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.

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

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.

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

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.

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