NEC Corporation is a global leader in IT and network technologies, providing advanced solutions in AI, biometrics, smart cities, and communications. It drives innovation for social value creation and infrastructure resilience. As part of the broader NEC family, NECLA frequently collaborates with NEC Corporation on next-generation networking, AI, and secure computing systems. Our joint efforts span fundamental research to real-world deployments, including innovations in optical networks, data science platforms, and trusted AI frameworks. Please read about our latest news and collaborative publications with NEC Corporation.

Posts

Phase-noise Tolerant Per-span Phase and Polarization Sensing

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Subsea cables include a supervisory system that monitors the health of the amplifier pumps and fiber loss on per span basis. In some of the cables, the monitoring is achieved optically and passively using high-loss loop back paths and wavelength selective reflectors. By sending monitoring pulses through the supervisory channel and comparing the phases and polarizations of the returning pulses reflected by consecutive reflectors, dynamic disturbances affecting individual spans can be monitored on a per span basis. Such per-span phase monitoring techniques require high phase coherence compared to DAS systems since the spans are 10s of kms long compared to typical DAS resolution of meters. A time-frequency spread technique was demonstrated to limit the coherence length requirement, however the limits of its effectiveness was not quantified. In this paper we present a detailed analysis of the trade-off between implementation complexity and the phase noise tolerance for given span length by lab experiments.

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GFF-Agnostic Black Box Gain Model for non-Flat Input Spectrum

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We present a simple and accurate semi-analytical model predicting the gain of a single-stage erbium-doped fiber amplifier (EDFA) embedded with an unknown gain flattening filter (GFF). Characteristic wavelength-dependent gain coefficients and their scaling laws are extracted with a limited set of simple flat input spectrum measurements at variable temperatures and pump powers. Based on a black box approach, the proposed model provides a precise gain profile estimation of GFF-embedded EDFA for non-flat input spectra in variable temperature and pump power conditions. The accuracy of the presented methodology is validated on an extensive experimental dataset and compared with state-of-the-art gain models based on semi-analytic and solutions.

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Optical Flow Processing for Chirp-Pulse Coherent OTDR

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We propose a novel optical flow processing technique for distributed temperature and strain sensing with the chirped-pulse coherent OTDR. Unlike conventional 1-dimensional cross-correlation methods, the technique treats the 2-dimensional waterfall data as sequential video frames, estimating local shifts through optical flow. The weighted least square approach with adaptive window size enables pixel-level optical flow calculation, providing accurate local shifts via accumulative tracks with enhanced spatial resolution. Preliminary experimental results over 20km fiber demonstrate its effectiveness for dynamic temperature and strain sensing, addressing limitations of traditional methods and improving sensing capabilities.

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