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arXiv · 2609.26214

Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing

Éloi Sainte-Beuve·Guillaume Larue·Louis-Adrien Dufrène·Quentin Lampin·Ali Al Khansa
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Abstract

We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. The DT acts as a world model, providing geometry, materials, and transmitter/receiver placements to a ray-tracing engine that generates time-indexed channel impulse responses (CIRs) for large numbers of plausible scenes (moving people and objects, layout variants, seasonal/weather conditions, etc). From these synthetic sequences, we train a sequential neural network that maps CIR time series to spatial occupancy estimates, enabling device-free localization (DFL) without instrumented targets. We posit that sensing is best approached as an environment-conditioned learning problem: rather than seeking a single global model, we advocate training or fine-tuning local models specialized to a site-specific DT. As a first experiment, we introduce a novel State Space Model architecture, trained and evaluated across multiple room geometries. The localization performances obtained demonstrate the potential of the approach.

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Éloi Sainte-Beuve, Guillaume Larue, Louis-Adrien Dufrène, Quentin Lampin, Ali Al Khansa. 2026-08-11. Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing. https://arxiv.org/abs/2609.26214

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