UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimization

TL;DR

This paper introduces FoMo, a universal foundation model for mobile traffic forecasting that leverages diffusion models and transformers to improve accuracy and adaptability across diverse urban environments and tasks.

Contribution

The paper presents a novel foundation model, FoMo, combining diffusion models and transformers with task-specific masking and contrastive learning for versatile mobile traffic prediction.

Findings

FoMo outperforms existing models on 9 real-world datasets.

FoMo demonstrates superior zero/few-shot learning capabilities.

FoMo effectively handles diverse forecasting tasks across multiple cities.

Abstract

Mobile traffic forecasting allows operators to anticipate network dynamics and performance in advance, offering substantial potential for enhancing service quality and improving user experience. However, existing models are often task-oriented and are trained with tailored data, which limits their effectiveness in diverse mobile network tasks of Base Station (BS) deployment, resource allocation, energy optimization, etc. and hinders generalization across different urban environments. Foundation models have made remarkable strides across various domains of NLP and CV due to their multi-tasking adaption and zero/few-shot learning capabilities. In this paper, we propose an innovative Foundation model for Mo}bile traffic forecasting (FoMo), aiming to handle diverse forecasting tasks of short/long-term predictions and distribution generation across multiple cities to support network planning and optimization. FoMo combines diffusion models and transformers, where various spatio-temporal masks are proposed to enable FoMo to learn intrinsic features of different tasks, and a contrastive learning strategy is developed to capture the correlations between mobile traffic and urban contexts, thereby improving its transfer learning capability. Extensive experiments on 9 real-world datasets demonstrate that FoMo outperforms current models concerning diverse forecasting tasks and zero/few-shot learning, showcasing a strong universality.