Research on Mobile Network Optimization Based on Collaboration of Large and Small Models
It proposes an intelligent mobile network optimization method based on the collaboration of large and small models.Firstly,we utilize large language models to process and understand unstructured data such as network logs and external open-source data,extracting key information.Secondly,a knowledge graph is constructed,encompassing multidimensional information such as network equipment,parameter configurations,and expert optimization experience,to analyze the relationship between network status and optimization requirements.Then,using specialized tools and models such as deep learning and graph neural networks for root cause analysis,network fault points can be quickly identified.Based on a knowledge graph database and the problem reasoning capabilities of large models,the proposed method can provide specific solutions to front-line employees.Finally,through the implementation and validation in real scenarios,experts and front-line employees evaluate and provide feedback on the proposed solutions.This evaluation and feedback information is continuously collected and fed back to the previous layers,forming a loop of continuous optimization.
Mobile network optimizationCollaboration of large and small modelsKnowledge graphGraph neural networkLarge language model
NETL
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