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Comparing Differentiable and Dynamic Ray Tracing: Introducing the Multipath Lifetime Map

arXiv 2410.14535 ↗First posted 18 Oct 2024eess.SP cs.LGcited by 0

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Abstract (arXiv)

With the increasing presence of dynamic scenarios, such as Vehicle-to-Vehicle communications, radio propagation modeling tools must adapt to the rapidly changing nature of the radio channel. Recently, both Differentiable and Dynamic Ray Tracing frameworks have emerged to address these challenges. However, there is often confusion about how these approaches differ and which one should be used in specific contexts. In this paper, we provide an overview of these two techniques and a comparative analysis against two state-of-the-art tools: 3DSCAT from UniBo and Sionna from NVIDIA. To provide a more precise characterization of the scope of these methods, we introduce a novel simulation-based metric, the Multipath Lifetime Map, which enables the evaluation of spatial and temporal coherence in radio channels only based on the geometrical description of the environment. Finally, our metrics are evaluated on a classic urban street canyon scenario, yielding similar results to those obtained from measurement campaigns.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 18 Oct 2024.

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References · 4

  1. 2022Sionna: An Open-Source Library for Next-Generation Physical Layer Researchcited by 145
  2. 2023Learning Radio Environments by Differentiable Ray Tracingcited by 23
  3. 2023Min-Path-Tracing: A Diffraction Aware Alternative to Image Method in Ray Tracingcited by 3
  4. 2024Fully Differentiable Ray Tracing via Discontinuity Smoothing for Radio Network Optimizationcited by 2

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