Digital shielding for cross-domain wi-fi signal adaptation using relativistic average generative adversarial network

Wi-Fi sensing exploits radio-frequency signals emitted by Wi-Fi devices to analyze environments, enabling tasks such as people tracking, intruder detection, and gesture recognition. Its growing diffusion is driven by the IEEE 802.11bf standard, which facilitates environmental monitoring, and the increasing demand for tools capable of penetrating obstacles while preserving privacy. However, the performance of Wi-Fi-based sensing solutions is influenced by the environment in which signals are acquired. This is critical when extracting spatial and temporal information from the surrounding scene, as such data reflect both environmental structure and interference sources. A main challenge is achieving generalization across domains, that is, ensuring consistent performance under varying conditions, such as different rooms or buildings, without significant accuracy loss. This paper presents a deep model for domain adaptation of Wi-Fi signals by simulating a digital shielding mechanism. The model is based on a Relativistic average Generative Adversarial Network (RaGAN), which mimics physical shielding to suppress domain-specific features while preserving signal integrity. Both the generator and discriminator use Bidirectional Long Short-Term Memory (Bi-LSTM) architectures, enabling modeling of waveform and time-dimension signal characteristics. To support training, an acrylic box lined with electromagnetic shielding fabric, replicating a Faraday cage, was constructed. Spectra from same-sized objects made of different materials were acquired both inside (domain-free) and outside (domain-dependent) the box. A multi-class Support Vector Machine (SVM), trained on shielded spectra and tested on RaGAN-denoised data, achieved 96 percent accuracy. The SVM also distinguished materials, suggesting a promising approach for security systems aimed at identifying the nature and composition of potentially dangerous objects.