We report the distributed-fiber-sensing field trial results over a 5G-transport-network. A standard communication fiber is used with real-time AI processing for cable self-protection, cable-cut threat assessment and road traffic monitoring in a long-term continuous test.
Empowered by the rapid advancement of fiber optic sensing techniques in recent years, network carriers are able to upgrade their network infrastructure beyond the basic communication services with extra sensing applications and services (e.g., monitoring traffic and road condition, leakage detection, etc.), thus evolving to a new era of Infrastructure-as-a-Sensor (IaaSr) or Network-as-a-Sensor (NaaSr). When network carriers upgrade their network infrastructures with distributed fiber optic sensing (DFOS) technique to provide IaaSr services, there will arise a critical challenge: how to provide survivable (or reliable) IaaSr services against network failures (e.g., fiber cut). In this work, for the first time, we investigate the problem of survivable DFOS placement against single link failure. More specifically, we study where to place the primary and backup sensors and how to assign the primary and backup fiber sensing routes, with the objective of minimizing the number of sensors used. We formulate the problem using Integer Linear Programming (ILP) to facilitate the optimal solution. In addition, we propose a set of efficient heuristic algorithms to solve the problem in a fast manner. In particular, the proposed Shared-one algorithm provides a cost-efficient shared protection, through a one-step global optimization of the assignment of primary and backup DFOS placement. We conduct extensive simulations to evaluate the performance of the proposed solutions. We find out that Shared-one can achieve a close-to-optimal performance, compared to the ILP optimal results, while outperforming the other heuristic solutions with an average performance improvement by at least 16%.
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.
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.
We demonstrated for the first time to our knowledge, the detection and localization of a static weight on an aerial cable by using frequency domain decomposition analysis of ambient vibrations detected by a φ-DAS system.
We demonstrate a new application of fiber-optic-sensing and machine learning techniques for vehicle run-off-road events detection to enhance roadway safety and efficiency. The proposed approach achieves high accuracy in a testbed under various experimental conditions.
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).
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.
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.
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.
