Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Artificial intelligence enabled radio propagation for communications – Part II: Scenario identification and channel modeling

Huang, Chen ; He, Ruisi ; Ai, Bo ; Molisch, Andreas F. ; Lau, Buon Kiong LU ; Haneda, Katsuyuki ; Liu, Bo ; Wang, Cheng-Xiang ; Yang, Mi and Oestges, Claude , et al. (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:
author
; ; ; ; ; ; ; ; and , et al. (More)
; ; ; ; ; ; ; ; ; and (Less)
organization
publishing date
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}},
}