Fully Differentiable Ray Tracing via Discontinuity Smoothing for Radio Network Optimization
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| Format: | Preprint |
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2024
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| _version_ | 1866913334088957952 |
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| author | Eertmans, Jerome Jacques, Laurent Oestges, Claude |
| author_facet | Eertmans, Jerome Jacques, Laurent Oestges, Claude |
| contents | Recently, Differentiable Ray Tracing has been successfully applied in the field of wireless communications for learning radio materials or optimizing the transmitter orientation. However, in the frame of gradient based optimization, obstruction of the rays by objects can cause sudden variations in the related objective functions or create entire regions where the gradient is zero. As these issues can dramatically impact convergence, this paper presents a novel Ray Tracing framework that is fully differentiable with respect to any scene parameter, but also provides a loss function continuous everywhere, thanks to specific local smoothing techniques. Previously non-continuous functions are replaced by a smoothing function, that can be exchanged with any function having similar properties. This function is also configurable via a parameter that determines how smooth the approximation should be. The present method is applied on a basic one-transmitter-multi-receiver scenario, and shows that it can successfully find the optimal solution. As a complementary resource, a 2D Python library, DiffeRT2d, is provided in Open Access, with examples and a comprehensive documentation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_11882 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fully Differentiable Ray Tracing via Discontinuity Smoothing for Radio Network Optimization Eertmans, Jerome Jacques, Laurent Oestges, Claude Signal Processing 51-08 Recently, Differentiable Ray Tracing has been successfully applied in the field of wireless communications for learning radio materials or optimizing the transmitter orientation. However, in the frame of gradient based optimization, obstruction of the rays by objects can cause sudden variations in the related objective functions or create entire regions where the gradient is zero. As these issues can dramatically impact convergence, this paper presents a novel Ray Tracing framework that is fully differentiable with respect to any scene parameter, but also provides a loss function continuous everywhere, thanks to specific local smoothing techniques. Previously non-continuous functions are replaced by a smoothing function, that can be exchanged with any function having similar properties. This function is also configurable via a parameter that determines how smooth the approximation should be. The present method is applied on a basic one-transmitter-multi-receiver scenario, and shows that it can successfully find the optimal solution. As a complementary resource, a 2D Python library, DiffeRT2d, is provided in Open Access, with examples and a comprehensive documentation. |
| title | Fully Differentiable Ray Tracing via Discontinuity Smoothing for Radio Network Optimization |
| topic | Signal Processing 51-08 |
| url | https://arxiv.org/abs/2401.11882 |