{"ID":2841244,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12092","arxiv_id":"2511.12092","title":"SenseRay-3D: Generalizable and Physics-Informed Framework for End-to-End Indoor Propagation Modeling","abstract":"Modeling indoor radio propagation is crucial for wireless network planning and optimization. However, existing approaches often rely on labor-intensive manual modeling of geometry and material properties, resulting in limited scalability and efficiency. To overcome these challenges, this paper presents SenseRay-3D, a generalizable and physics-informed end-to-end framework that predicts three-dimensional (3D) path-loss heatmaps directly from RGB-D scans, thereby eliminating the need for explicit geometry reconstruction or material annotation. The proposed framework builds a sensing-driven voxelized scene representation that jointly encodes occupancy, electromagnetic material characteristics, and transmitter-receiver geometry, which is processed by a SwinUNETR-based neural network to infer environmental path-loss relative to free-space path-loss. A comprehensive synthetic indoor propagation dataset is further developed to validate the framework and to serve as a standardized benchmark for future research. Experimental results show that SenseRay-3D achieves a mean absolute error of 4.27 dB on unseen environments and supports real-time inference at 217 ms per sample, demonstrating its scalability, efficiency, and physical consistency. SenseRay-3D paves a new path for sense-driven, generalizable, and physics-consistent modeling of indoor propagation, marking a major leap beyond our pioneering EM DeepRay framework.","short_abstract":"Modeling indoor radio propagation is crucial for wireless network planning and optimization. However, existing approaches often rely on labor-intensive manual modeling of geometry and material properties, resulting in limited scalability and efficiency. To overcome these challenges, this paper presents SenseRay-3D, a g...","url_abs":"https://arxiv.org/abs/2511.12092","url_pdf":"https://arxiv.org/pdf/2511.12092v1","authors":"[\"Yu Zheng\",\"Kezhi Wang\",\"Wenji Xi\",\"Gang Yu\",\"Jiming Chen\",\"Jie Zhang\"]","published":"2025-11-15T08:17:49Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.NI\"]","methods":"[]","has_code":false}