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.
We propose reusing existing optical cables in metropolitan networks for distributed sensing using a bidirectional, dual-band architecture where communications and sensing signals can coexist with weak interaction on the same optical fiber.
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.
The higher-order spatial modes of a light beam are receiving significant interest. They can be used to further increase the data speeds of high speed optical communication, and for novel optical sensing modalities. As such, the classification of higher-order spatial modes is ubiquitous. Canonical classification methods typically require the use of unconventional optical devices. However, in addition to having prohibitive cost, complexity, and efficacy, such methods are dependent on the light beams size and alignment. In this work, a novel method to classify higher-order spatial modes is presented, where a convolutional neural network is applied to images of higher-order spatial modes that are taken with a conventional camera. In contrast to previous methods, by training the convolutional neural network with higher-order spatial modes of various alignments and sizes, this method is not dependent on the light beams size and alignment. As a proof of principle, images of 4 Hermite-Gaussian modes (HG00, HG01, HG10, and HG11) are numerically calculated via known solutions to the electromagnetic wave equation, and used to synthesize training examples. It is shown that as compared to training the convolutional neural network with training examples that have the same sizes and alignments, a?~2×?increase in accuracy can be achieved.
For the first time, we demonstrate detection of vehicle speed, density, and road conditions using deployed fiber carrying high-speed data transmission, and prove carriers’ large-scale fiber infrastructures can also be used as ubiquitous sensing networks.
We propose a novel multi-parameter sensing technique based on a Brillouin optical time domain reflectometry in the elliptical-core few-mode fiber, using higher-order optical and acoustic modes. Multiple Brillouin peaks are observed for the backscattering of both the LP01 mode and LP11 mode. We characterize the temperature and strain coefficients for various opticalacoustic mode pairs. By selecting the proper combination of modes pairs, the performance of multi-parameter sensing can be optimized. Distributed sensing of temperature and strain is demonstrated over a 0.5-km elliptical-core few-mode fiber, with the discriminative uncertainty of 0.28°C and 5.81 ?? for temperature and strain, respectively.
We propose a single-ended Brillouin-based sensor in elliptical-core few-mode optical fiber for multi-parameter measurement using spontaneous Brillouin scattering. Distributed sensing of temperature and strain is demonstrated over 0.5 km elliptical-core few-mode fiber.
An ensemble learning algorithm is applied to enhance filtering tolerance of cascaded WSSs in open ROADM environment to demonstrate ~0.8dB Q-factor improvement over MLSE after transmitting over 3200km with 16 ROADMs.
