OPTICAL NETWORKING & SENSING
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Optical Networking & Sensing
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.
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. Later in the week he will explore
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
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
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
The noise figure ripple of a dual-stage EDFA is studied starting from experimental measurements under full spectral load conditions and defining device characteristics. Asemi-analytical model is then proposed showing 0.1 dB standard deviation on the error distribution in all cases of operation.
We report the first field verification of fault localization in an optical line system (OLS) by integrating digital longitudinal monitoring and OLS calibration, highlighting changes in physical metrics and parameters. Use cases shown are degradation of a fiber span loss and optical amplifier noise figure.
We propose a transceiver back-to-back BER-OSNR characterization method that requires only a single VOA; it leverages the receiver SNR degradation caused by received power attenuation. Experiments using commercial transceivers show that the measurement error is less than 0.2 dB in the Q-factor.
We demonstrate a method for measuring the backscatter coefficient of hollow-core fibre (HCF), and show the feasibility of distributed acoustic sensing (DAS) with simultaneous 9.6-Tb/s DWDM transmission over a 1.6-km field-deployed HCF cable.
Our optical networking & sending team has arrived in Frankfurt for the 2024 European Conference on Optical Communication (ECOC) and is excited to present many papers and tutorials this week. Please follow this page and on our social media channels for updates.
We propose methods and an architecture to conduct measurements and optimize newly installed optical fiber line systems semi-automatically using integrated physics-aware technologies in a data center interconnection (DCI) transmission scenario. We demonstrate, for the first time to our knowledge, digital
Optical fiber cables, initially designed for telecommunications, are increasingly repurposed for environmental monitoring using distributed fiber sensing technologies [1,2]. Distributed acoustic sensing (DAS) based on phase optical time domain reflectometry (?-OTDR) of Rayleigh backscatter enables various
The NEC Labs America team Yaowen Li, Andrea D’Amico, Yue-Kai Huang, Philip Ji, Giacomo Borraccini, Ming-Fang Huang, Ezra Ip, Ting Wang & Yue Tian (Not pictured: Fatih Yaman) has arrived in San Diego, CA for OFC24! Our team will be speaking and presenting throughout the event. Read more for an overview
We introduce a novel scheme to detect and localize optical network anomaly using forward transmission sensing, and develop a heuristic algorithm to optimize the route selection. The performance is verified via simulations and network experiments.
A method is proposed in order to improve QoT-E by calibrating the physical model parameters of an optical link post-installation, using only total power monitors integrated into the EDFAs and an OSA at the receiver.
We implement a cascaded learning framework using component-level EDFA models for optical power spectrum prediction in multi-span networks, achieving a mean absolute error of 0.17 dB across 6 spans and 12 EDFAs with only one-shot measurement.
We propose a method to estimate the input power dependency of the transceiver BER-OSNR characteristic. Experiments using commercial transceivers show that estimation error in Q-factor is less than 0.2 dB.
We proposed the use of BOTDA as a monitoring tool to identify fiber types present in deployed hybrid-span fiber cables, to assist in network planning, setting optimal launch powers, and selecting correct modulation formats.
We report the first field demonstration of 4D link tomography using a commercial transponder, which offers distance, time, frequency, and polarization-resolved monitoring. This scheme enables autonomous transponders that identify locations of multiple QoT degradation causes.
Recent advances in optical fiber sensing have enabled telecom network operators to monitor their fiber infrastructure while generating new revenue in various application scenarios, including data center interconnect, public safety, smart cities, and seismic monitoring. However, given the high utilization
There are increasing requirements for data center interconnection (DCI) services, which use fiber to connect any DC distributed in a metro area and quickly establish high-capacity optical paths between cloud services and mobile edge computing and the users. In such networks, coherent transceivers with
We report significant noise reduction in distributed acoustic sensing (DAS) link using enhanced-scatter fibre (ESF). The longest reach of 195km DAS link without inline amplifications is also demonstrated. We further present demonstration of simultaneous fibre-optic sensing and 400Gb/s data transmissions
We demonstrated under six minutes automatic provisioning of optical paths over field- deployed alien access links and WDM carrier links using commercial-grade ROADMs, whitebox mux-ponders, and multi-vendor transceivers. With channel probing, transfer learning, and Gaussian noise model, we achieved an
Coexistence of real-time constant-amplitude distributed acoustic sensing (DAS) and 400GbE signals is verified by field trial over metro fibers, demonstrating no QoT impact during co-propagation and supporting preemptive DAS-informed optical path switching before link failure
We report the first distributed acoustic sensing (DAS) experiment with over >1,000 km reach on a hybrid link comprising of a mixture of field and lab fibers with bi-directional inline Raman amplification after each span. We used 20× frequency-diversity chirped-pulses for the probe signal,and recovered
We present a manhole localization method based on distributed fiber optic sensing and weakly supervised machine learning techniques. For the first time to our knowledge, ambient environment data is used for underground cable mapping with the promise of enhancing operational efficiency and reducing field
We review recent advances in distributed fiber optic sensing (DFOS) and their applications. The scattering mechanisms in glass, which are exploited for reflectometry-based DFOS, are Rayleigh, Brillouin, and Raman scatterings. These are sensitive to either strain and/or temperature, allowing optical fiber
Distributed fiber optic sensing systems use long section of optical fiber as the sensing media. Therefore, the fiber characteristics determines the sensing capability and performance. In this presentation, various types of specialty optical fibers and their sensing applications will be introduced and
We explore two fiber sensing methods which enables coexistence with data transmission on DWDM fiber networks. Vibration detection and localization can be achieved by extracting optical phase from modified coherent transponders. Frequency-diverse chirped-pulse DAS with all-Raman amplification can improve
We review multiple use cases over deployed networks including co-existing sensing/data transmission, cable cut prevention and perimeter intrusion detection to realize telecom infrastructure can be sensing backbones instead of the sole function of data transmission.
We review recent advances aimed at increasing the reach of distributed fiber optic sensing with simultaneous data transmission. We review two methods based on measurement of accumulated phase on telecom signals, and chirp-pulsed DAS with inline amplification and frequency diversity.
We report the first distributed acoustic sensing (DAS) results over>1,000 km on a field-lab hybrid link using chirped-pulses with correlation detection and 20× frequency-diversity, achieving a sensitivity of 100 pa/√Hz at 20-meters spatial resolution.
We demonstrate distributed acoustic sensing (DAS) over a bidirectional datacenter link which uses self-homodyne coherent detection for the data signal. Frequency multiplexing allows sharing the optoelectronic hardware, and enables DAS as an auxiliary function.
We demonstrate a vibration detection and localization scheme based on bidirectional transmission of telecom signals with digital coherent detection at the receivers. Optical phase is extracted from the digital signal processing blocks of the coherent receiver, from which the vibration component is extracted
Distributed fiber optic sensing (DFOS) is a rapidly evolving field that allows the existing optical fiber infrastructure for telecommunications to be reused for wide-area sensing. Using the backscattering mechanisms of glass—which includes Rayleigh, Brillouin, and Raman backscatter—it is possible
We demonstrate, for the first time, that cyclic linear pulse coding can be bipolar for BOTDA sensors, breaking the unipolar limitation of linear coding techniques and elevating the coding gain for a given code length.
We demonstrate vibration detection and localization based on extracting optical phase from the DSP elements of a coherent receiver in bidirectional WDM transmission of 200-Gb/s DP-16QAM over 380 km of installed field fiber.
Distributed fiber optical systems (DFOS) allow deployed optical cables to monitor the ambient environment over wide geographic area. We review recent field trial results, and show how DFOS can be made compatible with passive optical networks (PONs).
We demonstrate a passive optical network (PON) that employs reflective semiconductor optical amplifiers (RSOAs) at optical network units (ONUs) to allow simultaneous data transmission with distributed fiber-optic sensing (DFOS) on individual distribution fibers.
To the best of our knowledge, we present the first field trial of distributed fiber optical sensing (DFOS) and high-speed communication, comprising a coexisting system, over an operation telecom network. Using probabilistic-shaped (PS) DP-144QAM, a 36.8 Tb/s with an 8.28-b/s/Hz spectral efficiency (SE)
An artificial neural network (ANN) based transfer learning model is built for quality of transmission (QoT) prediction in optical systems feasible with different modulation formats. Knowledge learned from one optical system can be transferred to a similar optical system by adjusting weights in ANN hidden
A two-stage neural network model is applied on captured PS-144QAM raw data to estimate channel G-OSNR in a metro network field trial. We obtained 0.27dB RMSE with first-stage CNN classifier and second-stage ANN regressions.
For the first time, we demonstrate detection of vehicle speed, density, and road conditions using deployed fiber carrying high-speed data transmission, and prove carriers’ large-scale fiber infrastructures can also be used as ubiquitous sensing networks.
We demonstrate high spectral efficiency transmission over 549 km of field-deployed single-mode fiber using probabilistic-shaped 144QAM. We achieved 41.5 Tb/s over the C-band at a spectral efficiency of 9.02 b/s/Hz using 32-Gbaud channels at a channel spacing of 33.33 GHz, and 38.1 Tb/s at a spectral
We propose a single-ended Brillouin-based sensor in elliptical-core few-mode optical fiber for multi-parameter measurement using spontaneous Brillouin scattering. Distributed sensing of temperature and strain is demonstrated over 0.5 km elliptical-core few-mode fiber.
An ensemble learning algorithm is applied to enhance filtering tolerance of cascaded WSSs in open ROADM environment to demonstrate ~0.8dB Q-factor improvement over MLSE after transmitting over 3200km with 16 ROADMs.
We study digital signal processing techniques to optimize the back-to-back performance of large probabilistic shaped constellations. We cover joint optimization of LDPC and constellation shaping, CD pre-compensation, clipping and I/Q imbalance compensation.
41.5-Tb/s over 549 km of deployed SSMF in Verizon’s network is achieved using probabilistic-shaped 144QAM to optimize throughput at ultra-fine granularity. This is the highest C-band only capacity and spectral efficiency in metro field environment.
Quality of transmission prediction for real-time mixed line-rate systems is realized using artificial neural network based transfer learning with SDN orchestrating. 0.42 dB accuracy is achieved with a 1000 to 20 reduction in training samples.
