Yoshiaki Aono works at NEC Corporation.
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.
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.
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.
In this talk, we will present recent technological advances in fiber sensing applications with long monitoring distances orextending multiple fiber spans. In forward-transmission-based sensing, adaptive beamforming techniques weredemonstrated to achieve multi-event vibration sensing in environments with interference and jamming with significantimprovements in signal reconstruction, noise reduction, and interference rejection from other locations. For sensing oversubmarine cables with many fiber spans with repeaters, it is shown that distributed reflection from Rayleigh scattering canbe detected with sufficient SNR for fiber sensing using HLLB paths. In particular, longitudinal averaging of receivedRayleigh scattered signals can facilitate state-of-polarization-based, multi-span sensing using eigenvalue method.
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.
Optical transmission networks require intelligent traffic adaptation and efficient spectrum usage. We present scalable machine learning (ML) methods for network performance modeling, andfield trials of distributed fiber sensing and classic optical network traffic coexistence.
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
We investigate the growth rate of phase power spectral density in fiber spools in the presence of ambient acoustic noise, observing a complex interplay between spool geometry, shielding effects, and phase cancellation at high acoustic frequencies.
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 […]
Field trials of ambient noise-based automated methods for manhole localization and condition diagnostics using a real-time DAS/DTS integrated system were conducted. Crossreferencingmultiple sensing data resulted in a 94.7% detection rate and enhanced anomaly identification.
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