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Description
This book covers the basic principles of wireless communication while delving into the fundamentals of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. The authors provide real-world examples and case studies to illustrate the use of machine learning in wireless communication applications such as channel estimation, mobility prediction, resource allocation, and beamforming. This book is an essential resource for researchers, engineers, and students interested in understanding and applying machine learning techniques in the context of wireless communication systems.
Table of Contents
Introduction.- Basic of Wireless Communication and Machine Learning.- Machine Learning Algorithms for Channel Prediction.- Machine Learning Algorithms for Resource Allocation.- Machine Learning Algorithms for Beamforming.- Machine Learning Algorithms for Mobility Prediction.- Practical Example of ML used in Wireless Communication.- Conclusion.
