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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.
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.
We proposed and demonstrated a new machine learning algorithm for wavelength modulation spectroscopy to enhance the accuracy of fire detection. The result shows more than 8% of accuracy improvement by analyzing CO/CO 2 2f signals.
Traffic conditions of the highway, Ya traffic volume meter CCTV Because it is observed in the spot, such as the discovery of traffic disturbances which deviates from the observation spot it may be delayed. The traffic flow has a problem from the point observations data indirectly order to be estimated, the capture accuracy of trending and regional circumstances change in time series. Therefore, we focused on the optical fiber sensing technology that utilizes the existing light off Aibainfura highway, actually measuring the travel vibration of the vehicle from the infrastructure as a continuous line, overhead grasp the traffic flow from the traveling locus We are working to. This time, tried traffic flow observation and the estimates of the average speed in the Tokyo, Nagoya and New Tomei Expressway. A result, the demonstration zone 45km in a traffic flow observable real time, succeeded in average speed calculation equivalent to the existing traffic meter, this technology has shown promise as a bird’s-eye technique wide and real-time traffic flow.
Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance fiber optic transmission systems. Nonlinear impairments are determined by the signal pattern and the transmission system parameters. Deterministic algorithms based on approximating the nonlinear Schrodinger equation through digital back propagation, or a single step approach based on perturbation methods have been demonstrated, however, their implementation demands excessive signal processing resources, and accurate knowledge of the transmission system. A completely different approach uses machine learning algorithms to learn from the received data itself to figure out the nonlinear impairment. In this work, a single-step, system agnostic nonlinearity compensation algorithm based on a neural network is proposed to pre-distort symbols at transmitter side to demonstrate ~0.6?dB Q improvement after 2800?km standard single-mode fiber transmission using 32 Gbaud signal. Without prior knowledge of the transmission system, the neural network tensor weights are constructed from training data thanks to the intra-channel cross-phase modulation and intra-channel four-wave mixing triplets used as input features.
Flexible wavelength-multiplexing technique in backbone submarine networks has been deployed to accommodate the trend of variable-rate modulation formats. In this paper, we propose a new design of flexible-rate transponders in the scenario of flexible multiplexing scheme to achieve near-Shannon performance. Probabilistic-shaped (PS) M-QAM is capable of adjusting the bit rate at very finer granularity by adapting the entropy of the distribution matcher. Instead of delivering variable bit rates at the fixed baud rate, various baud rates of 200Gb/s PS-16QAM is demonstrated to fit into the flexible grid multiple 3.125GHz bandwidth. This flexible baud rate saves the limited optical bandwidth assigned by the flexible multiplexing scheme to improve bandwidth utilization. The 200G PS-16QAM signals are experimentally demonstrated over 9000km straight-line testbed to achieve 3.05b/s/Hz~5.33 b/s/Hz spectral efficiency (SE) with up to 4dB Q margin. In addition, the high baud rate signals are used for lower SE while low baud rate signals are targeting at high SE transmission to reduce the implementation penalty.
Fiber nonlinearity is a major limitation on the achievable maximum capacity per fiber core. Digital signal processing (DSP) can be used directly to compensate nonlinear impairments, however with limited effectiveness. It is well known that fibers with higher chromatic dispersion (CD) reduce nonlinear impairments, and CD can be taken care of with DSP. Since, maximum CD is limited by material dispersion of the fiber we propose using strongly-coupled multi-core fibers with large group delay (GD) between the cores. Nonlinear mitigation is achieved through strong mode coupling, and group delay between the cores which suppresses four-wave mixing interaction by inducing large phase-mismatch, albeit stochastic in nature. Through simulations we determine the threshold GD required for noticeable nonlinearity suppression depends on the fiber CD. In particular, for dispersion-uncompensated links a large GD of the order of 1ns per 1000km is required to improve optimum Q by 1 dB. Furthermore, beyond this threshold, larger GD results in larger suppression without any signs of saturation.
Neuron network (NN) is proposed to work together with perturbation-based nonlinearity compensation (NLC) algorithm by feeding with intra-channel cross-phase modulation (IXPM) and intra-channel four-wave mixing (IFWM) triplets. Without prior knowledge of the transmission link and signal pulse shaping/baudrate, the optimum NN architecture and its tensor weights are completely constructed from a data-driven approach by exploring the training datasets. After trimming down the unnecessary input tensors based on their weights, its complexity is further reduced by applying the trained NN model at the transmitter side thanks to the limited alphabet size of the modulation formats. The performance advantage of Tx-side NN-NLC is experimentally demonstrated using both single-channel and WDM-channel 32Gbaud dual-polarization 16QAM over 2800km transmission
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.
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NEC Laboratories America, Inc. (NEC Labs) is the US-based center for NEC Corporation’s global network of corporate research laboratories. Our diverse research groups collaborate with industry, academia and governments to provide disruptive solutions to complex problems. A leader in the integration of IT and network technologies with more than 100 years of expertise, NEC provides a combination of products and solutions that cross-utilize the company’s experience and global resources to meet the complex and ever-changing needs of its customers.