# GLIP: Electromagnetic Field Exposure Map Completion by Deep Generative Networks

arXiv: https://arxiv.org/abs/2405.03384 · HTML: https://mlsanity.com/paper/2405.03384/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Data-Driven Spectrum Cartography via Deep Completion Autoencoders](https://mlsanity.com/paper/1911.12810/) (2019)

## Built on by (most cited first, 2 of 2)

- [Comparative analysis of Realistic EMF Exposure Estimation from Low Density Sensor Network by Finite & Infinite Neural Networks](https://mlsanity.com/paper/2504.07990/) (2025)
- [Channel Estimation by Infinite Width Convolutional Networks](https://mlsanity.com/paper/2504.08660/) (2025)

## Abstract (arXiv)

In Spectrum cartography (SC), the generation of exposure maps for radio frequency electromagnetic fields (RF-EMF) spans dimensions of frequency, space, and time, which relies on a sparse collection of sensor data, posing a challenging ill-posed inverse problem. Cartography methods based on models integrate designed priors, such as sparsity and low-rank structures, to refine the solution of this inverse problem. In our previous work, EMF exposure map reconstruction was achieved by Generative Adversarial Networks (GANs) where physical laws or structural constraints were employed as a prior, but they require a large amount of labeled data or simulated full maps for training to produce efficient results. In this paper, we present a method to reconstruct EMF exposure maps using only the generator network in GANs which does not require explicit training, thus overcoming the limitations of GANs, such as using reference full exposure maps. This approach uses a prior from sensor data as Local Image Prior (LIP) captured by deep convolutional generative networks independent of learning the network parameters from images in an urban environment. Experimental results show that, even when only sparse sensor data are available, our method can produce accurate estimates.