# Extending Machine Learning Based RF Coverage Predictions to 3D

arXiv: https://arxiv.org/abs/2409.00050 · HTML: https://mlsanity.com/paper/2409.00050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [An empirical study on using CNNs for fast radio signal prediction](https://mlsanity.com/paper/2006.09245/) (2020)

## Abstract (arXiv)

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.