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.

• 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