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Extending Machine Learning Based RF Coverage Predictions to 3D

Signal Processing 2024-09-04 v1 Computer Vision and Pattern Recognition Information Theory Machine Learning math.IT
Authors: Muyao Chen , Mathieu Châteauvert , Jonathan Ethier
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Abstract

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.

Keywords

deep learning for wireless communications millimeter wave communication radio frequency signal processing

Cite

@article{arxiv.2409.00050,
  title  = {Extending Machine Learning Based RF Coverage Predictions to 3D},
  author = {Muyao Chen and Mathieu Châteauvert and Jonathan Ethier},
  journal= {arXiv preprint arXiv:2409.00050},
  year   = {2024}
}

Comments

2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)

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