A Deep Generative Model for Synthesizing Labeled Wireless Signals
A paper introduces Inter-Instance Generative Adversarial Networks (IIns-GAN), a deep learning method to generate realistic labeled wireless signals. The generated signals adapt to different environment scenarios and support model training tasks such as distance estimation and environment identification. Experiments on public Ultra-Wideband (UWB) datasets show the generated signals mirror physical characteristics of real-world measurements and improve model training.
Researchers propose IIns-GAN, a deep generative model that synthesizes labeled wireless signals for wireless sensing applications. The method addresses the high cost of real-world data collection and limitations of traditional environmental-model-based synthesis. Generated signals are realistic and adaptable across environments, and experiments on public UWB datasets demonstrate their utility for training models in distance estimation and environment identification.
IIns-GAN is a GAN-based generative model designed to produce labeled wireless signals with physical realism. It likely conditions generation on environment and label information, enabling cross-scenario adaptation. Evaluation on UWB datasets suggests the model captures signal characteristics relevant to downstream tasks like distance estimation and environment identification.
This research could reduce the need for expensive labeled wireless sensing datasets, accelerating development of wireless sensing applications in areas such as indoor localization, activity recognition, and environmental monitoring. It may lower barriers for companies and researchers lacking access to large real-world datasets.
The method offers potential cost savings in data acquisition for wireless sensing model development. It could enable faster prototyping and deployment of sensing solutions in smart homes, healthcare, and industrial IoT, where labeled wireless data is scarce.
Next observable signals include open-sourcing of the IIns-GAN code or model, application to other wireless signal types (e.g., Wi-Fi, mmWave), and adoption in wireless sensing benchmarks. Further validation on diverse real-world environments would strengthen claims of adaptability.