# IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Prediction

arXiv: https://arxiv.org/abs/2501.06414 · HTML: https://mlsanity.com/paper/2501.06414/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Builds on (most cited first, 1 of 1)

- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)

## Built on by (most cited first, 4 of 4)

- [The First Indoor Pathloss Radio Map Prediction Challenge](https://mlsanity.com/paper/2501.13698/) (2025)
- [Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities](https://mlsanity.com/paper/2508.03736/) (2025)
- [Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches](https://mlsanity.com/paper/2604.22056/) (2026)
- [Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction](https://mlsanity.com/paper/2610.19015/) (2026)

## Abstract (arXiv)

In this paper, we propose a generalizable deep neural network model for indoor pathloss radio map prediction (termed as IPP-Net). IPP-Net is based on a UNet architecture and learned from both large-scale ray tracing simulation data and a modified 3GPP indoor hotspot model. The performance of IPP-Net is evaluated in the First Indoor Pathloss Radio Map Prediction Challenge in ICASSP 2025. The evaluation results show that IPP-Net achieves a weighted root mean square error of 9.501 dB on three competition tasks and obtains the second overall ranking.