[2603.01655] Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling

Computer Science > Machine Learning arXiv:2603.01655 (cs) [Submitted on 2 Mar 2026] Title:Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling Authors:Jérome Eertmans, Enrico M. Vitucci, Vittorio Degli-Esposti, Nicola Di Cicco, Laurent Jacques, Claude Oestges View a PDF of the paper titled Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling, by J\'erome Eertmans and 5 other authors View PDF Abstract:Ray tracing has become a standard for accurate radio propagation modeling, but suffers from exponential computational complexity, as the number of candidate paths scales with the number of objects raised to the power of the interaction order. This bottleneck limits its use in large-scale or real-time applications, forcing traditional tools to rely on heuristics to reduce the number of path candidates at the cost of potentially reduced accuracy. To overcome this limitation, we propose a comprehensive machine-learning-assisted framework that replaces exhaustive path searching with intelligent sampling via Generative Flow Networks. Applying such generative models to this domain presents significant challenges, particularly sparse rewards due to the rarity of valid paths, which can lead to convergence failures and trivial solutions when evaluating high-order interactions in complex environments. To ensure robust learning and efficient exploration, our framework incorporates three key architectural components...