Applications of Machine Learning in Wireless Communications (Telecommunications)

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Applications of Machine Learning in Wireless Communications (Telecommunications)

He, Ruisi (EDT)/ Ding, Zhiguo (EDT)

ウェブストア価格 ¥30,158（本体¥27,417）
Institution of Engineering and Technology（2019/08発売）
外貨定価 US$ 170.00
ポイント 274pt

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製本 Hardcover:ハードカバー版／ページ数 490 p.
言語 ENG
商品コード 9781785616570
DDC分類 621.3820285631

Full 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.

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