Q
Qunbi Zhuge
Researcher at Shanghai Jiao Tong University
Publications - 230
Citations - 2677
Qunbi Zhuge is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Quadrature amplitude modulation & Computer science. The author has an hindex of 24, co-authored 180 publications receiving 2006 citations. Previous affiliations of Qunbi Zhuge include Ciena & Beijing University of Posts and Telecommunications.
Papers
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Digital subcarrier multiplexing for fiber nonlinearity mitigation in coherent optical communication systems.
TL;DR: It is shown experimentally that the SCM signal with a nearly-optimum number of subcarriers can extend the maximum reach by 23% in a 24 GBaud DP-QPSK transmission with a BER threshold, further indicating the merits of SCM signals in baud-rate flexible agile transmissions and future high-speed optical transport systems.
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Beyond 100 Gb/s: capacity, flexibility, and network optimization [Invited]
TL;DR: FlexEthernet and FlexOTN will be put in place to allow network operators to optimize capacity in their optical transport networks without manual changes to the client hardware.
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Spectral Efficiency-Adaptive Optical Transmission Using Time Domain Hybrid QAM for Agile Optical Networks
TL;DR: In this paper, a continuous tradeoff between spectral efficiency and achievable distance by mixing modulation formats including QPSK, 8QAM, and 16QAM is demonstrated in two scenarios: 1) 28 Gbaud non-return-to-zero (NRZ) signal for fixed 50 GHz grid systems; 2) superchannel transmission at date rates of up to 1.15 Tb/s and spectral efficiencies of 7.68 b/s/Hz.
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Performance comparison of DML, EML and MZM in dispersion-unmanaged short reach transmissions with digital signal processing.
TL;DR: It is shown that, although the DML based transmitter is often believed to be less favorable in C-band high-speed transmissions, it exhibits superior performance over the other two transmitters when either linear or nonlinear digital signal processing is adopted.
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Machine learning based linear and nonlinear noise estimation
F. J. Vaquero Caballero,David J. Ives,Charles Laperle,D. Charlton,Qunbi Zhuge,Maurice O'Sullivan,Seb J. Savory +6 more
TL;DR: This paper estimates the linear and nonlinear signal-to-noise ratio (SNR) from the received signal by obtaining features of two distinct effects: nonlinear phase noise and second-order statistical moments from a small neural network trained to estimate the SNRs.