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

arXiv: https://arxiv.org/abs/2604.08199 · HTML: https://mlsanity.com/paper/2604.08199/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2026.

## Built on by (most cited first, 1 of 1)

- [On the Divergence of Accuracy and Mechanism Consistency in Time Series World Models](https://mlsanity.com/paper/2610.01842/) (2026)

## Abstract (arXiv)

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.