Artificial intelligence enabled radio propagation for communications – Part II: Scenario identification and channel modeling
(2022) In IEEE Transactions on Antennas and Propagation 70(6). p.3955-3969- 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, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review 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-art, the future challenges of AI/ML-based... (More)
- 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, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review 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-art, the future challenges of AI/ML-based channel data processing techniques are given as well. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/0053e93f-a0b0-4ae5-a99f-39512d8d14ce
- author
- organization
- publishing date
- 2022-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artificial intelligence, machine learning, channel modeling, channel prediction, scenario identification
- in
- IEEE Transactions on Antennas and Propagation
- volume
- 70
- issue
- 6
- pages
- 3955 - 3969
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85124834267
- ISSN
- 0018-926X
- DOI
- 10.48550/arXiv.2111.12228
- language
- English
- LU publication?
- yes
- id
- 0053e93f-a0b0-4ae5-a99f-39512d8d14ce
- date added to LUP
- 2021-11-19 10:40:55
- date last changed
- 2022-10-06 09:54:46
@article{0053e93f-a0b0-4ae5-a99f-39512d8d14ce, 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, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review 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-art, the future challenges of AI/ML-based channel data processing techniques are given as well.}}, author = {{Huang, Chen and He, Ruisi and Ai, Bo and Molisch, Andreas F. and Lau, Buon Kiong and Haneda, Katsuyuki and Liu, Bo and Wang, Cheng-Xiang and Yang, Mi and Oestges, Claude and Zhong, Zhangdui}}, issn = {{0018-926X}}, keywords = {{Artificial intelligence; machine learning; channel modeling; channel prediction; scenario identification}}, language = {{eng}}, number = {{6}}, pages = {{3955--3969}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{IEEE Transactions on Antennas and Propagation}}, title = {{Artificial intelligence enabled radio propagation for communications – Part II: Scenario identification and channel modeling}}, url = {{http://dx.doi.org/10.48550/arXiv.2111.12228}}, doi = {{10.48550/arXiv.2111.12228}}, volume = {{70}}, year = {{2022}}, }