Deep Learning Models for Time-Series Forecasting of RF-EMF in Wireless Networks-Reference-Cited by-同舟云学术

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Deep Learning Models for Time-Series Forecasting of RF-EMF in Wireless Networks

Published:2024 Issue: Volume:5 Page:1399-1414
ISSN:2644-125X
Container-title:IEEE Open Journal of the Communications Society
language:
Short-container-title:IEEE Open J. Commun. Soc.

Author:

Nguyen Chi¹,Cheema Adnan Ahmad¹^ORCID,Kurnaz Cetin²^ORCID,Rahimian Ardavan¹^ORCID,Brennan Conor³^ORCID,Duong Trung Q.⁴^ORCID

Affiliation:

1. SenComm Research Lab, School of Engineering, Ulster University, Belfast, U.K.

2. Department of Electrical and Electronics Engineering, Ondokuz Mayis University, Samsun, Turkey

3. School of Electronic Engineering, Dublin City University, Dublin 9, Ireland

4. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, U.K.

Funder

Canada Excellence Research Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8782661/10362961/10433563.pdf?arnumber=10433563

Reference28 articles.

1. 6G: A comprehensive survey on technologies, applications, challenges, and research problems

2. Explainable eAI for B5G/6G: Technical aspects, use cases, and research challenges;Liyanage;arXiv:2112.04698,2021

3. Exposure to RF EMF From Array Antennas in 5G Mobile Communication Equipment

4. Multi-band wireless networks: Architectures, challenges, and comparative analysis;Saeidi;arXiv:2212.07606,2022

5. Accurately assessing exposure to radio frequency electromagnetic fields from 5G networks,2021

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