arXiv:2603.01655v2 Announce Type: replace 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 interaction order. This bottleneck limits its use in large-scale or real-time applications, forcing traditional tools to rely on heuristics that reduce path candidates at the cost of potentially reduced accuracy. To overcome this limitation, we propose a machine-learning-assisted framework that replaces exhaustive path searching with i
The increasing demand for large-scale and real-time radio propagation modeling in applications like 5G/6G and autonomous systems necessitates more efficient computational methods.
This development addresses a fundamental bottleneck in radio propagation modeling, enabling more accurate and expansive simulations critical for advanced wireless communication and sensing technologies.
Traditional exhaustive ray tracing, limited by computational complexity, can now be augmented or replaced by machine learning approaches, allowing for faster and more scalable radio environment simulations.
- · Telecommunications infrastructure providers
- · Autonomous vehicle developers
- · Machine learning researchers
- · Wireless communication hardware manufacturers
- · Traditional radio propagation software vendors without AI integration
- · Hardware-centric ray tracing solutions
More efficient and accurate radio environment modeling becomes possible for large-scale deployments.
This improved modeling accelerates the development and optimization of next-generation wireless networks and sensing systems.
The enhanced understanding of radio propagation could lead to more robust and pervasive connectivity, facilitating widespread adoption of advanced autonomous systems and IoT.
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Read at arXiv cs.LG