Poster: GlueRT: Bridging the Gap Between Ray-tracing and Real-World Wireless Channel

Despite ray-tracing's theoretical foundation in radio frequency propagation modeling, significant discrepancies persist between simulated and measured channel characteristics in real environments. This paper presents GlueRT, a hybrid framework leveraging ray-tracing's physical interpretability while using a targeted neural network to correct simulation-reality gaps. GlueRT applies neural network-generated corrections to both amplitude and phase components of ray-tracing channel estimates, maintaining explainability while requiring less training data than pure learning methods. Experiments across various environments demonstrate GlueRT achieves higher channel estimation accuracy than traditional ray-tracing or neural network approaches.