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Author:   Ruisi He (Professor, Beijing Jiaotong University, State Key Laboratory of Rail Traffic Control and Safety, China) ,  Zhiguo Ding (Professor, University of Manchester, School of Electrical and Electronic Engineering, UK)
Publisher:   Institution of Engineering and Technology
Imprint:   Institution of Engineering and Technology
ISBN:  

9781785616570

ISBN 10:   1785616579
Pages:   490
Publication Date:   15 August 2019
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Chapter 1: Introduction of machine learning Chapter 2: Machine-learning-enabled channel modeling Chapter 3: Channel prediction based on machine-learning algorithms Chapter 4: Machine-learning-based channel estimation Chapter 5: Signal identification in cognitive radios using machine learning Chapter 6: Compressive sensing for wireless sensor networks Chapter 7: Reinforcement learning-based channel sharing in wireless vehicular networks Chapter 8: Machine-learning-based perceptual video coding in wireless multimedia communications Chapter 9: Machine-learning-based saliency detection and its video decoding application in wireless multimedia communications Chapter 10: Deep learning for indoor localization based on bimodal CSI data Chapter 11: Reinforcement-learning-based wireless resource allocation Chapter 12: Q-learning-based power control in small-cell networks Chapter 13: Data-driven vehicular mobility modeling and prediction

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Author Information

Ruisi He is a Professor at the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China. His research interests include measurement and modelling of wireless channels, machine learning and clustering analysis in wireless communications, vehicular and high-speed railway communications, 5G massive MIMO and high frequency communication techniques. Zhiguo Ding is a Professor at the School of Electrical and Electronic Engineering at the University of Manchester, UK. He holds a Ph. D degree in Electrical Engineering from Imperial College London, UK. His research interests are in 5G networks, game theory, cooperative and energy harvesting networks and statistical signal processing.

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