98 citations · 223 across the 23 of their papers we have counts for
24 papers
Artificial Intelligence in a Photonic Temporal Processor
Youlve Chen, Jinlong Xiang, Yimin Hu +13
Optical neural networks (ONNs) promise high-throughput and energy-efficient artificial intelligence, yet essentially all implementations so far encode information across space eith…
First Demonstration of Multi-Agent LLM System for Million-Scale Optical Link Management in Global Production AIDCs
Jingyi Su, Yihao Zhang, Dianxuan Fu +15
We present the first LLM-powered multi-agent system for autonomous fault management across millions of optical links in production AIDCs. Refined via SFT and continuous memory evol…
CBA: Communication-Bound-Aware Cross-Domain Resource Assignment for Pipeline-Parallel Distributed LLM Training in Dynamic Multi-DC Optical Networks
Dianxuan Fu, Xiaomin Liu, Yihao Zhang +3
We propose a communication-bound-aware cross-domain resource assignment framework for pipeline-parallel distributed training over multi-datacenter optical networks, which lowers it…
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…
First Field-Trial Demonstration of L4 Autonomous Optical Network for Distributed AI Training Communication: An LLM-Powered Multi-AI-Agent Solution
Yihao Zhang, Qizhi Qiu, Xiaomin Liu +7
We demonstrate the first cross-domain cross-layer level-4 autonomous optical network via a multi-AI-agent system. Field trials show ~98% task completion rate across the distributed…
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…
Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach
Yajie Sheng, Bin Chen, Yi Lei +5
Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provid…
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.…
Dual-Tap Optical-Digital Feedforward Equalization Enabling High-Speed Optical Transmission in IM/DD Systems
Yu Guo, Yangbo Wu, Zhao Yang +6
Intensity-modulation and direct-detection (IM/DD) transmission is widely adopted for high-speed optical transmission scenarios due to its cost-effectiveness and simplicity. However…
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…
Four-dimensional direct detection with Jones space optical full-field recovery
Qi Wu, Yixiao Zhu, Hexun Jiang +4
Data centers, the engines of the global Internet, are supported by massive high-speed optical interconnects. In optical fiber communication, the classic direct detection obtains on…
Optical Field Recovery in Jones Space
Qi Wu, Yixiao Zhu, Hexun Jiang +2
Optical full-field recovery makes it possible to compensate for fiber impairments such as chromatic dispersion and polarization mode dispersion (PMD) in the digital signal processi…
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…
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…
Neural Network Training for OSNR Estimation -- From Prototype to Product
Andrew D. Shiner, Mohammad E. Mousa-Pasandi, Meng Qiu +6
A method for in-service OSNR measurement with a coherent transceiver is presented and experimentally verified. A neural network is employed to identify and remove the nonlinear noi…
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…
Single wavelength 480 Gb/s direct detection over 80km SSMF enabled by Stokes Vector Kramers Kronig transceiver
Thang Hoang, Mohammed Sowailem, Qunbi Zhuge +6
We propose 4D modulation with directed detection employing a novel Stokes-Vector Kramers-Kronig transceiver. It shows that employing Stokes vector receiver, transmitted digital car…