arXiv (All)
open access
Digital Twin-Guided Wireless Localization via Probabilistic Inverse Learning
This work investigates how site-specific digital twins (DTs) can support wireless localization using simplified distributed infrastructure. We propose PRISM (Probabilistic Refinement using Inverse learning and a Site-specific Model), a DT-guided probabilistic localization scheme with single-antenna
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Ray-Traced Digital Twin for 5G NR Positioning with OpenAirInterface#1297490
- An Efficient Uplink 3D AoA Positioning Framework for 5G RedCap UEs in Indoor Factory Environments.#364630
- Fidelity Where it Matters: Site-Specific Nonuniform Refinement for Wireless Digital Twins#1248430
- HalifaxDT: A Wireless Digital Twin from Open Geospatial Data#667968
- Radio Map Construction with Post-Hoc Location Calibration under Quasi-Static Positioning Errors: Joint Estimation, Performance Bounds, and GNSS-Based Evaluation#998694
Record · ID 1468509
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive