Shaoliang Zhang is a former researcher at NEC Laboratories America, Inc.
By adjusting single shaping factor in the distribution matcher, probabilistic-shaped M-QAM is reviewed to provide both flex-rate and near-Shannon performance at the given flex-grid bandwidth and filling ratio.
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.
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.
We report on the evolution of the longest segment of FASTER cable at 11,017 km, with 8QAM transponders at 4b/s/Hz spectral efficiency (SE) in service. With offline testing, 6 b/s/Hz is further demonstrated using probabilistically shaped 64QAM, and a novel, low complexity nonlinearity compensation technique based on generating a black-box model of the transmission by training an artificial neural network, resulting in the largest SE-distance product 66,102 b/s/Hz-km over live-traffic carrying cable.
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.
A systematic study, including theory, simulation and experiments, is carried out to review the generalized pairwise optimization algorithm for designing optimized constellation. In order to verify its effectiveness, the algorithm is applied in three testing cases: 2-dimensional 8 quadrature amplitude modulation (QAM), 4-dimensional set-partitioning QAM, and probabilistic-shaped (PS) 32QAM. The results suggest that geometric shaping can work together with PS to further bridge the gap toward the Shannon limit.
Generalized mutual information (GMI) has been comprehensively studied in multidimensional constellation and probabilistic-shaped (PS) constellation together with different forward error correction (FEC) coding schemes. The simulation results confirm that GMI is an efficient and accurate tool to compare their post-FEC performance. In particular for uniformly geometric-shaped constellation, the pre-FEC Q-factor is highly correlated with GMI though the correlation is reduced at lower FEC coding rate. Furthermore, GMI can be used to design optimized constellation together with generalized pairwise optimization algorithm to mitigate the GMI loss in non-Gray-mapped constellation. The GMI-optimized 32QAM (opt32) shows ~0.5 dB signal-to-noise ratio improvement between 3 and 4 b/s GMI in both simulated and experimental results. Optimized two-dimensional 8 QAM is also designed to show the consistent GMI improvement over multi-dimensional 8 QAM-equivalent formats. In simulations, PS-64 QAM outperforms opt32 when a long sequence block is used in the distribution matcher.
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