OT-ADG: Optimal-transport-driven adaptive data generation for WiFi sensing in airborne mobile networks

Airborne Mobile Networks (AMNs) require high-precision situational awareness in enclosed spaces for critical tasks like border surveillance and maritime monitoring. Compared to vision and wearable technologies, WiFi signals in AMN environments are a promising sensing medium due to their non-intrusiveness, cost-effectiveness, and privacy benefits. However, the development of WiFi-based sensing in AMNs is hindered by the scarcity of high-quality large-scale data. While Data Augmentation (DA) can address this scarcity, traditional time-series DA may distort WiFi Channel State Information (CSI)’s time–frequency properties, and deep generative models suffer from mode collapse, spectral distortion, and high computational costs. To overcome these limitations, we propose OT-ADG, an adaptive data generation algorithm based on Optimal Transport (OT) theory to synthesize high-fidelity WiFi sensing samples. It dynamically models subcarrier-specific energy distributions and computes optimal transport plans using Sinkhorn’s algorithm with entropy regularization. Furthermore, we present ViFi, the first large-scale and scenario-rich WiFi sensing dataset to fill the gap in AMN applications, encompassing 26 real-world scenarios with 20640 CSI samples and synchronized videos. Experimental results show OT-ADG’s superior performance, with maximum improvements of up to 14.88% over baseline recognition methods, while outperforming existing DA approaches. Moreover, the robust performance of multiple WiFi-based HAR models validates the effectiveness of the ViFi dataset.