Differentiable Wireless Simulation with Geometric Transformers

Thomas Hehn, Markus Peschl, Tribhuvanesh Orekondy, Arash Behboodi, Johann Brehmer

Keywords: Differentiable simulation, inverse problems, learning to simulate, geometric deep learning, equivariance, wireless communication, electromagnetic signals

TL;DR: Neural surrogates for wireless signal propagation based on geometric inductive biases for inverse problems

Abstract:

Modelling the propagation of electromagnetic signals is critical for designing modern communication systems. While there are precise simulators based on ray tracing, they do not lend themselves to solving inverse problems or the integration in an automated design loop. We propose to address these challenges through differentiable neural surrogates that exploit the geometric aspects of the problem. We introduce the Wireless Geometric Algebra Transformer (Wi-GATr), a generic, equivariant backbone architecture for simulating wireless
propagation in a 3D environment. Further, we introduce two datasets of wireless signal propagation in indoor scenes. On these datasets, we show the data-efficiency of our model on signal prediction and applicability to inverse problems based on differentiable predictive modelling.

Submission Number: 31