113 citations · 188 across the 10 of their papers we have counts for
10 papers
Efficient Multi-Agent Optimization of Optical Power in S+C+L-Band Systems
Junzhe Xiao, Kaida Chen, Cong Wang +4
We propose an AI Agent tailored for link power management in multi-band systems. In S+C+L band span-level study, the agent efficiently solves various optimization objectives. In ne…
Quasi-Constant Modulus Design for Nonlinearity-Tolerant Geometric Shaped Four Dimensional Modulation Format
Junzhe Xiao, Zekun Niu, Lyu Li +3
In this paper, the quasi-constant modulus (QCM) property is analyzed and leveraged in the design of nonlinearity-tolerant four-dimensional (4D) modulation formats. Accordingly, we…
Millimeter-scale rigid diamond probe for high sensitivity endoscopic-magnetometry applications
Jihongbo Shen, Heng Yuan, Hongyu Tao +6
Magnetometry based on diamond nitrogen-vacancy (NV) centers has been extensively studied for applications requiring diverse capabilities, spanning from nanometer spatial resolution…
Deep photonic reservoir computer for nonlinear equalization of 16-level quadrature amplitude modulation signals
Rui-Qian Li, Yi-Wei Shen, Zekun Niu +5
Photonic reservoir computer (PRC) is a kind of real-time and adaptive recurrent neural network, where only weights in the readout layer require training. PRC is a promising tool to…
Deep Learning Waveform Channel Modeling for Wideband Optical Fiber Transmission: Model Comparisons, Challenges and Potential Solutions
Minghui Shi, Hang Yang, Zekun Niu +6
Fast and accurate waveform simulation is critical for understanding fiber channel characteristics, developing digital signal processing (DSP) technologies, optimizing optical netwo…
Low Complexity Joint Chromatic Dispersion and Time/Frequency Offset Estimation Based on Fractional Fourier Transform
Guozhi Xu, Zekun Niu, Lyu Li +2
We propose and experimentally validate a joint estimation method for chromatic dispersion and time-frequency offset based on the fractional Fourier transform, which reduces computa…
Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schrödinger Equation Using Linear Feature Decoupling Method
Yunfan Zhang, Zekun Niu, Minghui Shi +2
We utilize the Feature Decoupling Distributed (FDD) method to enhance the capability of deep learning to fit the Nonlinear Schrodinger Equation (NLSE), significantly reducing the N…
Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method
Hang Yang, Haochen Zhao, Zekun Niu +4
The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental…
Fast and accurate waveform modeling of long-haul multi-channel optical fiber transmission using a hybrid model-data driven scheme
Hang Yang, Zekun Niu, Haochen Zhao +3
The modeling of optical wave propagation in optical fiber is a task of fast and accurate solving the nonlinear Schrödinger equation (NLSE), and can enable the optical system design…
Fast and Accurate Optical Fiber Channel Modeling Using Generative Adversarial Network
Hang Yang, Zekun Niu, Shilin Xiao +4
In this work, a new data-driven fiber channel modeling method, generative adversarial network (GAN) is investigated to learn the distribution of fiber channel transfer function. Ou…