Applications of Machine Learning in Wireless Communications
Published on 15. August 2019
Book
Hardback
490 pages
978-1-78561-657-0 (ISBN)
Description
Machine learning explores the study and development of algorithms that can learn from and make predictions and decisions based on data. Applications of machine learning in wireless communications have been receiving a lot of attention, especially in the era of big data and IoT, where data mining and data analysis technologies are effective approaches to solving wireless system evaluation and design issues.
This edited book presents current and future developments and trends in wireless communication technologies based on contributions from machine learning and other fields of artificial intelligence, including channel modelling, signal estimation and detection, energy efficiency, cognitive radios, wireless sensor networks, vehicular communications, and wireless multimedia communications. The book is aimed at a readership of researchers, engineers and students working on wireless communications and machine learning, especially those working with big data and artificial intelligence multi-disciplinary fields related to wireless communication technologies.
This edited book presents current and future developments and trends in wireless communication technologies based on contributions from machine learning and other fields of artificial intelligence, including channel modelling, signal estimation and detection, energy efficiency, cognitive radios, wireless sensor networks, vehicular communications, and wireless multimedia communications. The book is aimed at a readership of researchers, engineers and students working on wireless communications and machine learning, especially those working with big data and artificial intelligence multi-disciplinary fields related to wireless communication technologies.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 240 mm
Width: 165 mm
Thickness: 30 mm
Weight
957 gr
ISBN-13
978-1-78561-657-0 (9781785616570)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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.
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.
Content
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
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