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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.08772 |
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| _version_ | 1866915997452075008 |
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| author | Zhou, Zihao Wang, Zhaolin Liu, Yuanwei |
| author_facet | Zhou, Zihao Wang, Zhaolin Liu, Yuanwei |
| contents | Wireless digital twins (WDTs) enable site-specific learning, management, and evaluation in wireless networks. However, constructing and maintaining a high-fidelity WDT over large-scale complex environments can be prohibitively expensive, especially in terms of data acquisition, geometric reconstruction, storage, and ray tracing. To address this issue, a task-oriented nonuniform refinement framework for WDTs is proposed, where limited resources are selectively allocated to the WDT components that matter most to wireless fidelity. Specifically, a unified refinement framework is first developed, which maximizes task-level fidelity under resource constraints through fine-grained component-wise fidelity allocation. This framework is then instantiated for building-level geometry refinement in urban WDTs. It is found that different buildings exhibit highly heterogeneous impacts on wireless fidelity. Motivated by this observation, an ellipsoid-guided selective refinement algorithm (EGSR) is proposed. By jointly considering the relevance of each building to both line-of-sight (LoS) and non-line-of-sight (NLoS) propagation paths, its refinement priority can be estimated using only a low-fidelity WDT. Simulations across multiple urban scenarios show that EGSR can substantially improve radio-map fidelity and preserve beamforming effectiveness by refining only a small subset of buildings. These results demonstrate the potential of task-oriented fidelity allocation as a scalable principle for constructing efficient and performance-aware WDTs, thereby facilitating reliable site-specific learning and optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08772 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fidelity Where it Matters: Site-Specific Nonuniform Refinement for Wireless Digital Twins Zhou, Zihao Wang, Zhaolin Liu, Yuanwei Signal Processing Wireless digital twins (WDTs) enable site-specific learning, management, and evaluation in wireless networks. However, constructing and maintaining a high-fidelity WDT over large-scale complex environments can be prohibitively expensive, especially in terms of data acquisition, geometric reconstruction, storage, and ray tracing. To address this issue, a task-oriented nonuniform refinement framework for WDTs is proposed, where limited resources are selectively allocated to the WDT components that matter most to wireless fidelity. Specifically, a unified refinement framework is first developed, which maximizes task-level fidelity under resource constraints through fine-grained component-wise fidelity allocation. This framework is then instantiated for building-level geometry refinement in urban WDTs. It is found that different buildings exhibit highly heterogeneous impacts on wireless fidelity. Motivated by this observation, an ellipsoid-guided selective refinement algorithm (EGSR) is proposed. By jointly considering the relevance of each building to both line-of-sight (LoS) and non-line-of-sight (NLoS) propagation paths, its refinement priority can be estimated using only a low-fidelity WDT. Simulations across multiple urban scenarios show that EGSR can substantially improve radio-map fidelity and preserve beamforming effectiveness by refining only a small subset of buildings. These results demonstrate the potential of task-oriented fidelity allocation as a scalable principle for constructing efficient and performance-aware WDTs, thereby facilitating reliable site-specific learning and optimization. |
| title | Fidelity Where it Matters: Site-Specific Nonuniform Refinement for Wireless Digital Twins |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.08772 |