Jeffrey A Mundt works at Verizon.
Verizon and NEC Laboratories America turned live telecom fiber into a city-wide sensing network. By repurposing existing optical infrastructure, the system detects vibrations along fiber routes, cutting cable damage incidents and improving response times. The approach also enables AI models that adapt to new, unseen routes without manual labeling.
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 cabledamage 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 proposedsensing and AI framework, demonstrating scalable, telecom-compatible DFOS for real-world infrastructure monitoring and intelligent network operations.
We report the first field study of the phase and polarization dynamics of deployed antiresonant hollow core fiber cable in a data center interconnect for real-world vibration sensing,revealing enhanced phase sensitivity and significantly faster polarization angular rate compared with standard single mode fibers.
We present methods and field trial results demonstrating an integrated distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) system for manhole localization, condition diagnostics, and anomaly detection in pre-deployed telecommunication fiber networks. The proposed system leverages ambient environmental signals, such as vibrational patterns from traffic and day-night temperature fluctuations, and machine learning techniques for automated detection. By combining DAS waterfall traces with temperature measurements from DTS, we achieve improved classification accuracy. Experimental results from three real-world testbeds in Texas and New Jersey show a significant improvement in classification accuracyfrom 78.9% and 89.5% using DAS and DTS alone, respectively, to 94.7% via cross-referenced analysis. We propose a structured prediction formulation for manhole localization based on a U-Net architecture with a gated attention mechanism, where the label of each fiber location in the waterfall image is predicted using both its neighboring context and within-patch discriminative features. The method also supports cross-route generalization for manhole localization and enables condition diagnostics, identifying issues such as cable exposure and water ingress. These results highlight the potential for scalable deployment of fiber sensing solutions for real-time, continuous monitoring of telecom infrastructure.
Vibration sensing based on forward transmission is an emerging topic for network protection and environmental monitoring, especially in long-haul submarine cables and urban fiber networks. However, previous field trials of this approach have mainly focused on localizing strong events under controlled or relatively quiet conditions. In this work, we investigate the capability of forward-transmission vibration sensing to detect weak signals in noisy environments. We demonstrate a high-sensitivity vibration sensing system operating over an 80-km deployed live urban fiber loop without optical amplifiers. The system is enhanced by adaptive time-frequency masking and in-band laser phase noise suppression techniques to improve sensitivity and noise robustness. It has successfully identified and localized weak real-world vibration events with peak-to-peak amplitude lower than 20 rad, such as construction activity near a manhole and even footsteps on handhole lids. Field trial results confirm its robust performance under dynamic environments, including road traffic-induced ground vibrations and aerial cable disturbances. To the best of our knowledge, this is the first demonstration of weak vibration event detection using forward transmission in urban fiber networks. It remarks a significant step towards practical distributed vibration sensing in smart city applications.
Distributed fiber-optic sensing combined with machine learning enables continuous monitoring of telecom infrastructure. We employ generative modeling for event classification, supporting semi supervised learning, uncertainty calibration, and noise resilience. Our approach offers a scalable, data-efficient solution for real-world deployment in complex environments.
We demonstrate real-time fiber risk assessment and dynamic network routing in live metro networks using deployed DASs, satellite imagery, and large-scale AI, achieving the first significantreduction in fiber failures in four years
Publication Date: 4/3/2025 Event: OFC 2025 Reference: Th4C.2: 1-3, 2025 Authors: Jian Fang, NEC Laboratories America, Inc.; Ming-Fang Huang, NEC Laboratories America, Inc.; Scott Kotrla, Verizon; Tiejun J. Xia, Verizon; Glenn A. Wellbrock, Verizon; Jeffrey A Mundt, Verizon; Ting Wang, NEC Laboratories America, Inc.; Yoshiaki Aono, NEC Corporation Abstract: We demonstrated a high-sensitivity forwarding-transmission vibration […]