MobiWorld: World Models for Mobile Wireless Network

Abstract

Accurate modeling and simulation of mobile networks are essential for enabling intelligent and cost-effective network optimization. In this paper, we propose MobiWorld, a generative world model designed to support high-fidelity and flexible environment simulation for mobile network planning and optimization. Unlike traditional predictive models constrained by limited generalization capabilities, MobiWorld exhibits strong universality by integrating heterogeneous data sources, including sensors, mobile devices, and base stations, as well as multimodal data types such as sequences and images. It is capable of generating both network element-level observations (e.g., traffic load, user distribution) and system-level performance indicators (e.g., throughput, energy consumption) to support a wide range of planning and optimization tasks. Built upon advanced diffusion models, MobiWorld offers powerful controllable generation capabilities by modeling the joint distribution between mobile network data and diverse conditional factors including spatio temporal contexts, user behaviors, and optimization policies. This enables accurate simulation of dynamic network states under varying policy configurations, providing optimization agents with precise environmental feedback and facilitating effective decision-making without relying on costly real-network interactions. We demonstrate the effectiveness of MobiWorld in a collaborative energy-saving scenario, where an agent uses observations and rewards generated by MobiWorld to optimize base station sleep and user offloading policies. Experimental results show that MobiWorld exhibits strong controllable generation performance and outperforms traditional methods in energy optimization.