Radio Propagation Modeling

Radio propagation modeling studies how signals reflect, diffract, scatter, and weaken as they travel through buildings, streets, and other environments. Methods range from empirical path-loss models and computationally intensive ray tracing to learned radio maps, neural channel models, and digital twins that combine geometry with measurements. Current work uses physics-informed networks, differentiable simulation, and generative models to improve prediction while reducing computation and measurement needs. A central challenge is generalizing across environments and hardware configurations while keeping simulated predictions faithful to real-world measurements and downstream network performance.