Betastack: Enhancing base station traffic prediction with network-specific Large Language Models

Abstract

Accurate traffic forecasting in base station networks is crucial for efficient network management, resource allocation, and ensuring quality of service. This paper introduces BetaStack, a novel network-specific Large Language Model (LLM) designed to enhance base station traffic prediction. Unlike existing approaches, BetaStack incorporates physical constraints and a specialized network protocol embedding layer that captures the hierarchical structure of network traffic data. Through fine-tuning with these network-specific adaptations and a self-regressive prediction mechanism, BetaStack effectively leverages the powerful sequence modeling capabilities of LLMs to address the intricacies of network traffic. Extensive experiments on real-world data from base station cells in Guangdong, China demonstrate that BetaStack achieves significant performance improvements over both state-of-the-art time-series forecasting models and specialized network traffic prediction models. These results underscore the potential of BetaStack to improve the accuracy of network traffic prediction, enabling more efficient network management. The code can be found in https://github.com/lqf0624/BetaStack.git.