When Large Language Models Meet Optical Networks: Paving the Way for Automation
CoRR(2024)
Abstract
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 on 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 sematic similarities of 2,400 test situations
exhibit the great potential of LLM in optical networks.
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