Probabilistic and Differentiable Wireless Simulation with Geometric Transformers
Keywords: Wireless communication, electromagnetic signals, inverse problems, learning to simulate, inverse problems, geometric deep learning, equivariance, diffusion models
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 first introduce the Wireless Geometric Algebra Transformer (Wi-GATr), a generic backbone architecture for simulating wireless propagation in a 3D environment. It uses versatile representations based on geometric algebra and is equivariant with respect to E(3), the symmetry group of the underlying physics. Second, we study two algorithmic approaches to signal prediction and inverse problems based on differentiable predictive modelling and diffusion models. We show how these let us predict received power, localize transmitters, and reconstruct the 3D environment from the received signal. Finally, we introduce two large, geometry-focused datasets of wireless signal propagation in indoor scenes. In experiments, we show that our geometry-forward approach achieves higher-fidelity predictions with less
data than various baselines.
Primary Area: Machine learning for other sciences and fields
Submission Number: 11094
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