AI-Augmented OTDR Fault Localization Framework for Resilient Rural Fiber Networks in the United States
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908391671070720 |
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| author | Farabi, Sabab Al |
| author_facet | Farabi, Sabab Al |
| contents | This research presents a novel framework that combines traditional Optical Time-Domain Reflectometer (OTDR) signal analysis with machine learning to localize and classify fiber optic faults in rural broadband infrastructures. The proposed system addresses a critical need in the expansion of middle-mile and last-mile networks, particularly in regions targeted by the U.S. Broadband Equity, Access, and Deployment (BEAD) Program. By enhancing fault diagnosis through a predictive, AI-based model, this work enables proactive network maintenance in low-resource environments. Experimental evaluations using a controlled fiber testbed and synthetic datasets simulating rural network conditions demonstrate that the proposed method significantly improves detection accuracy and reduces false positives compared to conventional thresholding techniques. The solution offers a scalable, field-deployable tool for technicians and ISPs engaged in rural broadband deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03041 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Augmented OTDR Fault Localization Framework for Resilient Rural Fiber Networks in the United States Farabi, Sabab Al Networking and Internet Architecture Signal Processing This research presents a novel framework that combines traditional Optical Time-Domain Reflectometer (OTDR) signal analysis with machine learning to localize and classify fiber optic faults in rural broadband infrastructures. The proposed system addresses a critical need in the expansion of middle-mile and last-mile networks, particularly in regions targeted by the U.S. Broadband Equity, Access, and Deployment (BEAD) Program. By enhancing fault diagnosis through a predictive, AI-based model, this work enables proactive network maintenance in low-resource environments. Experimental evaluations using a controlled fiber testbed and synthetic datasets simulating rural network conditions demonstrate that the proposed method significantly improves detection accuracy and reduces false positives compared to conventional thresholding techniques. The solution offers a scalable, field-deployable tool for technicians and ISPs engaged in rural broadband deployment. |
| title | AI-Augmented OTDR Fault Localization Framework for Resilient Rural Fiber Networks in the United States |
| topic | Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2506.03041 |