Machine Learning Techniques for 6G Wireless Networks

1. Auflage 2027
ISBN: 978-1-041-11269-3
Verlag: CRC Press

Buch, Englisch, 536 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Smart Engineering Systems: Design and Applications

The text explores the fundamental concepts of machine learning in the context of wireless communications, emphasizing its potential to unlock new functionalities and efficiencies in 6G networks.  • Covers a wide range of topics essential to understanding and leveraging machine learning in 6G networks, including supervised and unsupervised learning, reinforcement learning, and deep learning techniques.

• Discusses spectrum management, resource allocation, and intelligent beamforming, tailored to the unique challenges and opportunities presented by next-generation wireless technologies.

• Addresses strategies for overcoming obstacles such as privacy concerns, computational complexity, and scalability issues when integrating machine learning into 6G networks.

• Explains artificial intelligence quality of service management in 6G networks, edge computing, and machine learning integration in 6G wireless networks.

• Highlights how deep learning outperforms traditional machine learning in optimizing 6G through advanced feature extraction and adaptability.

This text is primarily intended for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, telecommunication, communication system design, computer science, and engineering.

Zielgruppe

Academic, Postgraduate, and Undergraduate Advanced

Autoren/Hrsg.

Mamodiya, Udit

Yadav, Kusum

Tripathi, Suman Lata

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Mathematik | Informatik EDV | Informatik Technische Informatik

Inhaltsverzeichnis

1. Introduction to Machine Learning in 6G Wireless Networks. 2. Analysis of Machine Learning Approach for Position Estimation in Mobile Applications. 3. MACHINE LEARNING TECHNIQUES FOR SPECTRUM MANAGEMENT IN 6G NETWORKS. 4. AI-Driven Dynamic Spectrum Allocation and Optimization for Next-Generation 6G Wireless Networks. 5. Machine Learning Approach for Adaptive Beamforming in 6G Wireless Systems. 6. Privacy and Security Challenges in AI-based 6G Communication Systems. 7. Machine Learning-Driven Mobility Optimization and Seamless Handover Management in 6G Wireless Networks. 8. Machine Learning Techniques for 6G Wireless Networks. 9. Harnessing Deep Learning for Adaptive Slice Lifecycle Management and Orchestration in Next-Generation 6G Wireless Networks. 10. Regulatory and Ethical Considerations in ML-Driven 6G Networks. 11. Designing Cost-Effective and Scalable Machine Learning Solutions for 6G Networks in Developing Regions. 12. Machine Learning for 6G: An Interdisciplinary Systems Perspective. 13. Logistic Regression-based Detection of Multi-Class DDoS Attacks in SDN Environments Using Flow-Level Features for 6G Wireless Networks. 14. Deep Learning for 6G-enabled IoT and Massive Machine-Type Communications. 15. Machine Learning-Driven Channel Modelling and Prediction for Multimodal Subsea Communication Networks. 16. AI-based Quality of Service Management for 5G and 6G Networks. 17. A Framework for Trustworthy AI Governance in Machine Learning-Driven 6G Wireless Networks. 18. Generative AI and Federated Intelligence for AI-Native 6G Networks:Applications, Challenges, and Future Research Directions.

Über Autor(innen)

Udit Mamodiya is working as an Associate Professor and Associate Dean (Research) at Poornima University, Jaipur, Rajasthan, India. His research interests include renewable energy sources, reliability analysis, expert systems, wireless networks, sensors, and decision support systems. He has authored over 50 papers (SCI, Scopus, and UGC Care). He also has 50 utility patents (national and international) and 20 design patents and copyrights.

Kusum Yadav is an Associate Professor at the University of Hail, Kingdom of Saudi Arabia. She has more than fourteen years of teaching and research experience. Her areas of research interest include the Internet of Things, Blockchain, Machine Learning, and Artificial Intelligence. She has published more than sixty-five research papers in journals of international repute, including SCI and Scopus. She has also presented more than 25 research articles in various national, international, and IEEE conferences.

Suman Lata Tripathi is associated with the Lovely Professional University, Punjab, as a Professor with more than nineteen years of experience in academics. She has published more than one hundred and forty-one research papers in refereed IEEE, Springer, Elsevier, and IOP science journals and conferences. She has also published 13 Indian patents and 2 copyrights. Her area of expertise includes microelectronics device modeling and characterization, low-power VLSI circuit design, VLSI design of testing, advanced FET design for the Internet of Things, embedded system design, and biomedical applications.