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

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.3939-3954
Abstract
To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate
the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML... (More)
To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate
the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
artificial intelligence, machine learning, clustering and tracking, parameter estimation, propagation channel
in
IEEE Transactions on Antennas and Propagation
volume
70
issue
6
pages
3939 - 3954
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85124825629
ISSN
0018-926X
DOI
10.48550/arXiv.2111.12227
language
English
LU publication?
yes
id
73f63ccd-39df-4868-907a-6a0bfb60b13c
date added to LUP
2021-10-31 15:55:19
date last changed
2022-06-17 09:08:29
@article{73f63ccd-39df-4868-907a-6a0bfb60b13c,
  abstract     = {{To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate<br/>the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper.}},
  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; clustering and tracking; parameter estimation; propagation channel}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{3939--3954}},
  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 I: Channel characterization and antenna-channel optimization}},
  url          = {{http://dx.doi.org/10.48550/arXiv.2111.12227}},
  doi          = {{10.48550/arXiv.2111.12227}},
  volume       = {{70}},
  year         = {{2022}},
}