Path Loss Model Based on Machine Learning Using Multi-Dimensional Gaussian Process Regression-Reference-Cited by-同舟云学术

Path Loss Model Based on Machine Learning Using Multi-Dimensional Gaussian Process Regression

Published:2022 Issue: Volume:10 Page:115061-115073
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Jang Ki Joung¹^ORCID,Park Sejun¹,Kim Junseok²^ORCID,Yoon Youngkeun²^ORCID,Kim Chung-Sup²,Chong Young-Jun²,Hwang Ganguk¹^ORCID

Affiliation:

1. Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

2. Radio Resource Research Section, Radio and Satellite Research Division, Electronics and Telecommunications Research Institute, Daejeon, South Korea

Funder

Institute for Information and Communications Technology Planning and Evaluation

Korea Government

National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09931712.pdf?arnumber=9931712

Reference52 articles.

1. Deep learning for radio propagation: Using image-driven regression to estimate path loss in urban areas

2. Model-Aided Deep Learning Method for Path Loss Prediction in Mobile Communication Systems at 2.6 GHz

3. Clustering Enabled Wireless Channel Modeling Using Big Data Algorithms

4. Machine-Learning-Based Data Processing Techniques for Vehicle-to-Vehicle Channel Modeling

5. Joint Shadowing Process in Urban Peer-to-Peer Radio Channels

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