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.
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.
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.
Optical transmission systems require accurate modeling and performance estimation for autonomous adaption and reconfiguration. We present efficient and scalable machine learning (ML) methods for modeling optical networks at component- and network-level with minimizeddata collection.
A differential algorithm is proposed to calibrate the physical digital model of an optical line system from scratch at the commissioning phase, using minimal measurements and maximizing signal and OSNR estimation accuracy.
We implement a cascaded learning framework leveraging three different EDFA and fiber component models for OSNR and GSNR prediction, achieving MAEs of 0.20 and 0.14 dBover a 5-span network under dynamic channel loading.
We experimentally demonstrate real time mode division multiplexing free space optical communication with commercial 400G open and disaggregated transponders. As proof of concept,using HG00, HG10, and HG01 modes, we transmit 1.2 Tb/s/l (3´1l´400Gb/s) error free.
In this work, for the first time, we experimentally demonstrate mode division multiplexing-based bidirectional free space optical communication in real-time using commercial transponders. As proof of concept, via bidirectional pairs of Hermite-Gaussian modes (HG00, HG10, and HG01), using a Telecom Infra Project Phoenix compliant commercial 400G transponder, 400-Gb/s data signals (56-Gbaud, DP-16QAM) are bidirectionally transmitted error free, i.e., with less than 1e-2 pre-FEC BERs, over approximately 1-m of free space
Scalable methods for optical transmission performance prediction using machine learning (ML) are studied in metro reconfigurable optical add-drop multiplexer (ROADM) networks. A cascaded learning framework is introduced to encompass the use of cascaded component models for end-to-end (E2E) optical path prediction augmented with different combinations of E2E performance data and models. Additional E2E optical path data and models are used to reduce the prediction error accumulation in the cascade. Off-line training (pre-trained prior to deployment) and transfer learning are used for component-level erbium-doped fiber amplifier (EDFA) gain models to ensure scalability. Considering channel power prediction, we show that the data collection processof the pre-trained EDFA model can be reduced to only 5% of the original training set using transfer learning. We evaluate the proposed method under three different topologies with field deployed fibers and achieve a mean absolute error of 0.16 dB with a single (one-shot) E2E measurement on the deployed 6-span system with 12 EDFAs.
