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

Chen Huang; Ruisi He; Bo Ai; Andreas F. Molisch; Buon Kiong Lau; Katsuyuki Haneda; Bo Liu; Cheng-Xiang Wang; Mi Yang; Claude Oestges; Zhangdui Zhong

doi:10.48550/arXiv.2111.12228

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

Chen Huang, Ruisi He, Bo Ai, Andreas F. Molisch, Buon Kiong Lau, Katsuyuki Haneda, Bo Liu, Cheng-Xiang Wang, Mi Yang, Claude Oestges, Zhangdui Zhong

Research output: Contribution to journal › Review article › peer-review

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.

Original language	English
Pages (from-to)	3955-3969
Journal	IEEE Transactions on Antennas and Propagation
Volume	70
Issue number	6
DOIs	https://doi.org/10.48550/arXiv.2111.12228 https://doi.org/10.1109/TAP.2022.3149665
Publication status	Published - 2022 Jun

Subject classification (UKÄ)

Communication Systems
Computer Sciences

Free keywords

Artiﬁcial intelligence
machine learning
channel modeling
channel prediction
scenario identiﬁcation

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Huang, C., He, R., Ai, B., Molisch, A. F., Lau, B. K., Haneda, K., Liu, B., Wang, C.-X., Yang, M., Oestges, C., & Zhong, Z. (2022). Artificial intelligence enabled radio propagation for communications – Part II: Scenario identification and channel modeling. IEEE Transactions on Antennas and Propagation, 70(6), 3955-3969. https://doi.org/10.48550/arXiv.2111.12228, https://doi.org/10.1109/TAP.2022.3149665

@article{0053e93fa0b04ae5a99f39512d8d14ce,

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

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.",

keywords = "Artiﬁcial intelligence, machine learning, channel modeling, channel prediction, scenario identiﬁcation",

author = "Chen Huang and Ruisi He and Bo Ai and Molisch, \{Andreas F.\} and Lau, \{Buon Kiong\} and Katsuyuki Haneda and Bo Liu and Cheng-Xiang Wang and Mi Yang and Claude Oestges and Zhangdui Zhong",

year = "2022",

month = jun,

doi = "10.48550/arXiv.2111.12228",

language = "English",

volume = "70",

pages = "3955--3969",

journal = "IEEE Transactions on Antennas and Propagation",

issn = "0018-926X",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

number = "6",

}

Huang, C, He, R, Ai, B, Molisch, AF, Lau, BK, Haneda, K, Liu, B, Wang, C-X, Yang, M, Oestges, C & Zhong, Z 2022, 'Artificial intelligence enabled radio propagation for communications – Part II: Scenario identification and channel modeling', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 3955-3969. https://doi.org/10.48550/arXiv.2111.12228, https://doi.org/10.1109/TAP.2022.3149665

TY - JOUR

T1 - Artificial intelligence enabled radio propagation for communications – Part II: Scenario identification and channel modeling

AU - Huang, Chen

AU - He, Ruisi

AU - Ai, Bo

AU - Molisch, Andreas F.

AU - Lau, Buon Kiong

AU - Haneda, Katsuyuki

AU - Liu, Bo

AU - Wang, Cheng-Xiang

AU - Yang, Mi

AU - Oestges, Claude

AU - Zhong, Zhangdui

PY - 2022/6

Y1 - 2022/6

N2 - 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.

AB - 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.

KW - Artiﬁcial intelligence

KW - machine learning

KW - channel modeling

KW - channel prediction

KW - scenario identiﬁcation

UR - https://www.scopus.com/pages/publications/85124834267

U2 - 10.48550/arXiv.2111.12228

DO - 10.48550/arXiv.2111.12228

M3 - Review article

SN - 0018-926X

VL - 70

SP - 3955

EP - 3969

JO - IEEE Transactions on Antennas and Propagation

JF - IEEE Transactions on Antennas and Propagation

IS - 6

ER -

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

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Artificial intelligence enabled radio propagation for communications – Part I: Channel characterization and antenna-channel optimization

IEEE Transactions on Antennas and Propagation (Journal)

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

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Research output

Artificial intelligence enabled radio propagation for communications – Part I: Channel characterization and antenna-channel optimization

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IEEE Transactions on Antennas and Propagation (Journal)

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