# Digital Shielding for Cross-Domain Wi-Fi Signal Adaptation using Relativistic Average Generative Adversarial Network

arXiv: https://arxiv.org/abs/2504.20568 · HTML: https://mlsanity.com/paper/2504.20568/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 88 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [The relativistic discriminator: a key element missing from standard GAN](https://mlsanity.com/paper/1807.00734/) (2018)
- [Unsupervised Person Re-identification: Clustering and Fine-tuning](https://mlsanity.com/paper/1705.10444/) (2017)
- [Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions](https://mlsanity.com/paper/2005.00065/) (2020)
- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [Seismic Signal Denoising and Decomposition Using Deep Neural Networks](https://mlsanity.com/paper/1811.02695/) (2018)
- [Illumination-Adaptive Person Re-identification](https://mlsanity.com/paper/1905.04525/) (2019)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [Learning Person Re-identification Models from Videos with Weak Supervision](https://mlsanity.com/paper/2007.10631/) (2020)
- [DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise Cleaning](https://mlsanity.com/paper/2308.12084/) (2023)
- [Disentangled Representations for Short-Term and Long-Term Person Re-Identification](https://mlsanity.com/paper/2409.05277/) (2024)

## Built on by (most cited first, 1 of 1)

- [Transformer-Based Person Identification via Wi-Fi CSI Amplitude and Phase Perturbations](https://mlsanity.com/paper/2507.12854/) (2025)

## Abstract (arXiv)

Wi-Fi sensing uses radio-frequency signals from Wi-Fi devices to analyze environments, enabling tasks such as tracking people, detecting intrusions, and recognizing gestures. The rise of this technology is driven by the IEEE 802.11bf standard and growing demand for tools that can ensure privacy and operate through obstacles. However, the performance of Wi-Fi sensing is heavily influenced by environmental conditions, especially when extracting spatial and temporal features from the surrounding scene. A key challenge is achieving robust generalization across domains, ensuring stable performance even when the sensing environment changes significantly. This paper introduces a novel deep learning model for cross-domain adaptation of Wi-Fi signals, inspired by physical signal shielding. The model uses a Relativistic average Generative Adversarial Network (RaGAN) with Bidirectional Long Short-Term Memory (Bi-LSTM) architectures for both the generator and discriminator. To simulate physical shielding, an acrylic box lined with electromagnetic shielding fabric was constructed, mimicking a Faraday cage. Wi-Fi signal spectra were collected from various materials both inside (domain-free) and outside (domain-dependent) the box to train the model. A multi-class Support Vector Machine (SVM) was trained on domain-free spectra and tested on signals denoised by the RaGAN. The system achieved 96% accuracy and demonstrated strong material discrimination capabilities, offering potential for use in security applications to identify concealed objects based on their composition.