Optical Networking and Sensing

Our Optical Networking and Sensing department is leading world-class research into the next generation of optical networks and sensing systems that will power ICT-based social solutions for years. From forward-looking theoretical studies to cutting-edge experiments to world- and industry-first technology field trials, we deliver globally recognized innovation that looks into the future and translates it into present reality. Read our optical networking and sensing news and publications from our team of researchers.

Posts

Size and Alignment Independent Classification of the High-order Spatial Modes of a Light Beam Using a Convolutional Neural Network

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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 beam’s 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 beam’s 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.

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Field and lab experimental demonstration of nonlinear impairment compensation using neural networks

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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.

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Neural-Network-Based G-OSNR Estimation of Probabilistic-Shaped 144QAM Channels in DWDM Metro Network Field Trial

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A two-stage neural network model is applied on captured PS-144QAM raw data to estimate channel G-OSNR in a metro network field trial. We obtained 0.27dB RMSE with first-stage CNN classifier and second-stage ANN regressions.

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Coupled-Core Fiber Design For Enhancing Nonlinearity Tolerance

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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.

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Fiber Nonlinearity Compensation by Neural Networks

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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

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Spectrally-Efficient 200G Probabilistically-Shaped 16QAM over 9000km Straight Line Transmission with Flexible Multiplexing Scheme

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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.

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First Field Trial of Sensing Vehicle Speed, Density, and Road Conditions by Using Fiber Carrying High Speed Data

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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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On the Performance Metric and Design of Non-Uniformly Shaped Constellation

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Asymmetric information is shown to be more accurate in characterizing the performance of quadrant folding shaped (QFS) M-QAM. The performance difference of QFS M-QAM schemes strongly depends on the FEC coding rate, and the optimum FEC coding rate is found to be around ?0.8, which is independent of QFS M-QAM and the designed rates.

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Multi-parameter distributed fiber sensing with higherorder optical and acoustic modes

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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 optical–acoustic 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.

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Coherent optical wireless communication link employing orbital angular momentum multiplexing in a ballistic and diffusive scattering medium

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We experimentally investigate the scattering effect on an 80 Gbit/s orbital angular momentum (OAM) multiplexed optical wireless communication link. The power loss, mode purity, cross talk, and bit error rate performance are measured and analyzed for different OAM modes under scattering levels from ballistic to diffusive regions. Results show that (i) power loss is the main impairment in the ballistic scattering, while the mode purities of different OAM modes are not significantly affected; (ii) in the diffusive scattering, however, the performance of an OAM-multiplexed link further suffers from the increased cross talk between the different OAM modes.

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