Description
Book SynopsisThis book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, security, and social networking), and provides solutions using various data-driven techniques, including machine learning, deep learning, federated learning, and artificial intelligence.
This book details wireless communication problems that can be solved by data-driven solutions. It presents a generalized approach toward solving problems using specific data-driven techniques. The book also develops a taxonomy of problems according to the type of solution presented and includes several case studies that examine data-driven solutions for issues such as quality of service (QoS) in heterogeneous wireless networks, 5G/6G networks, and security in wireless networks.
The target audience of this book includes professionals, researchers, professors, and students working in the field of networking, communication
Table of Contents
Part 1: Data-Driven Wireless Networks: Design and Applications
Chapter 1: Data-Driven Wireless Networks: A Perspective
Chapter 2: A Collaborative Data-Driven Intelligence for Future Wireless Networks
Chapter 3: Federated learning Technique in Enabling Data-driven Design for Wireless Communication
Chapter 4: Application of Wireless Network Data Driver using Edge Computing and Deep Learning in Intelligent Transportation
Chapter 5: Data-Driven Agriculture and the Role of AI in Smart Farming
Part II: Data-Driven Techniques and Security Issues in Wireless Networks
Chapter 6: Data-Driven Techniques and Security Issues in Wireless Network
Chapter 7: Data-Driven Techniques for Intrusion Detection in Wireless Networks
Part III: Advanced Topics in Data-Driven Intelligence for Wireless Networks
Chapter 8: Policy-based Data Analytic for Software-Defined Wireless
Chapter 9: Data-Driven Coexistence in Next-Generation Heterogeneous Cellular Networks
Chapter 10: Programming Languages, Tools, and Technique
