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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 modeling, signal estimation and detection, energy efficiency, and more.
Inhaltsverzeichnis
- 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
Zusammenfassung
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.
