"radio propagation" | Laurent Jacques

Fast, differentiable, gpu-accelerated ray tracing for multiple diffraction and reflection paths

Mon, 20 Apr 2026 00:00:00 +0000

Abstract: Ray tracing is a widely used method to model radio wave propagation in complex environments. However, traditional ray tracing tools are often not differentiable, preventing their use in gradient-based optimization and machine learning pipelines. We present a fast, differentiable, GPU-accelerated ray tracing framework supporting multiple diffraction and reflection paths, enabling efficient optimization of radio network parameters. TODO: abstract exact à coller depuis arXiv:2510.16172.

Note: Best Paper Award EuCAP 2026.

Demonstrating DiffeRT: An open-source library for optimizing radio networks with differentiable ray tracing

Mon, 26 May 2025 00:00:00 +0000

Abstract: This proposal presents a demonstration of DiffeRT, an open-source Differentiable Ray Tracing toolbox designed for Machine Learning and optimization applications in the context of radio propagation. Built on the JAX framework for differentiable programming and the Equinox library as a Machine Learning framework, DiffeRT extends its predecessor, DiffeRT2d, to support 3D scenes with enhanced performance and higher-level electromagnetic field computation features. Unlike existing alternatives like Sionna, DiffeRT focuses exclusively on ray-tracing functionalities, offering both low-level and high-level APIs for tailored solutions. This demonstration will showcase DiffeRT’s unique capabilities, highlighting its suitability for a range of Machine Learning and optimization workflows. The tool is accessible via the Python Package Index (PyPI), and its source code can be obtained from its public repository.

DiffeRT2d: A Differentiable Ray Tracing Python Framework for Radio Propagation

Thu, 05 Sep 2024 00:00:00 +0000

Abstract: Ray Tracing (RT) is arguably one of the most prevalent methodologies in the field of radio propagation modeling. However, access to RT software is often constrained by its closed-source nature, licensing costs, or the requirement of high-performance computing resources. While this is typically acceptable for large-scale applications, it can present significant limitations for researchers who require more flexibility in their approach, while working on more simple use cases. We present DiffeRT2d, a 2D Open Source differentiable ray tracer that addresses the aforementioned gaps. DiffeRT2d employs the power of JAX (Bradbury et al., 2024) to provide a simple, fast, and differentiable solution. Our library can be utilized to model complex objects, such as reconfigurable intelligent surfaces, or to solve optimization problems that require tracing the paths between one or more pairs of nodes. Moreover, DiffeRT2d adheres to numerous high-quality Open Source standards, including automated testing, documented code and library, and Python type-hinting.