Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing
cs.LG, eess.SP
Submitted: 2026-08-11
Updated: 2026-08-11
Comments: Accepted at EUSIPCO 2026
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: 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.
Terminology
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.
Sources
- Integrated Sensing and Communications Over the Years: An Evolution Perspective
- InverTwin: Solving Inverse Problems via Differentiable Radio Frequency Digital Twin
- RayLoc: Wireless Indoor Localization via Fully Differentiable Ray-tracing
- Efficiently Modeling Long Sequences with Structured State Spaces
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