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Wenjia Zhang

Researcher at Shanghai Jiao Tong University

Publications -  84
Citations -  559

Wenjia Zhang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Computer science & Optical switch. The author has an hindex of 11, co-authored 66 publications receiving 390 citations. Previous affiliations of Wenjia Zhang include Columbia University & Singapore–MIT alliance.

Papers
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Nonlinear Distortion Mitigation by Machine Learning of SVM Classification for PAM-4 and PAM-8 Modulated Optical Interconnection

TL;DR: The results indicate that CBT-SVMs have better performance for PAM-8 compared to Pam-4, and the sensitivity gain increases almost linearly with the increase of eye-linearity (increase of modulation nonlinearity distortion).
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40-Gb/s DPSK Data Transmission Through a Silicon Microring Switch

TL;DR: In this paper, the authors demonstrate switching of a 40-Gb/s differential-phase-shift-keyed (DPSK) signal through a coupled silicon photonic microring switch.
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Next-Generation Optically-Interconnected High-Performance Data Centers

TL;DR: In this paper, an end-to-end photonic networking platform for future optically-interconnected data center networks is proposed, which includes a reconfigurable hybrid photonic network building block and a protocol-agnostic optical network interface.
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A hybrid optical packet and wavelength selective switching platform for high-performance data center networks

TL;DR: This architecture based on cascaded silicon microrings and semiconductor optical amplifiers supports wavelength reconfigurable packet and circuit switching, and is highly scalable, energy efficient and potentially integratable.
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Threshold-Based Pruned Retraining Volterra Equalization for 100 Gbps/Lane and 100-m Optical Interconnects Based on VCSEL and MMF

TL;DR: The threshold-based pruned retraining Volterra equalization (TRVE) is proposed to reduce the computation complexity, while maintaining the transmission performance and the TRVE with $\ell _1$-regularization is proposed and experimentally verified to optimize the threshold selection.