Ting Wang NEC Labs America

Ting Wang

Department Head

Optical Networking & Sensing

Posts

Nonlinear Impairment Compensation using Neural Networks

Neural networks are attractive for nonlinear impairment compensation applications in communication systems. In this paper, several approaches to reduce computational complexity of the neural network-based algorithms are presented.

Field Trial of Abnormal Activity Detection and Threat Level Assessment with Fiber Optic Sensing for Telecom Infrastructure Protection

We report the field trial results of monitoring abnormal activities near deployed cable with fiber-optic-sensing technology for cable protection. Detection and position determination of abnormal events and evaluating the threat to the cable is realized.

Field Trial of Vibration Detection and Localization using Coherent Telecom Transponders over 380-km Link

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 Sensor Network using Telecom Cables as Sensing Media: Applications

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).

Field Trial of Distributed Fiber Sensor Network Using Operational Telecom Fiber Cables as Sensing Media

We demonstrate fiber optic sensing systems in a distributed fiber sensor network built on existing telecom infrastructure to detect temperature, acoustic effects, vehicle traffic, etc. Measurements are also demonstrated with different network topologies and simultaneously sensing four fiber routes with one system.

Address Challenges in Placing Distributed Fiber Optic Sensors

We are the first to investigate a novel problem, called distributed fiber optic sensor placement, in the context of Infrastructure-as-a-Sensor. We propose an ILP-based optimal solution and a close-to-optimal heuristic solution, both of which aim at minimizing the cost of sensors.

New Methods for Non-Destructive Underground Fiber Localization using Distributed Fiber Optic Sensing Technology

To the best of our knowledge, we present the first underground fiber cable position detection methods using distributed fiber optic sensing (DFOS) technology. Meter level localization accuracy is achieved in the results.

First Proof That Geographic Location on Deployed Fiber Cable Can Be Determined by Using OTDR Distance Based on Distributed Fiber Optical Sensing Technology

We demonstrated for the first time that geographic locations on deployed fiber cables can be determined accurately by using OTDR distances. The method involves vibration stimulation near deployed cables and distributed fiber optical sensing technology.

Demonstration of photonic neural network for fiber nonlinearity compensation in long-haul transmission systems

We demonstrate the experimental implementation of photonic neural network for fiber nonlinearity compensation over a 10,080 km trans-pacific transmission link. Q-factor improvement of 0.51 dB is achieved with only 0.06 dB lower than numerical simulations.

First Field Trial of Distributed Fiber Optical Sensing and High-Speed Communication Over an Operational Telecom Network

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) (48-Gbaud channels, 50-GHz channel spacing) was achieved. Employing DFOS technology, road traffic, i.e., vehicle speed and vehicle density, were sensed with 98.5% and 94.5% accuracies, respectively, as compared to video analytics. Additionally, road conditions, i.e., roughness level was sensed with >85% accuracy via a machine learning based classifier.