# Generative Adversarial Networks for Recovering Missing Spectral Information

arXiv: https://arxiv.org/abs/1812.04744 · HTML: https://mlsanity.com/paper/1812.04744/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2018.

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

- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness](https://mlsanity.com/paper/2201.01399/) (2022)

## Abstract (arXiv)

Ultra-wideband (UWB) radar systems nowadays typical operate in the low frequency spectrum to achieve penetration capability. However, this spectrum is also shared by many others communication systems, which causes missing information in the frequency bands. To recover this missing spectral information, we propose a generative adversarial network, called SARGAN, that learns the relationship between original and missing band signals by observing these training pairs in a clever way. Initial results shows that this approach is promising in tackling this challenging missing band problem.