Ting Wang NEC Labs America

Ting Wang

Department Head

Optical Networking & Sensing

Posts

Distributed Fiber Optic Sensors Placement for Infrastructure-as-a-Sensor

/in /by

Recently, the distributed fiber optic sensing (DFOS) techniques have advanced rapidly. There emerges various types of DFOS sensors that can monitor physical parameters such as temperature, strain, and vibration. With these DFOS sensors deployed, the telecom networks are capable of offering additional services beyond communications, such as monitoring road traffic condition, monitoring utility pole health, monitoring city noise and accident, thus evolving to a new paradigm of Infrastructure-as-a-Sensor (IaaSr) or Network-as-a-Sensor (NaaSr). When telecom network carriers upgrade their infrastructures with DFOS sensors to provide such IaaSr/NaaSr services, there will arise a series of critical challenges: (1) where to place the DFOS sensors, and (2) how to provision the DFOS sensing fiber routes to cover the whole network infrastructures with the minimum number of DFOS sensors? We name this as the DFOS placement problem. In this paper, we prove that the DFOS placement problem is an NP-hard problem, and we analyze the upper bound of the number of DFOS sensors used. To facilitate the optimal solution, we formulate the DFOS placement problem with an Integer Linear Programming model that aims at minimizing the number of DFOS sensors used. Furthermore, we propose a cost-efficient heuristic solution, called Explore-and-Pick (EnP), which can achieve a close-to-optimal performance in a fast manner. We analyze the approximation ratio and the computational complexity of the proposed EnP algorithm. In addition, we conduct comprehensive simulations to evaluate the performance of the proposed solutions. Simulation results show that the EnP algorithm can outperform the baseline algorithm by 16% in average and 26% at best, and it achieves a performance that is close to the optimal result obtained by ILP.

Read more

Employing Fiber Sensing and On-Premise AI Solutions for Cable Safety Protection over Telecom Infrastructure

/in /by

We review the distributed-fiber-sensing field trial results over deployed telecom networks. With local AI processing, real-time detection, and localization of abnormal events with cable damage threat assessment are realized for cable self-protection.

Read more

Distributed Acoustic Sensing for Datacenter Optical Interconnects using Self-Homodyne Coherent Detection

/in /by

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.

Read more

DAS over 1,007-km Hybrid Link with 10-Tb/s DP-16QAM Co-propagation using Frequency-Diverse Chirped Pulses (OFC)

/in /by

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.

Read more

Perimeter Intrusion Detection with Rayleigh Enhanced Fiber Using Telecom Cables as Sensing Backhaul

/in /by

We report field test results of facility perimeter intrusion detection with distributed-fiber-sensing technology and backscattering-enhanced-fiber by using deployed telecom fiber cables as sensing backhaul. Various intrusive activities, such as walking/jumping at >100ft distance, are detected.

Read more

Remote Drone Detection and Localization with Optical Fiber Microphones and Distributed Acoustic Sensing

/in /by

We demonstrate the first fiber-optic drone detection method with ultra-highly sensitive optical microphones and distributed acoustic sensor. Accurate drone localization has been achieved through acoustic field mapping and data fusion.

Read more

Vibration Detection and Localization using Modified Digital Coherent Telecom Transponders

/in /by

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 by bandpass filtering, and the position along the cable closest to the vibration’s epicenter is recovered by correlation. We demonstrate our scheme first using offline experiment with 200-Gb/s DP-16QAM, and we report field trial results over installed fiber to detect real-world vibration events.

Read more

Weight Pruning Techniques for Nonlinear Impairment Compensation using Neural Networks

/in /by

Neural networks (NNs) are attractive for nonlinear impairment compensation applications in communication systems, such as optical fiber nonlinearity, nonlinearity of driving amplifiers, and nonlinearity of semiconductor optical amplifiers. Without prior knowledge of the transmission link or the hardware characteristics, optimal parameters are completely constructed from a data-driven approach by exploring training datasets, once the NN structure is given. On the other hand, due to computational power and energy consumption, especially in high-speed communication systems, the computational complexity of the optimized NN needs to be confined to the hardware, such as FPGA or ASIC without sacrificing its performance improvement. In this paper, two approaches are presented to accommodate the NN-based algorithms for high-speed communication systems. The first approach is to reduce computational complexity of the NN-based nonlinearity compensation algorithms on the basis of weight pruning (WP). WP can significantly reduce the computational complexity, especially because the nonlinear compensation task studied here results in a sparse NN. The authors have studied an enhanced approach of WP by imposing an additional restriction on the selection of non-zero weights on each hidden layer. The second approach is to implement NNs onto a silicon-photonic integrated platform, enabling power efficiency to be further improved without sacrificing the high-speed operation.

Read more

AI-Driven Applications over Telecom Networks by Distributed Fiber Optic Sensing Technologies

/in /by

By employing distributed fiber optic sensing (DFOS) technologies, field deployed fiber cables can be utilized as not only communication media for data transmissions but also sensing media for continuously monitoring of the physical phenomenon along the entire route. The fiber can be used to monitor ambient environment along the route covering a wide geographic area. With help of artificial intelligence and machine learning (AI/ML) technologies on information processing, many applications can be developed over telecom networks. We review the recent field results and demonstrate how DFOS can work with existing communication channels and provide holistic view of road traffic monitoring included vehicle counts and average vehicle speeds. A long-term wide-area road traffic monitoring system is an efficient way of gathering seasonal vehicle activities which can be applied in future smart city applications. Additionally, DFOS also offers cable cut prevention functions such as cable self-protection and cable cut threat assessment. Detection and localization of abnormal events and evaluating the threat to the cable are realized to protect telecom facilities.

Read more

Detection and Localization of Stationary Weights Hanging on Aerial Telecommunication Fibers using Distributed Acoustic Sensing

/in /by

For the first time to our knowledge, a stationary weight hanging on an operational aerial telecommunication field fiber was detected and localized using only ambient data collected by a φ-DAS system. Although stationary weights do not create temporally varying signals, and hence cannot be observed directly from the DAS traces, the existence and the location of the additional weights were revealed by the operational modal analysis of the aerial fiber structure.

Read more