Yue-Kai Huang is a Senior Researcher in the Optical Networking and Sensing Department at NEC Laboratories America in Princeton, NJ. He received his MS in Electro-Optical Engineering and his BS in Electrical and Electronics Engineering from National Taiwan University. He received his PhD in Electrical and Electronics Engineering from Princeton University, where his doctoral research focused on photonics and high-speed optical communication systems.
At NEC, Dr. Huang’s work advances the field of optical networking and fiber-based sensing systems. His research includes long-distance fiber transmission, optical/RF frontend designs for high-capacity systems, system design for distributed fiber sensing, and optical computation techniques using high-speed photonics. His work on intelligent optical sensor networks, in particular, uses fiber not only as a communication medium but also as a pervasive sensing platform. These innovations enable real-time monitoring of critical infrastructures such as transportation systems, utilities, and data centers. By combining fundamental photonics research with applied system development, Dr. Huang helps drive NEC’s mission to create more resilient, adaptive, and efficient network and sensing solutions.
His contributions result in many of NEC’s products in coherent 100G~400G and DAS sensing solutions and support the integration of advanced optical technologies into large-scale environments, bridging the gap between physical infrastructure and digital intelligence to improve safety, performance, and situational awareness.
We propose a method to compensate the phase offset between samples from different tributaries in time-interleaved phase OTDR using nested phase reference channels. We demonstrate our method for a four-span bidirectional link with high-loss loopback.
NEC Laboratories America’s Optical Networking & Sensing team will participate in OFC 2026 in Los Angeles, March 15–19, contributing to panels, workshops, and courses focused on optical sensing, multicore fibers, and next-generation high-capacity optical communication systems.
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.
Optical link tomography (OLT) is a rapidly evolving field that allows the multi-span, end-to-end visualization of optical power along fiber links in multiple dimensions from network endpoints, solely by processing signals received at coherent receivers. This paper has two objectives: (1) to report the first field trial of OLT, using a commercial transponder under standard DWDM transmission, and (2) to extend its capability to visualize across 4D (distance, time, frequency, and polarization), allowing for locating and measuring multiple QoT degradation causes, including time-varying power anomalies, spectral anomalies, and excessive polarization dependent loss. We also address a critical aspect of OLT, i.e., its need for high fiber launch power, by improving power profile signal-to-noise ratio through averaging across all available dimensions. Consequently, multiple loss anomalies in a field-deployed link are observed even at launch power lower than the system-optimal level. The applications and use cases of OLT from network commissioning to provisioning and operation for current and near-term network scenarios are also discussed.
Eric Blow of NEC Labs will address how machine-learning methods applied to distributed acoustic-sensing data can monitor facility perimeters and detect intrusion via walk, dig, or drive events over buried optical fibre—for example achieving ~90% classification accuracy.
We experimentally investigated variable spectral loading in an OMS, identifying performance under best and worst transmission conditions. Metrics and data visualization allowed correlation between channel configurations and OSNR variations, enabling the derivation of a simple spectrum allocation rule.
We introduce anchor vectors to monitor Rayleigh-backscattering variability in a fiber-optic computing system that performs nonlinear random projection for image classification. With a ~0.4-s calibration interval, system stability can be maintained with a linear decoder, achieving an average accuracy of 80%-90%.
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 jointly estimate the phase noise power spectral densities of multiple lasers using interferometry between different combinations of laser pairs. We demonstrate a beat-frequency trackingmethod that allows under-sampling of interferometric products without phase jumps.
This paper outlines QoT-driven optimization strategies in coherent fiber-optic WDM networks, addressing distinct transmission scenarios, QoT metrics, control-plane methodologies, and emerging trends to enhance network reliability, flexibility and capacity.
