Extending Machine Learning Based RF Coverage Predictions to 3D
<|reference_start|>Extending Machine Learning Based RF Coverage Predictions to 3D: 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.<|reference_end|>
@article{chen2024extending,
    title={Extending Machine Learning Based RF Coverage Predictions to 3D},
    author={Muyao Chen, Mathieu Ch^ateauvert, Jonathan Ethier},
    journal={arXiv preprint arXiv:2409.00050},
    year={2024},
    doi={10.1109/AP-S/USNC-URSI47032.2022.9887000},
    archivePrefix={arXiv},
    eprint={2409.00050},
    primaryClass={eess.SP cs.CV cs.IT cs.LG math.IT}
}