Leveraging deployed telecom cables for distributed fiber sensing topologies and applications

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Publication Date: 4/1/2026

Event: Journal of Optical Communications and Networking

Reference: 18(4):B72-B84, 2026

Authors: Scott R. Kotrla, Verizon; Ming-Fang Huang, NEC Laboratories America, Inc.; Jian Fang, NEC Laboratories America, Inc.; Shaobo Han, NEC Laboratories America, Inc.; Jamie Lynn, NEC Laboratories America, Inc.; Ezra Ip, NEC Laboratories America, Inc.; Jeffrey A. Mundt, Verizon; Ting Wang, NEC Laboratories America, Inc.

Abstract: Distributed fiber optic sensing (DFOS) has emerged as a promising technology for wide-area monitoring by utilizing existing telecom cables as large-scale sensing media. This paper explores three sensing modalities, backscattering-based sensing, forward-transmission-based sensing, and hybrid sensing, and discusses their respective benefits, challenges, and application domains. Backscattering sensing demonstrates strong potential for applications such as road traffic monitoring, pavement condition assessment, intrusion detection, and cable damage prevention, but is constrained in amplified dense wavelength division multiplexing (DWDM) networks. Forward-transmission sensing enables sensing over operational telecom links with in-line amplification, extending sensing reach, although it involves trade-offs in spatial resolution and localization accuracy.

To address these challenges, a hybrid sensing architecture that integrates backscattering and forward-transmission techniques is introduced, achieving enhanced sensing distance while maintaining high sensitivity and localization performance.In addition, this work incorporates artificial intelligence (AI) through a locally adaptive anomaly detection (LAAD) framework based on self-supervised representation learning. By leveraging location-based pretext tasks and unlabeled data, the proposed AI approach enables efficient adaptation across heterogeneous fiber routes and operational environments, significantly reducing reliance on labeled data while improving cross-domain generalization. Field trials over deployed telecom networks validate the feasibility and effectiveness of the proposed sensing and AI framework, demonstrating scalable, telecom-compatible DFOS for real-world infrastructure monitoring and intelligent network operations.

Publication Link: https://ieeexplore.ieee.org/document/11435207