Machine Learning Techniques for 6G Wireless Networks

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.

• 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.