Welcome everyone, and thank you for being here today. My name is Jérome Eertmans, and I will present my Ph.D. work on differentiable ray tracing for radio propagation modeling.
Let me start by providing a brief context. In wireless communications, understanding how radio waves propagate through complex environments is essential. Ray tracing is a simulation technique that traces individual ray paths from a transmitter to a receiver.
RT models the key electromagnetic interactions: reflection off surfaces, diffraction around edges, and scattering from rough surfaces.
Radio waves travel through complex environments like cities.
RT simulates individual paths between TX and RX.
Each path can undergo multiple interactions.
This enables site-specific channel modeling.
So why differentiable ray tracing? The key idea is to make the entire RT simulation pipeline differentiable, meaning we can compute gradients of any output with respect to any input.
Differentiable RT motivation bullet.
Differentiable RT motivation bullet.
Differentiable RT motivation bullet.
Differentiable RT motivation bullet.
This represents a paradigm shift: from traditional CPU-based non-differentiable RT to GPU-enabled, optimization-ready differentiable RT.
Transition to GPU-enabled, differentiable ray tracing.
These motivations come with several practical challenges, which are the central theme of my thesis.
Challenge bullet.
Challenge bullet.
Challenge bullet.
Challenge bullet.