Machine Learning for Radio Resource Management and Optimization in 5G and Beyond

1. Auflage 2025
ISBN: 978-1-032-84473-2
Verlag: CRC Press

Buch, Englisch, 248 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 543 g

Machine Learning for Radio Resource Management and Optimization in 5G and Beyond highlights a new line of research that uses innovative technologies and methods based on artificial intelligence/machine learning techniques to address issues and challenges related to radio resource management in 5G and 6G communication systems. This book provides comprehensive coverage of current and emerging waveform design, channel modeling, multiple access, random access, scheduling, network slicing, and resource optimization for 5G wireless networks and beyond.

This book is suitable for researchers, scholars, and industry professionals working in different fields related to mobile networks and intelligent systems. Additionally, it serves as a hands-on resource for students interested in the fields of cellular networks (5G/6G) and computational intelligence.

Zielgruppe

Postgraduate

Autoren/Hrsg.

Ouaissa, Mariyam

Ouaissa, Mariya

Lamaazi, Hanane

Slimani, Khadija

Khan, Ihtiram Raza

Sundaravadivazhagan, B.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Drahtlostechnologie

Inhaltsverzeichnis

1. Fundamentals of 5G and Beyond Networks. 2. Optimizing Resource Allocation in Intelligent Communication Networks: Fundamentals and Challenges. 3. Radio Resource Management for M2m Communications in Cellular Networks. 4. Integrating Blockchain for Secure and Efficient Radio Resource Management in 5G and Beyond Networks. 5. Federated Learning for Intelligent Network Management in 5G. 6. Non-orthogonal Multiple Access Wireless Systems using Deep Learning. 7. Advancements in Machine Learning Techniques for Optimization of Massive MIMO Design. 8. Predictive Modeling of Household Power Consumption using Machine Learning and Meta-Heuristic Optimization Technique. 9. Intelligent Reinforcement Learning-based Scheduling in 5G Networks and Beyond. 10. AR/VR-based Object Detection for Blind People using 5G Communication. 11. Exploring Sentiment Patterns in Social Media Networks: The Impact of AI, Deep Learning, and Large Models in the 5G Landscape. 12. 5G and AI-based Data Fusion in Intelligent Networks.

Über Autor(innen)

Mariyam Ouaissa is currently an Assistant Professor in Networks and Systems at ENSA, Chouaib Doukkali University, El Jadida, Morocco. She earned her Ph.D. in 2019 from National Graduate School of Arts and Crafts, Meknes, Morocco and her Engineering Degree in 2013 from the National School of Applied Sciences, Khouribga, Morocco. She is a communication and networking researcher and practitioner with industry and academic experience. Dr Ouaissa's research is multidisciplinary that focuses on Internet of Things, M2M, WSN, vehicular communications and cellular networks, security networks, congestion overload problem and the resource allocation management and access control.

Mariya Ouaissa is currently an Assistant Professor in Cybersecurity and Networks at Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco. She earned her Ph.D. in 2019 in Computer Science and Networks, at the Laboratory of Modelisation of Mathematics and Computer Science from ENSAM-Moulay Ismail University, Meknes, Morocco. She is a Networks and Telecoms Engineer, graduated in 2013 from National School of Applied Sciences Khouribga, Morocco. She is a co-Founder and IT Consultant at IT Support and Consulting Center. She was working for School of Technology of Meknes Morocco as a Visiting Professor from 2013 to 2021.

Hanane Lamaazi received an M.Sc. degree in Networks and Telecommunications from Chouaib Doukkali University and a Ph.D. degree in Network and Computer Sciences from Moulay Ismail University, Morocco, in 2013 and 2018, respectively. She was a Post-doctoral Fellow at the Center on Cyber-Physical Systems at Khalifa University, UAE, from 2019 - 2022. She is an Assistant Professor at the College of Information Technology at UAE University, UAE. Her research interests focus on the Internet of Things (IoT), RPL Routing protocol, Edge Computing, Crowd-sensing and Security.

Slimani Khadija earned her Ph.D. in Computer Science from the Faculty of Science at Ibn Tofail University in 2020. During her doctoral studies, she collaborated with University of Technology of Belfort Montbéliard (UTBM), Montbéliard in France to conduct research with a focus on machine learning, deep learning, pattern recognition, and computer vision, specifically applied to academic emotion recognition. Upon the successful completion of her Ph.D. thesis, Dr. Khadija embarked on a postdoctoral journey at the University of Poitiers, assuming the role of a postdoctoral associate. In this capacity, her research revolved around Objects DRI (Detection, Recognition, and Identification), while employing machine learning and deep learning methodologies to enhance video content filtering for optimal security. Her contributions extended to diverse engineering schools in Paris, where she undertook teaching responsibilities across modules spanning data science, deep learning, machine learning, databases, and computer vision. She is currently an Associate Professor at the Graduate School of Automatic Electronic Computing in Paris, France.

Ihtiram Raza Khan is working as senior academician at Jamia Hamdard, Delhi, He has over 26 years of experience and earned his PhD in the field of software engineering and neural networks. His research interests include Software engineering, Computer Graphics, Machine and Deep learning, Big data, Analytics, Cyber security and IOT. He has been actively involved in training and placement activities as Head and has offered consultancies to 15+ companies. He has over 20 International and Indian patents and copyrights against his name. He has written over 20 books and 30 book chapters, 75+ research papers in SCI/Scopus/Springer and peer-reviewed journals.

B. Sundaravadivazhagan is an experienced researcher and educator in the field of Information and Communication Engineering. He has more than 21 years of experience in teaching and research and has earned his Ph.D. in Information and Communication Engineering from Anna University in Chennai in 2016. He is a member of various professional bodies such as IEEE, ISACA, ISTE, and ACM, and has published over 40 research articles in SCI and Scopus journals. He has also served as a resource person, keynote speaker, and advisory committee member in more than 20 international and national conferences. He has received two research grants from the Ministry of Higher Education, Research and Innovation, The Research Council (TRC), Oman. His research interests include IoT, AI and Machine learning, Deep learning, Cloud computing, Networks and security, Wireless networks, and MANET.