Optical Networking and Sensing

Our Optical Networking and Sensing department is leading world-class research into the next generation of optical networks and sensing systems that will power ICT-based social solutions for years. From forward-looking theoretical studies to cutting-edge experiments to world- and industry-first technology field trials, we deliver globally recognized innovation that looks into the future and translates it into present reality. Read our optical networking and sensing news and publications from our team of researchers.


Local and Global Optimization Methods for Optical Line Control Based on Quality of Transmission

The ever-increasing demand for data traffic in recent decades has pushed network operators to give importance to the aspect of infrastructure control to facilitate its scalability and maximize its capacity. A generic lightpath (LP) is deployed starting from a traffic request between a given pair of nodes in a network. LPs are operated in the network based on an estimate of the quality of transmission (QoT), which is derived from the physical layer characteristics of a selected route. Regardless of the model used to estimate QoT, it is necessary to calibrate themodel to maximize its accuracy and define minimum design margins. The model calibration process depends significantly on the type of data that can be collected in the field (i.e., type of metric, resolution) and therefore on the available monitoring devices. In this work, a systematic evaluation of the QoT estimation is carried out on a multi-span erbium-doped-fiber-amplified optical line system (OLS) using in the first case only total power monitors and in the second experimentally emulating optical channel monitors (OCMs). Given the type of monitoring devices available, three different physical models are calibrated, and six optimization methods are used to define the optimal configuration of the target gain and tilt parameters of the optical amplifiers, jointly optimizing the working point of all amplifiers (global approach) or proceeding span by span (local approach). Subsequently, the OLS was set in each configuration obtained, and the generalized signal-to-noise ratio (GSNR) profile was measured at the end.

Radio-Frequency Linear Analysis and Optimization of Silicon Photonic Neural Networks

Broadband analog signal processors utilizing silicon photonics have demonstrated a significant impact in numerous application spaces, offering unprecedented bandwidths, dynamic range, and tunability. In the past decade, microwave photonic techniques have been applied to neuromorphic processing, resulting in the development of novel photonic neural network architectures. Neuromorphic photonic systems can enable machine learning capabilities at extreme bandwidths and speeds. Herein, low-quality factor microring resonators are implemented to demonstrate broadband optical weighting. In addition, silicon photonic neural network architectures are critically evaluated, simulated, and optimized from a radio-frequency performance perspective. This analysis highlights the linear front-end of the photonic neural network, the effects of linear and nonlinear loss within silicon waveguides, and the impact of electrical preamplification.

Low-rank Constrained Multichannel Signal Denoising Considering Channel-dependent Sensitivity Inspired by Self-supervised Learning for Optical Fiber Sensing

Optical fiber sensing is a technology wherein audio, vibrations, and temperature are detected using an optical fiber; especially the audio/vibrations-aware sensing is called distributed acoustic sensing (DAS). In DAS, observed data, which is comprised of multichannel data, has suffered from severe noise levels because of the optical noise or the installation methods. In conventional methods for denoising DAS data, signal-processing- or deep-neural-network (DNN)-based models have been studied. The signal-processing-based methods have the interpretability, i.e., non-black box. The DNN-based methods are good at flexibility designing network architectures and objective functions, that is, priors. However, there is no balance between the interpretability and the flexibility of priors in the DAS studies. The DNN-based methods also require a large amount of training data in general. To address the problems, we propose a DNN-structure signal-processing-based denoising method in this paper. As the priors of DAS, we employ spatial knowledge; low rank and channel-dependent sensitivity using the DNN-based structure.The result of fiber-acoustic sensing shows that the proposed method outperforms the conventional methods and the robustness to the number of the spatial ranks. Moreover, the optimized parameters of the proposed method indicate the relationship with the channel sensitivity; the interpretability.

Link Loss Analysis of Integrated Linear Weight Bank within Silicon Photonic Neural Network

Over the last decade, silicon photonic neural networks have demonstrated the possibility of photonic-enabled machine learning at the edge. These systems enable low-latency ultra-wideband classifications, channel estimations, and many other signal characterization tasks within wireless environments. While these proof-of-concept experiments have yielded promising results, poor device and architectural designs have resulted in sub-optimal bandwidth and noise performance. As a result, the application space of this technology has been limited to GHz bandwidths and high signal-to-ratio input signals. By applying a microwave photonic perspective to these systems, the authors demonstrate high-bandwidth operation while optimizing for RF performance metrics: instantaneous bandwidth, link loss, noise figure, and dynamic range. The authors explore the extended capabilities due to these improved metrics and potential architectures to continue further optimization. The authors introduce novel architectures and RF analysis for RF-optimized neuromorphic photonic hardware.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.