most citedFirst Field Trial of LLM-Powered AI Agent for Lifecycle Management of Autonomous Driving Optical Networks

1 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

physics.optics2025

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…

cs.MA2025

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…

physics.optics2025

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…

eess.SY2024★ 1 cited

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…

eess.SY2024

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.…

physics.optics2024★ 1 cited

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…