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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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41.5-Tb/s Transmission Over 549 km of Field Deployed Fiber Using Throughput Optimized Probabilistic-Shaped 144QAM

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We demonstrate high spectral efficiency transmission over 549 km of field-deployed single-mode fiber using probabilistic-shaped 144QAM. We achieved 41.5 Tb/s over the C-band at a spectral efficiency of 9.02 b/s/Hz using 32-Gbaud channels at a channel spacing of 33.33 GHz, and 38.1 Tb/s at a spectral efficiency of 8.28 b/s/Hz using 48-Gbaud channels at a channel spacing of 50 GHz. To the best of our knowledge, these are the highest total capacities and spectral efficiencies reported in a metro field environment using C-band only. In high spectral efficiency transmission, it is necessary to optimize back-to-back performance in order to maximize the link loss margin. Our results are enabled by the joint optimization of constellation shaping and coding overhead to minimize the gap to Shannon’s capacity, transmitter- and receiver-side digital backpropagation, signal clipping optimization, and I/Q imbalance compensation.

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Leveraging Knowledge Bases for Future Prediction with Memory Comparison Networks

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Making predictions about what might happen in the future is important for reacting adequately in many situations. For example, observing that “Man kidnaps girl” may have the consequence that “Man kills girl”. While this is part of common sense reasoning for humans, it is not obvious how machines can acquire and generalize over such knowledge. In this article, we propose a new type of memory network that can predict the next future event also for observations that are not in the knowledge base. We evaluate our proposed method on two knowledge bases: Reuters KB (events from news articles) and Regneri KB (events from scripts). For both knowledge bases, our proposed method shows similar or better prediction accuracy on unseen events (or scripts) than recently proposed deep neural networks and rankSVM. We also demonstrate that the attention mechanism of our proposed method can be helpful for error analysis and manual expansion of the knowledge base.

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Intelligent Filtering-Penalty Monitoring and Mitigation for Cascaded WSSs using Ensemble Learning Algorithm

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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.

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Neuron-Network-based Nonlinearity Compensation Algorithm

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A simplified, system-agnostic NLC algorithm based on a neuron network is proposed to pre-distort symbols at transmitter side to demonstrate ~0.6dB Q improvement after 2800km SMF transmission using 32Gbaud DP-16QAM.

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Optimization of Probabilistic Shaping Enabled Transceivers with Large Constellation Sizes for High Capacity Transmission

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We study digital signal processing techniques to optimize the back-to-back performance of large probabilistic shaped constellations. We cover joint optimization of LDPC and constellation shaping, CD pre-compensation, clipping and I/Q imbalance compensation.

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