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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Bibliographic Details
Published in:IEEE transactions on antennas and propagation Vol. 70; no. 6; pp. 3955 - 3969
Main Authors: 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
Language:English
Published: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 antenna-channel optimization, and in this part (Part II), we review the state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state of the art, the future challenges of AI-/ML-based channel data processing techniques are given as well.
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ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2022.3149665