pith:2TZZKM3I
Advancing Network Digital Twin Framework for Generating Realistic Datasets
An open framework combines vehicle mobility models, site-specific ray tracing, and network simulation to generate realistic wireless datasets for machine learning.
arxiv:2604.12888 v2 · 2026-04-14 · cs.NI · eess.SP
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\pithnumber{2TZZKM3IG777QGFURNKMUQMDEC}
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Claims
we present an open and user-friendly NDT framework that integrates controllable vehicular mobility with the site-specific ray tracer Sionna and the discrete-event ns-3 network simulator, enabling virtualized end-to-end modeling of wireless networks across the radio, network, and application layers.
That the combined mobility-ray-tracing-network simulation produces data sufficiently close to real-world measurements to be useful for training and validating machine-learning algorithms without additional calibration or validation against field data.
An open integration of vehicular mobility, Sionna ray tracing, and ns-3 produces realistic cross-layer datasets for vehicular wireless networks and is released with code and example data.
Receipt and verification
| First computed | 2026-06-04T01:08:50.106957Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
d4f395336837fff818b48b54ca418320a74ce969928d1c7d0851c6c4d4134f82
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/2TZZKM3IG777QGFURNKMUQMDEC \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d4f395336837fff818b48b54ca418320a74ce969928d1c7d0851c6c4d4134f82
Canonical record JSON
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