Comparing Differentiable and Dynamic Ray Tracing: Introducing the Multipath Lifetime Map
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911139201286144 |
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| author | Eertmans, Jérome Vitucci, Enrico Maria Degli-Esposti, Vittorio Jacques, Laurent Oestges, Claude |
| author_facet | Eertmans, Jérome Vitucci, Enrico Maria Degli-Esposti, Vittorio Jacques, Laurent Oestges, Claude |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14535 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Comparing Differentiable and Dynamic Ray Tracing: Introducing the Multipath Lifetime Map Eertmans, Jérome Vitucci, Enrico Maria Degli-Esposti, Vittorio Jacques, Laurent Oestges, Claude Signal Processing Machine Learning 51-08 D.2.2; D.2.8; D.2.13 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. |
| title | Comparing Differentiable and Dynamic Ray Tracing: Introducing the Multipath Lifetime Map |
| topic | Signal Processing Machine Learning 51-08 D.2.2; D.2.8; D.2.13 |
| url | https://arxiv.org/abs/2410.14535 |