Beyond Static Forecasting: Unleashing the Power of World Models for Mobile Traffic Extrapolation
2026-04-09 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors address predicting mobile network traffic, which helps in planning and optimizing wireless networks. They introduce MobiWM, a model that learns how mobile traffic changes when network settings like power and antenna angles are adjusted. MobiWM combines different types of data, such as images and time series, to better understand spatial patterns and can simulate many future scenarios to aid decision-making. Tests show MobiWM predicts traffic more accurately than existing models and can be used to improve network management with reinforcement learning.
mobile traffic predictionwireless network optimizationworld modelmultimodal data fusionspatial semanticsnetwork parametersreinforcement learningdigital twincounterfactual simulation
Authors
Xiaoqian Qi, Haoye Chai, Yue Wang, Yong Li
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.