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

2026-04-09

AI Summary

This work addresses the limitation of existing mobile traffic forecasting models, which capture only static long-term temporal patterns and fail to model the complex interactions between traffic dynamics and adaptive network parameter adjustments. To overcome this, we propose MobiWM, a world model for mobile networks that treats traffic as a system state and explicitly models its dynamic evolution in response to network actions—such as transmit power, azimuth, and mechanical/electrical tilt—while integrating multimodal environmental context from both images and sequential data. MobiWM enables open-ended, continuous action trajectory rollouts over unlimited time horizons and, for the first time, introduces world models to mobile traffic extrapolation by constructing an explorable counterfactual simulation environment. Evaluated on real-world variable-parameter data spanning 9 regions and 31,900 cells, MobiWM significantly outperforms existing methods in distributional fidelity and effectively supports downstream reinforcement learning optimization, paving the way for digital twin–driven wireless network management.

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.

Problem

• mobile traffic prediction
• network parameter adjustment
• dynamic interaction
• wireless network optimization
• temporal patterns

Innovation

• World Model
• Mobile Traffic Prediction
• Multimodal Fusion
• Counterfactual Simulation
• Digital Twin