arXiv:2507.19653v2 Announce Type: replace-cross Abstract: We study the realism of Sionna v1.0.2 ray-tracing for outdoor cellular links in central Rome. We use a real measurement set of 1,664 user-equipments (UEs) and six nominal base-station (BS) sites. Using these fixed positions we systematically vary the main simulation parameters, including path depth, diffuse/specular/refraction flags, carrier frequency, as well as antenna's properties like its altitude, radiation pattern, and orientation. Simulator fidelity is scored for each base station via Spearman correlation between measured and sim
The proliferation of AI/ML applications in wireless communication necessitates robust simulation tools, making current limitations of ray-tracing a critical area of research.
This research highlights the limitations of current simulation tools for AI-based wireless tasks, identifying a key bottleneck for the effective deployment of learning-based radio frequency (RF) technologies.
A greater understanding of ray-tracing limitations will lead to more realistic simulations and, consequently, better-performing AI models for RF tasks.
- · AI/ML researchers in wireless communication
- · Developers of advanced simulation software
- · Telecommunication companies utilizing AI for network optimization
- · Organizations relying solely on current ray-tracing for RF model validation
Researchers gain nuanced insights into the shortcomings of existing ray-tracing simulations for urban wireless environments.
This will drive the development of next-generation simulation tools and data generation methods for AI-driven RF applications.
Improved simulation fidelity could accelerate the operational deployment of advanced AI-powered wireless networks and spectrum management systems.
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Read at arXiv cs.LG