Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning-Reference-Cited by-同舟云学术

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Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning

Published:2024-06 Issue: Volume:22 Page:102289
ISSN:2590-1230
Container-title:Results in Engineering
language:en
Short-container-title:Results in Engineering

Author:

Afape Johnson O.,Willoughby Alexander A.,Sanyaolu Modupe E.,Obiyemi Obiseye O.,Moloi Katleho,Jooda Janet O.,Dairo Oluropo F.^ORCID

Publisher

Elsevier BV

Reference35 articles.

1. Millimeter wave communications for future Mobile networks;Xiao;IEEE J. Sel. Area. Commun.,2017

2. A comprehensive survey on millimeter wave communications for fifth-generation wireless networks: Feasibility and challenges;Uwaechia;IEEE Access,2020

3. Some tropical rainfall rate characteristics at Ile-Ife for microwave and millimeter wave applications;Ajayi;J. Clim. Appl. Meteorol.,1984

4. 5G 3GPP-like channel models for outdoor urban microcellular and macrocellular environments;Haneda,2016

5. Propagation path loss models for 5G urban micro-and macro-cellular scenarios;Sun,2016

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. Integration of 5G, 6G and IoT with Low Earth Orbit (LEO) networks: Opportunity, challenges and future trends;Results in Engineering;2024-09

2. A novel pathloss prediction and optimization approach using deep learning in millimeter wave communication systems;e-Prime - Advances in Electrical Engineering, Electronics and Energy;2024-09

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