Applications of Machine Learning in Wireless Communications
Produktdetaljer
Biografisk notat
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.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 detailed and comprehensive reference considers how to combine the disciplines of wireless communications and machine learning. Coverage includes channel modelling, signal estimation and detection, energy efficiency, cognitive radios, wireless sensor networks, vehicular communications and wireless multimedia communications.
- 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