{"paper":{"title":"Advancing Network Digital Twin Framework for Generating Realistic Datasets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"An open framework combines vehicle mobility models, site-specific ray tracing, and network simulation to generate realistic wireless datasets for machine learning.","cross_cats":["eess.SP"],"primary_cat":"cs.NI","authors_text":"Carlo Fischione, G\\'abor Fodor, Oscar Stenhammar, Sundeep Rangan","submitted_at":"2026-04-14T15:37:54Z","abstract_excerpt":"The integration of accurate and reproducible wireless network simulations is a key enabler for research on open, virtualized, and intelligent communication systems. Network Digital Twins (NDTs) provide a scalable alternative to costly and time-consuming measurement campaigns, while enabling controlled experimentation and data generation for data-driven network design. In this paper, 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"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"An open framework combines vehicle mobility models, site-specific ray tracing, and network simulation to generate realistic wireless datasets for machine learning.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"6577a6f4297cab7a94c84acd46b396d8df416f8e7d00eed299dce8604f98eff8"},"source":{"id":"2604.12888","kind":"arxiv","version":2},"verdict":{"id":"9cf79127-c04d-466f-b031-46f4461bf1c8","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T14:09:21.012535Z","strongest_claim":"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.","one_line_summary":"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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"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.","pith_extraction_headline":"An open framework combines vehicle mobility models, site-specific ray tracing, and network simulation to generate realistic wireless datasets for machine learning."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.12888/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}