The University of Arizona, established in 1885, is a public land-grant research university located in Tucson, Arizona. As the first university in the Arizona Territory, it is a large institution renowned for its diverse academic offerings and robust research activities, with a mission to advance knowledge and educate students. We have collaborated with the University of Arizona on AI research involving large-scale neural networks and data-centric model evaluation. Our collaboration has contributed to the development of improved training strategies for robust and efficient machine learning systems. Please read about our latest news and collaborative publications with the University of Arizona.

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Model transfer of QoT prediction in optical networks based on artificial neural networks

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An artificial neural network (ANN) based transfer learning model is built for quality of transmission (QoT) prediction in optical systems feasible with different modulation formats. Knowledge learned from one optical system can be transferred to a similar optical system by adjusting weights in ANN hidden layers with a few additional training samples, where highly related information from both systems is integrated and redundant information is discarded. Homogeneous and heterogeneous ANN structures are implemented to achieve accurate Q-factor-based QoT prediction with low root-mean-square error. The transfer learning accuracy under different modulation formats, transmission distances, and fiber types is evaluated. Using transfer learning, the number of retraining samples is reduced from 1000 to as low as 20, and the training time is reduced by up to four times.

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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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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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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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ANN-Based Transfer Learning for QoT Prediction in Real-Time Mixed Line-Rate Systems

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Quality of transmission prediction for real-time mixed line-rate systems is realized using artificial neural network based transfer learning with SDN orchestrating. 0.42 dB accuracy is achieved with a 1000 to 20 reduction in training samples.

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Flex-Rate Transmission using Hybrid Probabilistic and Geometric Shaped 32QAM

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A novel algorithm to design geometric shaped 32QAM to work with probabilistic shaping is proposed to approach the Shannon limit within ~0.2 dB in SNR. The experimental results show ~0.2 dB SNR advantage over 64Gbaud PAS-64QAM, and flex-rate transmission demonstrates > 500 km reach improvement over 32QAM.

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Universal Hybrid Probabilistic-geometric Shaping Based on Two-dimensional Distribution Matchers

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We propose universal distribution matchers applicable to any two-dimensional signal constellation. We experimentally demonstrate that the performance of 32-ary QAM, based on hybrid probabilistic-geometric shaping, is superior to probabilistically shaped 32QAM and regular 32QAM.

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