6 papers
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…
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…
Assessment of Intra-channel Fiber Nonlinearity Compensation in 200 GBaud and Beyond Coherent Optical Transmission Systems
Zhiyuan Yang, Mengfan Fu, Yihao Zhang +4
In this paper, we investigate and assess the performance of intra-channel nonlinearity compensation (IC-NLC) in long-haul coherent optical transmission systems with a symbol rate o…
SAMA-IR: comprehensive input refinement methodology for optical networks with field-trial validation
Yihao Zhang, Qizhi Qiu, Xiaomin Liu +4
We propose a novel input refinement methodology incorporating sensitivity analysis and memory-aware weighting for jointly refining numerous diverse inputs. Field trials show ~2.5 d…
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…