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This research proposes using 3D digital twins as synthetic data generators to train sequential neural networks for device-free localization via radio sensing. By shifting from a single global model to site-specific environment-conditioned learning, the approach leverages ray-traced channel impulse responses to handle diverse physical conditions without requiring instrumented targets. The introduction of a novel State Space Model architecture demonstrates significant potential for scaling AI-based sensing across varying geometries and environmental factors.
Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing
arXiv:2609.26214v1 Announce Type: new 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.
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.
Machine Learning (cs.LG); Signal Processing (eess.SP)
arXiv:2609.26214 [cs.LG]
(or arXiv:2609.26214v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.26214 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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