Application of Machine Learning Algorithms to Path Loss Modeling: A Review-Reference-Cited by-同舟云学术

Application of Machine Learning Algorithms to Path Loss Modeling: A Review

Published:2022-11-01 Issue: Volume: Page:
ISSN:
Container-title:2022 5th Information Technology for Education and Development (ITED)
language:
Short-container-title:

Author:

Abdulkarim Abubakar¹,Faruk Nasir²,Alozie Emmanuel³,Sowande Olugbenga. A.³,Olayinka Imam-Fulani Yusuf³,Usman Aliyu D.⁴,Adewole Kayode S.⁵,Oloyede Abdulkarim A.³,Chiroma Haruna⁶,Garba Salisu⁷,Imoize Agbotiname Lucky⁸,Musa Abdulwaheed⁹,Taura Lawan S.²

Affiliation:

1. Ahmadu Bello University,Department of Electrical Engineering,Nigeria

2. Sule Lamido University,Department of Physics,Nigeria

3. University of Ilorin,Department of Telecommunication Science,Nigeria

4. Ahmadu Bello University,Department of Electronics and Telecommunication Engineering,Nigeria

5. University of Ilorin,Department of Computer Science,Nigeria

6. College of Computer Science and Engineering, University of Hafr Al Batin,Saudi Arabia

7. Sule Lamido University,Department of Computer Science,Nigeria

8. University of Lagos,Department of Electrical and Electronics Engineering,Nigeria

9. Kwara State University,Department of Electrical and Computer Engineering,Nigeria

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10051167/10051171/10051448.pdf?arnumber=10051448

Reference32 articles.

1. Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

2. Performance evaluation of Dynamic Neural Networks for mobile radio path loss prediction

3. Modeling and inferring the attenuation induced by vegetation barriers at 2G/3G/4G cellular bands using Artificial Neural Networks

4. A Multi-wall and Multi-frequency Indoor Path Loss Prediction Model Using Artificial Neural Networks

5. A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Assessing the Capability of Random Forest to Estimate Received Power in LoRaWAN for Agricultural Settings Using Climate Data;2023 33rd International Telecommunication Networks and Applications Conference;2023-11-29

2. Investigation of the Impact of Height and Bandwidth on Path Loss Exponent of 5G FR1 Wireless Networks at 800, 3500, and 5900 MHz;2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS);2023-11-01