3.5 GHz Outdoor Radio Signal Strength Prediction With Machine Learning Based on Low-Cost Geographic Features-Reference-Cited by-同舟云学术

3.5 GHz Outdoor Radio Signal Strength Prediction With Machine Learning Based on Low-Cost Geographic Features

Published:2022-06 Issue:6 Volume:70 Page:4155-4170
ISSN:0018-926X
Container-title:IEEE Transactions on Antennas and Propagation
language:
Short-container-title:IEEE Trans. Antennas Propagat.

Author:

Liu Yaxi¹^ORCID,Dong Jiangbo²,Huangfu Wei¹^ORCID,Liu Jiangchuan³^ORCID,Long Keping¹^ORCID

Affiliation:

1. Beijing Advanced Innovation Center for Materials Genome Engineering and the Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

2. China Mobile Group Design Institute Company Ltd., Beijing, China

3. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Scientific and Technological Innovation Foundation of Shunde Graduate School, University of Science and Technology at Beijing, Beijing, China

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8/9794936/09779409.pdf?arnumber=9779409

Reference36 articles.

1. The indoor radio propagation channel

2. Path Loss Exponent Prediction for Outdoor Millimeter Wave Channels through Deep Learning

3. Machine Learning-Based Methods for Path Loss Prediction in Urban Environment for LTE Networks

4. Path Loss Prediction in Smart Campus Environment: Machine Learning-based Approaches

5. A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

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