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

TL;DR

This work tackles cross-domain generalization in Wi-Fi sensing by introducing digital shielding, a RaGAN-based denoising framework with Bi-LSTM units that recreates environment-free CSI amplitudes. An acrylic box lined with electromagnetic shielding fabric simulates a Faraday cage to generate shielded data, while unshielded data train the generator to map noisy spectra to their ideal forms. A simple multi-class SVM is used to validate that the denoised spectra preserve material-discriminative features, achieving 96% accuracy on four materials. The approach demonstrates a practical pathway for cross-domain Wi-Fi sensing and material recognition, with potential implications for privacy-preserving sensing and security applications.

Abstract

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.

Figures (6)

• Figure 1: Proposed model architecture for digital shielding. The generator and discriminator are implemented using Bi-LSTM networks to process the sequential data in Wi-Fi signals. In the first stage, the model analyzes the sequence in the forward direction to capture dependencies as the signal progresses over time; in the second, it analyzes the sequence in the backward direction to account for future dependencies. This dual approach enhances comprehension of the temporal relationships within the waveform and time-series data of Wi-Fi signals.
• Figure 2: Amplitudes extracted from the CSI: the top plot shows the acquisition of the copper cube in a real-world environment, while the bottom plot represents the acquisition inside the acrylic box lined with electromagnetic shielding fabric (i.e., Faraday cage).
• Figure 3: Details of the proposed architecture. The first part (left) shows the generator, whose main components are an initial fully connected layer, a Bi-LSTM layer, and a final fully connected layer. The second part (right) shows the discriminator, whose main components are a Bi-LSTM layer and two sequential fully connected layers.
• Figure 4: Acrylic box lined with electromagnetic shielding fabric, designed to replicate the effects of a Faraday cage. The box isolates objects, enabling the RaGAN to learn the impact of physical shielding. The legend shows four cubes made of different materials (acrylic, aluminum, copper, and pine), each with dimensions of $2 \times 2 \times 2$ cm, which were used in the classification task to validate the effectiveness of the proposed denoising method. Two ESP32 devices are positioned within the box, serving as the transmitter and receiver for Wi-Fi signals.
• Figure 5: Amplitudes extracted from the CSI: the plot shows the reconstructed spectrum of the copper cube derived from data acquired in a real-world environment.