Artificial intelligence enabled radio propagation for communications – Part I: Channel characterization and antenna-channel optimization
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/73f63ccd-39df-4868-907a-6a0bfb60b13c
- author
- organization
- publishing date
- 2022-06
- 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}}, }