GeNeRT: A Physics-Informed Approach to Intelligent Wireless Channel Modeling via Generalizable Neural Ray Tracing
arXiv:2506.18295v2 Announce Type: replace Abstract: Neural ray tracing (RT) has emerged as a promising paradigm for channel modeling by integrating physical propagation principles with neural networks. However, existing neural RT methods remain limited by strong spatial dependence and weak adherence to electromagnetic laws. We propose GeNeRT, a generalizable neural RT framework that improves generalization and accuracy through relative geometric features, scatterer semantics, and a Fresnel-inspired polarization-driven architecture. GeNeRT is trained through a three-stage strategy: polarization
The increasing demand for intelligent wireless systems and the limitations of current channel modeling spur the development of physics-informed AI solutions like neural ray tracing.
Improved wireless channel modeling is critical for advancing 6G, IoT, and autonomous systems, directly impacting performance, reliability, and computational efficiency in diverse environments.
Current neural ray tracing methods are becoming more generalizable and physically accurate, moving towards robust, context-aware wireless communication infrastructure rather than specialized solutions.
- · Telecommunications infrastructure providers
- · AI/ML hardware manufacturers
- · Autonomous vehicle developers
- · Wireless device manufacturers
- · Legacy channel modeling software vendors
- · Companies relying on static communication protocols
More efficient and reliable wireless networks due to accurate propagation prediction.
Accelerated development of advanced applications like holographic communication and real-time digital twins.
Enhanced electromagnetic spectrum efficiency leading to new policy and regulatory frameworks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG