Beyond Static Forecasting: Unleashing the Power of World Models for Mobile Traffic Extrapolation

Abstract

Mobile traffic prediction is a fundamental yet challenging problem for wireless network planning and optimization. Conventional models mainly learn static long-term temporal patterns and cannot capture the dynamics under network-parameter adjustments. Leveraging the advantage of world models in learning underlying dynamics, we propose MobiWM, a mobile network world model that treats cell traffic as states and antenna parameters as actions. MobiWM combines factorized spatio-temporal modelling with multimodal environmental context aligned through shared spatial semantics. Its learned action-state transitions enable iterative rollout over specified adjustment trajectories for counterfactual planning. Extensive experiments on massive variable-parameter mobile traffic datasets demonstrate that MobiWM outperforms baselines by at least 16.40% on average. A model-based Actor-critic case study further demonstrates its potential as a learned surrogate for network optimization.