23 citations · 30 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
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…
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…