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Tag Archive for: tingjun chen

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Tingjun Chen works at Duke University.

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Optical Link Tomography: First Field Trial and 4D Extension

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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.

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Toward Intelligent and Efficient Optical Networks: Performance Modeling, Co-existence, and Field Trials

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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.

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Multi-span OSNR and GSNR Prediction using Cascaded Learning

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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.

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Scalable Machine Learning Models for Optical Transmission System Management

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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.

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Multi-span optical power spectrum prediction using cascaded learning with one-shot end-to-end measurement

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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.

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Field Verification of Fault Localization with Integrated Physical-Parameter-Aware Methodology

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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.

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

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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 longitudinal monitoring (DLM) and optical line system (OLS) physical parameter calibration working together in real-time to extract physical link parameters for fast optical fiber line systems provisioning. Our methodology has the following advantages over traditional design: a minimized footprint at user sites, accurate estimation of the necessary optical network characteristics via complementary telemetry technologies, and the capability to conduct all operation work remotely. The last feature is crucial, as it enables remote operation to implement network design settings for immediate response to quality of transmission (QoT) degradation and reversion in the case of unforeseen problems. We successfully performed semi-automatic line system provisioning over field fiber network facilities at Duke University, Durham, North Carolina. The tasks of parameter retrieval, equipment setting optimization, and system setup/provisioning were completed within 1 h. The field operation was supervised by on-duty personnel who could access the system remotely from different time zones. By comparing Q-factor estimates calculated from the extracted link parameters with measured results from 400G transceivers, we confirmed that our methodology has a reduction in the QoT prediction errors ( 0.3 dB) over existing designs ( 0.6 dB). ©

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4D Optical Link Tomography: First Field Demonstration of Autonomous Transponder Capable of Distance, Time, Frequency, and Polarization-Resolved Monitoring

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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.

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Inline Fiber Type Identification using In-Service Brillouin Optical Time Domain Analysis

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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.

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Modeling the Input Power Dependency in Transceiver BER-ONSR for QoT Estimation

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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.

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