Abstract: With the exponential growth in the number of 5G base stations and the surge in connected terminals,the scale of network traffic is expected to grow exponentially,exhibiting significant nonlinear,multimodal,and bursty characteristics,posing new challenges to network resource allocation and optimization..To address these challenges,this paper proposed a network traffic prediction method based on large language models(NT-LLM).This approach leverages reprogramming techniques to transform traditional network traffic data into a format suitable for LLMs,thus fully utilizing their advantages in cross-task reasoning and complex pattern recognition.With only a small amount of training data and a short training period,NT-LLM can efficiently handle complex network traffic patterns at different time scales.Experimental results demonstrate that compared to baseline models such as LSTM,Informer,and Transformer,the NT-LLM model significantly reduces the mean squared error of network traffic predictions across multiple regions by 44.26%,56.78%,and 51.36%,respectively.Furthermore,this method does not require extensive fine-tuning of pre-trained language models,showcasing strong scalability and adaptability.It maintains high prediction accuracy while reducing computational resource consumption.

Key words: Network traffic prediction, Large language model, Reprogramming, Time-series data, Deep learning