Publisher Description

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks.

• Provides a detailed theoretical foundation of machine learning and computational intelligence algorithms
• Highlights the state of art machine learning-based solutions for communication networks
• Presents video demonstrations and code snippets on each chapter for easy understanding of the concepts
• Discusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networks
• Demonstrates applications of machine learning techniques for optical networks

Table of Contents

1. Various Deep Learning Based Resource Allocation Techniques in Wireless Communication System. 2. Federated Deep Reinforcement Learning-based Resource allocation in Heterogeneous Networks 3. A Comprehensive Overview of Internet of Nano Things (IoNT) in the Next Generation Heterogeneous Networks: Deployment Aspects, Applications, and Challenges 4. Emerging world of Metaverse: An Indian Perspective 5. Intelligent Optical Networks: Challenges, Opportunities, and Applications 6. Machine Learning for Non-Orthogonal Multiple Access 7. Compensating inbound signal strength for radio controlled mobile robots using ANFIS 8. Optimising Wireless Sensor Networks using Machine Learning 9. Machine Learning-Assisted Interference Management in the 6G UAV Networks with Soft Frequency Reuse 10. Computational Intelligence in Communication Networks: Classification, Clustering, Reinforcement Learning, Deep Learning

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