Bridging Data Gaps: Enhancing Wireless Localization with Physics-Informed Data Augmentation

Machine learning (ML) models have emerged as the state-of-the-art approach for wireless applications such as transmitter localization. One of the challenges for ML models, however, is their reliance on the abundance of high quality data. Specifically for localization, a significant challenge is to obtain training data that ''covers'' the entire landscape, in order to ensure a high accuracy for the ML approaches. This issue is compounded when trying to localize multiple transmitters. To address this problem, we introduce a new data augmentation pipeline, termed Physics-informed Augmentation and RF Modeling (PhARMNet), that can combine existing data with a physics-based simulation model, producing a larger dataset with an improved coverage of the landscape of interest. Our results show that PhARMNet offers significant advantages over traditional path loss models. For localization, we demonstrate that augmenting training data with PhARMNet-produced samples improves localization accuracy, particularly in out-of-distribution regions and multi-transmitter settings.