Geometric Wireless Simulation with Equivariant Transformers

Published: 17 Jun 2024, Last Modified: 11 Jul 2024ICML 2024 Workshop GRaMEveryoneRevisionsBibTeXCC BY 4.0
Track: Extended abstract
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 E(3) 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 inverse problem solving capabilities using differentiable predictive modelling as well as diffusion models.
Submission Number: 36
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