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

TL;DR

This paper introduces MobiWM, a world model for mobile networks that captures traffic dynamics and supports unlimited-horizon rollout for network planning, outperforming existing models in fidelity and enabling advanced optimization.

Contribution

MobiWM is the first world model for mobile traffic that models network dynamics with multimodal context fusion and supports counterfactual simulation for optimization.

Findings

MobiWM achieves superior distributional fidelity in traffic prediction.

It enables unlimited-horizon rollout for network adjustment trajectories.

A case study shows MobiWM's effectiveness in network optimization.

Abstract

Mobile traffic prediction is a fundamental yet challenging problem for wireless network planning and optimization. Existing models focus on learning static long-term temporal patterns in mobile traffic series, which limits their ability to capture the dynamics between mobile traffic and network parameter adjustments. In this paper, we propose MobiWM, a world model for mobile networks. Taking mobile traffic as the system state, MobiWM models the dynamics between the states and network parameter actions, including power, azimuth, mechanical tilt, and electrical tilt through a predictive backbone. It fuses multimodal environmental contexts, comprising both image and sequential data, with encoded actions, leveraging shared spatial semantics to enhance spatial understanding. Leveraging the capacity of world models to capture real-world operational dynamics, MobiWM supports unlimited-horizon rollout over continuous network-adjustment action trajectories, providing operators with an explorable counterfactual simulation environment for network planning and optimization. Extensive experiments on variable-parameter mobile traffic data covering 31,900 cells across 9 districts demonstrate that MobiWM achieves the best distributional fidelity across all evaluation scenarios, significantly outperforming existing traffic prediction baselines and representative world models. A downstream RL-based case study further validates MobiWM as a simulation environment for network optimization, establishing a new paradigm for digital twin-driven wireless network management.