Feature Name Decoration Enhanced Router Performance Prediction by a Large Language Model

Future carrier networks for 6G are expected to verify performance across heterogeneous networks integrating multiple technologies. This integration requires effective verification of network performance under unpredictable conditions. In response, a node modeling method has been proposed for digitally evaluating the performance of actual network nodes. However, creating accurate node models is challenging due to the high cost and difficulty of collecting extensive real-world data under various environmental conditions. Therefore, the objective of this study is to improve the performance of actual network nodes using limited real-world datasets. We investigate the potential of natural language processing technologies to improve the accuracy of router performance estimation. In this paper, we propose a Feature Name Decoration (FND) method using Large Language Models (LLMs) to predict actual router metrics. The FND can help clarify the relationships between specific features, such as router settings and traffic conditions, and their impact on router metrics. The results show that the proposed FND improves the estimation accuracy of actual router performance metrics, including throughput, packet loss rate, and packet delay.