Automation of Fast Configuration Error Diagnosis in Optical Transport Networks – Natural Language Processing is All You Need

Wang, Cen; Yoshikane, Noboru; Elson, Daniel J.; Tsuritani, Takehiro

doi:10.1364/ofc.2023.m3g.6

Optical Fiber Communication Conference (OFC) 2023 2023

DOI: 10.1364/ofc.2023.m3g.6

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Automation of Fast Configuration Error Diagnosis in Optical Transport Networks – Natural Language Processing is All You Need

Cen Wang

Noboru Yoshikane

Daniel J. Elson

et al.

Abstract: We train language models to automate the diagnosis of OTN configuration errors, and the diagnostic accuracy is up to 97.56%. We additionally demonstrate the effectiveness of the models on a real OTN system.

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2024

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When Large Language Models Meet Optical Networks: Paving the Way for Automation

Wang,

Jiang

et al. 2024

Electronics

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Since the advent of GPT, large language models (LLMs) have brought about revolutionary advancements in all walks of life. As a superior natural language processing (NLP) technology, LLMs have consistently achieved state-of-the-art performance in numerous areas. However, LLMs are considered to be general-purpose models for NLP tasks, which may encounter challenges when applied to complex tasks in specialized fields such as optical networks. In this study, we propose a framework of LLM-empowered optical networks, facilitating intelligent control of the physical layer and efficient interaction with the application layer through an LLM-driven agent (AI-Agent) deployed in the control layer. The AI-Agent can leverage external tools and extract domain knowledge from a comprehensive resource library specifically established for optical networks. This is achieved through user input and well-crafted prompts, enabling the generation of control instructions and result representations for autonomous operation and maintenance in optical networks. To improve LLM’s capability in professional fields and stimulate its potential on complex tasks, the details of performing prompt engineering, establishing domain knowledge library, and implementing complex tasks are illustrated in this study. Moreover, the proposed framework is verified on two typical tasks: network alarm analysis and network performance optimization. The good response accuracies and semantic similarities of 2400 test situations exhibit the great potential of LLM in optical networks.

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When Large Language Models Meet Optical Networks: Paving the Way for Automation

Wang,

Jiang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

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Automation of Fast Configuration Error Diagnosis in Optical Transport Networks – Natural Language Processing is All You Need

Abstract: We train language models to automate the diagnosis of OTN configuration errors, and the diagnostic accuracy is up to 97.56%. We additionally demonstrate the effectiveness of the models on a real OTN system.

Cited by 1 publication

References 3 publications

When Large Language Models Meet Optical Networks: Paving the Way for Automation

When Large Language Models Meet Optical Networks: Paving the Way for Automation

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