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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| Published in: | IEEE transactions on antennas and propagation Vol. 70; no. 6; pp. 3955 - 3969 |
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| Main Authors: | , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0018-926X, 1558-2221 |
| Online Access: | Get full text |
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| Abstract | 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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| AbstractList | 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. |
| Author | Lau, Buon Kiong Molisch, Andreas F. Liu, Bo Huang, Chen Zhong, Zhangdui He, Ruisi Wang, Cheng-Xiang Yang, Mi Oestges, Claude Ai, Bo Haneda, Katsuyuki |
| Author_xml | – sequence: 1 givenname: Chen orcidid: 0000-0002-3949-2693 surname: Huang fullname: Huang, Chen email: huangchen@pmlabs.com.cn organization: State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China – sequence: 2 givenname: Ruisi orcidid: 0000-0003-4135-3227 surname: He fullname: He, Ruisi email: ruisi.he@bjtu.edu.cn organization: State Key Laboratory of Rail Traffic Control and Safety and the Key Laboratory of Railway Industry of Broadband Mobile Information Communications, Beijing Jiaotong University, Beijing, China – sequence: 3 givenname: Bo orcidid: 0000-0001-6850-0595 surname: Ai fullname: Ai, Bo email: aibo@ieee.org organization: State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China – sequence: 4 givenname: Andreas F. orcidid: 0000-0002-4779-4763 surname: Molisch fullname: Molisch, Andreas F. email: molisch@usc.edu organization: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA – sequence: 5 givenname: Buon Kiong orcidid: 0000-0002-9203-2629 surname: Lau fullname: Lau, Buon Kiong email: bklau@ieee.org organization: Department of Electrical and Information Technology, Lund University, Lund, Sweden – sequence: 6 givenname: Katsuyuki orcidid: 0000-0002-4778-6405 surname: Haneda fullname: Haneda, Katsuyuki email: katsuyuki.haneda@aalto.fi organization: Department of Radio Science and Engineering, Aalto University, Espoo, Finland – sequence: 7 givenname: Bo orcidid: 0000-0002-3093-4571 surname: Liu fullname: Liu, Bo email: Bo.Liu@glasgow.ac.uk organization: School of Engineering, University of Glasgow, Glasgow, U.K – sequence: 8 givenname: Cheng-Xiang orcidid: 0000-0002-9729-9592 surname: Wang fullname: Wang, Cheng-Xiang email: chxwang@seu.edu.cn organization: Purple Mountain Laboratories, Nanjing, China – sequence: 9 givenname: Mi surname: Yang fullname: Yang, Mi email: myang@bjtu.edu.cn organization: State Key Laboratory of Rail Traffic Control and Safety and the Key Laboratory of Railway Industry of Broadband Mobile Information Communications, Beijing Jiaotong University, Beijing, China – sequence: 10 givenname: Claude orcidid: 0000-0002-0902-4565 surname: Oestges fullname: Oestges, Claude email: claude.oestges@uclouvain.be organization: Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Universite Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium – sequence: 11 givenname: Zhangdui orcidid: 0000-0001-8889-7374 surname: Zhong fullname: Zhong, Zhangdui email: zhdzhong@bjtu.edu.cn organization: State Key Laboratory of Rail Traffic Control and Safety and the Key Laboratory of Railway Industry of Broadband Mobile Information Communications, Beijing Jiaotong University, Beijing, China |
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| SubjectTerms | 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 |
| Title | Artificial Intelligence Enabled Radio Propagation for Communications-Part II: Scenario Identification and Channel Modeling |
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| Volume | 70 |
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