Extending Machine Learning Based RF Coverage Predictions to 3D

Autor:	Chen, Muyao, Châteauvert, Mathieu, Ethier, Jonathan
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Information Theory Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1109/AP-S/USNC-URSI47032.2022.9887000
Popis:	This paper discusses recent advancements made in the fast prediction of signal power in mmWave communications environments. Using machine learning (ML) it is possible to train models that provide power estimates with both good accuracy and with real-time simulation speeds. Work involving improved training data pre-processing as well as 3D predictions with arbitrary transmitter height is discussed. Comment: 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.00050 Zobrazit plný text záznamu View this record from Arxiv