Ting Wang NEC Labs America

Ting Wang

Department Head

Optical Networking & Sensing

Posts

First Field Trial of Hybrid Fiber Sensing with Data Transmission Resulting in Enhanced Sensing Sensitivity and Spatial Resolution

/in /by

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 applications including traffic monitoring [3], railway [4] and perimeter intrusion detection [5] and cable damage detection [6], etc. The sensing range of DAS is typically limited to several tens of kilometers due to low optical signal-to-noise (OSNR) of the received backscatter. Additionally, compatibility of DAS with existing fiber infrastructure is hindered by the unidirectional operation of inline amplifiers with isolators. An alternative approach based on forward transmission was recently proposed [7, 8], which involves probing an optical fiber with a continuous wave (CW) signal and measuring either changes in received phase or the state of polarization (SOP) to detect cumulative vibration-induced strain. Unlike backscatter measurement, forward transmissions methods have longer sensing range due to higher OSNR, and is compatible with existing telecom infrastructure. However, potential challenges include limited localization accuracy, and low number of simultaneous events that can be discriminated and localized [7]. In this paper, we propose a new concept of “hybrid fiber sensing” for long-haul DWDM networks where the repeater node architecture combines DAS with forward-phase sensing (FPS), enhancing sensitivity by 32%. This approach achieves a multi-span, fine-resolution fiber sensing system. The FPS method detects vibration anomalies and coarsely localizes its position to within a fiber span. A segmented DAS then refines the position estimate and provides a precise waveform measurement. Consequently, the special resolution improves from one fiber span of 80 km to 4 m. Our scheme is validated on a test bed comprising lab spools and field fibers, demonstrating the capability to detect and monitor field construction while simultaneously supporting full C-band 400-Gb/s real-time (RT) data transmission.

Read more

Seeing the Vibration from Fiber-Optic Cables: Rain Intensity Monitoring using Deep Frequency Filtering

/in /by

The various sensing technologies such as cameras LiDAR radar and satellites with advanced machine learning models offers a comprehensive approach to environmental perception and understanding. This paper introduces an innovative Distributed Fiber Optic Sensing (DFOS) technology utilizing the existing telecommunication infrastructure networks for rain intensity monitoring. DFOS enables a novel way to monitor weather condition and environmental changes provides real-time continuous and precise measurements over large areas and delivers comprehensive insights beyond the visible spectrum. We use rain intensity as an example to demonstrate the sensing capabilities of DFOS system. To enhance the rain sensing performance we introduce a Deep Phase-Magnitude Network (DFMN) divide the raw sensing data into phase and magnitude component allowing targeted feature learning on each component independently. Furthermore we propose a Phase Frequency learnable filter (PFLF) for the phase component filtering and conduct standard convolution layers on the magnitude component leveraging the inherent physical properties of optical fiber sensing. We formulate the phase-magnitude channel into a parallel network and subsequently fuse the features for a comprehensive analysis in the end. Experimental results on the collected fiber sensing data show that the proposed method performs favorably against the state-of-the-art approaches.

Read more

Deep Learning-based Intrusion Detection and Impulsive Event Classification for Distributed Acoustic Sensing across Telecom Networks

/in /by

We introduce two pioneering applications leveraging Distributed Fiber Optic Sensing (DFOS) and Machine Learning (ML) technologies. These innovations offer substantial benefits forfortifying telecom infrastructures and public safety. By harnessing existing telecom cables, our solutions excel in perimeter intrusion detection via buried cables and impulsive event classification through aerial cables. To achieve comprehensive intrusion detection, we introduce a label encoding strategy for multitask learning and evaluate the generalization performance of the proposed approach across various domain shifts. For accurate recognition of impulsive acoustic events, we compare several standard choices of representations for raw waveform data and neural network architectures, including convolutional neural networks (ConvNets) and vision transformers (ViT).We also study the effectiveness of the built-in inductive biases under both high- and low-fidelity sensing conditions and varying amounts of labeled training data. All computations are executed locally through edge computing, ensuring real-time detection capabilities. Furthermore, our proposed system seamlessly integrates with cameras for video analytics, significantly enhancing overall situation awareness of the surrounding environment.

Read more

NEC Labs America Team Attending CVPR 2024 in Seattle

/in /by

Our team will be attending CVPR 2024 (The IEEE /CVF Conference on Computer Vision & Pattern Recognition) from June 17-21! See you there at the NEC Labs America Booth 1716! Stay tuned for more information about our participation.

Read more

Semi-Automatic Line-System Provisioning with Integrated Physical-Parameter-Aware Methodology: Field Verification and Operational Feasibility

/in /by

We propose methods and 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, digital longitudinal monitoring (DLM) and optical line system (OLS) physical parameter calibration working together in real-time to extract physical link parameters for transmission performance optimization. Our methodology has the following advantages over traditional design: minimized footprint at the user site, accurate estimate of necessary optical network characteristics via complementary telemetry technologies, and ability to conduct all operation work from remotely. The last feature is crucial as remote operation personnel can implement network design settings for immediate response to quality of transmission (QoT) degradation and reverting in case of unforeseen problems. We successfully completed the semi-automatic line system provisioning over field fiber networks facilities at Duke University, Durham, NC. The tasks of parameter retrieval, equipment setting optimization, and system setup/provisioning were completed within 1 hour. The field operation was supervised by on-duty personnel who can access the system remotely from different timezones. By comparing Q-factor estimates calculated by the extracted link parameters with measured results from 400G transceivers, we confirmed our methodology has a reduction in the QoT prediction errors overexisting design.

Read more

NEC Labs America at OFC 2024 San Diego from March 24 – 28

/in /by

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 of our participation.

Read more

Optical Network Anomaly Detection and Localization Based on Forward Transmission Sensing and Route Optimization

/in /by

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.

Read more

Optical Line Physical Parameters Calibration in Presence of EDFA Total Power Monitors

/in /by

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.

Read more

Multi-Span Optical Power Spectrum Prediction using ML-based EDFA Models and Cascaded Learning

/in /by

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.

Read more

Field Implementation of Fiber Cable Monitoring for Mesh Networks with Optimized Multi-Channel Sensor Placement

/in /by

We develop a heuristic solution to effectively optimize the placement of multi-channel distributed fiber optic sensors in mesh optical fiber cable networks. The solution has beenimplemented in a field network to provide continuous monitoring.

Read more