizani, “Unveiling the future: A comprehensive analysis of 6G technology and its transformative potential,” Telecommunication Systems 2025 88:3, vol. 88, no. 3, pp. 1–38, Jun. 2025, doi: 10.1007/S11235-025-01323-W.
Source
Telecommunication Systems
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Keywords
6G Network, Deep Learning, Machine Learning Techniques, Network Security, Network Slicing, Telecommunication Infrastructure
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Springer Nature