Welcome everyone! Today I will present our work on unified GPU-ready differentiable path tracing for reflection and diffraction sequences.
In recent years, we have observed a number of exciting improvements in radio propagation modeling.
Today, we will focus on the ray tracing techniques.
RT uses path tracing methods based on Fermat's principle to determine the coordinates of each ray path.
Recently, we have differentiable RT has emerged as a powerfull tool for optimization or solving inverse problems, by allowing us to compute the gradient of any output with respect to the input parameters.
In parallel, the use of GPU accelerators is becoming increasingly common.
Even in small scenes, one must trace thousands of paths to capture all the revelant interactions (e.g., reflections, diffractions) that contribute to the received signal. In coverage scenarios, this scale to the number of receiver locations. Using differentiable RT, we could optimize the transmistting antenna location, or perform material calibration. This motivates the need for fast algorithms and GPU acceleration.
So far, we have thus observed a paradigm shift: from...
to...
In practice, such applications pose some implementation challenges:
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