Artificial Intelligence Enabled Radio Propagation for Communications-Part II: Scenario Identification and Channel Modeling

This two-part paper investigates the application of artificial intelligence (AI) and, in particular, machine learning (ML) to the study of wireless propagation channels. In Part I of this article, we introduced AI and ML and provided a comprehensive survey on ML-enabled channel characterization and...

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Veröffentlicht in:	IEEE transactions on antennas and propagation Jg. 70; H. 6; S. 3955 - 3969
Hauptverfasser:	Huang, Chen, He, Ruisi, Ai, Bo, Molisch, Andreas F., Lau, Buon Kiong, Haneda, Katsuyuki, Liu, Bo, Wang, Cheng-Xiang, Yang, Mi, Oestges, Claude, Zhong, Zhangdui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial intelligence Artificial intelligence (AI) channel modeling channel prediction Data processing Decision trees Laboratories Literature reviews Machine learning machine learning (ML) Modelling Optimization Propagation Radio transmission Random forests scenario identification State-of-the-art reviews Support vector machines Testing Training Wireless communication
ISSN:	0018-926X, 1558-2221
Online-Zugang:	Volltext
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