113 citations · 425 across the 25 of their papers we have counts for
25 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…
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…
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…
First Field Trial of LLM-Powered AI Agent for Lifecycle Management of Autonomous Driving Optical Networks
Xiaomin Liu, Qizhi Qiu, Yihao Zhang +4
We design and demonstrate the first field trial of LLM-powered AI Agent for ADON. Three operation modes of the Agent are proposed for network lifecycle management. The Agent effici…
Preamble Design for Joint Frame Synchronization, Frequency Offset Estimation, and Channel Estimation in Upstream Burst-mode Detection of Coherent PONs
Yongxin Sun, Hexun Jiang, Yicheng Xu +5
Coherent optics has demonstrated significant potential as a viable solution for achieving 100 Gb/s and higher speeds in single-wavelength passive optical networks (PON). However, u…
Heuristic Optimization of Amplifier Reconfiguration Process for Autonomous Driving Optical Networks
Qizhi Qiu, Xiaomin Liu, Yihao Zhang +3
We propose a heuristic-based optimization scheme for reliable optical amplifier reconfiguration process in ADON. In the experiment on a commercial testbed, the scheme prevents a 1.…
Improved Physics-based Raman Amplifier Model in C+L Networks through Input Parameter Refinement
Yihao Zhang, Xiaomin Liu, Qizhi Qiu +4
We propose a n input parameter refinement scheme for the physics-based Raman amplifier model. Experiments over C+L band are conducted. Results show the scheme can lower the physica…
Building a digital twin of EDFA: a grey-box modeling approach
Yichen Liu, Xiaomin Liu, Yihao Zhang +6
To enable intelligent and self-driving optical networks, high-accuracy physical layer models are required. The dynamic wavelength-dependent gain effects of non-constant-pump erbium…
Multicarrier Modulation-Based Digital Radio-over-Fibre System Achieving Unequal Bit Protection with Over 10 dB SNR Gain
Yicheng Xu, Yixiao Zhu, Xiaobo Zeng +5
We propose a multicarrier modulation-based digital radio-over-fibre system achieving unequal bit protection by bit and power allocation for subcarriers. A theoretical SNR gain of 1…
Fast predicting the complex nonlinear dynamics of mode-locked fiber laser by a recurrent neural network with prior information feeding
Guoqing Pu, Runmin Liu, Hang Yang +4
As an imperative method of investigating the internal mechanism of femtosecond lasers, traditional femtosecond laser modeling relies on the split-step Fourier method (SSFM) to iter…
A Grey-box Launch-profile Aware Model for C+L Band Raman Amplification
Yihao Zhang, Xiaomin Liu, Yichen Liu +3
Based on the physical features of Raman amplification, we propose a three-step modelling scheme based on neural networks (NN) and linear regression. Higher accuracy, less data requ…
Physics-informed EDFA Gain Model Based on Active Learning
Xiaomin Liu, Yuli Chen, Yihao Zhang +4
We propose a physics-informed EDFA gain model based on the active learning method. Experimental results show that the proposed modelling method can reach a higher optimal accuracy…
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…
An Interpretable Mapping from a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization
Zhiqun Zhai, Hexun Jiang, Mengfan Fu +4
In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical…
Low Complexity Component Nonlinear Distortions Mitigation Scheme for Probabilistically Shaped 64-QAM Signals
Yiwen Wu, Mengfan Fu, Huazhi Lun +3
We propose a degenerated hierarchical look-up table (DH-LUT) scheme to compensate component nonlinearities. For probabilistically shaped 64-QAM signals, it achieves up to 2-dB SNR…
A Data-Fusion-Assisted Telemetry Layer for Autonomous Optical Networks
Xiaomin Liu, Huazhi Lun, Ruoxuan Gao +4
For further improving the capacity and reliability of optical networks, a closed-loop autonomous architecture is preferred. Considering a large number of optical components in an o…
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…
First insight into the regime transition dynamics of mode-locked fibre lasers
Guoqing Pu, Lilin Yi, Li Zhang +3
Mode-locked lasers exhibit complex nonlinear dynamics. Precise observation of these dynamics will aid in understanding of the underlying physics and provide new insights for laser…
Real-time programmable mode-locked fiber laser with a human-like algorithm
Guoqing Pu, Lilin Yi, Li Zhang +1
Nonlinear polarized evolution-based passively mode-locked fiber lasers with ultrafast and high peak power pulses are a powerful tool for engineering applications and research. Howe…
Application of Machine Learning in Fiber Nonlinearity Modeling and Monitoring for Elastic Optical Networks
Qunbi Zhuge, Xiaobo Zeng, Huazhi Lun +3
Fiber nonlinear interference (NLI) modeling and monitoring are the key building blocks to support elastic optical networks (EONs). In the past, they were normally developed and inv…