This reference text covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and engages in a discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication. It further presents concepts and theories related to high-speed communication systems, which are important in intelligent wireless communications.
Features:
• Discusses machine learning-based network management strategy in wireless systems, and machine learning-inspired big data analytics frameworks for wireless network applications.
• Presents high-speed communication systems, deep learning for wireless networks, security aspects in wireless networks, and decision-making for wireless networks.
• Highlights the importance of using deep reinforcement learning in intelligent wireless networks and deep reinforcement learning-based mobile data offloading frameworks.
• Covers novel network architectures for distributed edge learning, and privacy issues in distributed edge learning.
• Illustrates experimentation and deep learning-based simulations in networking systems, deep learning-based communication frameworks for multiuser detection and sparse channel estimation.
The book is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.
Table of Contents:
Chapter 1. Deep Learning Transformations for Innovating Healthcare in the Health Sector. Chapter 2. Preliminary Study of 6G Networks Signifies A Revolutionary Change in Wireless Communication. Chapter 3. AI Applications, Healthcare, Agriculture, Defence & Medicine. Chapter 4. Artificial intelligence & Machine learning in healthcare: A systematic bibliometric analysis. Chapter 5. Deep Learning and Neural Network in the Stock Market. Chapter 6. Deep Learning Strategies for Advanced Wireless Communication. Chapter 7. A Machine Learning Based Model for Predicting Diabetes Leading to Retinopathy. Chapter 8. Intelligent Wireless Networks: Edge Computing, Sensors, Real-Time Computing, Security, Emerging Applications. Chapter 9. Empowering the Future of Education and Data Science: A Deep Learning Approach to Wireless Networks. Chapter 10. ML Algorithms Supervised/Unsupervised Learning, Application in Diverse Fields Including Networking. Chapter 11. ML in Wireless Networks: Management, Security, Analytics, Virtualization, Sensors, Real Cases
About the Author :
Bharat Bhushan is an Associate Professor at Department of Computer Science and Engineering, Sharda School of Computing Science & Engineering, Sharda University, Greater Noida, India.
Mohd Anas Wajid is an Assistant Professor and Post-Doctoral Research Associate at TEC de Monterrey, Mexico. His research interests include Soft Computing, Machine Learning, Data Science, Information Retrieval, Neutrosophy, and STEM.
Sudhir Kumar Sharma is currently a Professor at Department of Computer Science & Applications, Sharda School of Computing Science & Engineering, Sharda University, Greater Noida, India His research interests include Artificial Intelligence, LLMs and Security.
Achyut Shankar is currently working as an Associate Professor at School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India. His areas of interest include Wireless sensor network, Machine Learning, Internet of things, Block-chain and Cloud computing.
Mithun Chakraborty is currently associated with Darjelling Hill Institute of Technology and Management as Founding Principal. His current research focus is intelligent sensing, communication and computing, joint radar communication based smart vehicle.
Parma Nand is Dean and Pro-VC at School of Computing Science & Engineering, Sharda University, Greater Noida, India. He has expertise in Wireless and Sensor Network, Cryptography, Algorithm and Computer Graphics.
