Spatio-Temporal Knowledge Driven Diffusion Model for Mobile Traffic Generation

Generating mobile traffic in urban environments is important for network planning and optimization. However, existing models show weakness in capturing spatial-temporal dynamics between mobile traffic and urban environments. This makes it difficult for the models to generate high-fidelity traffic data and control the generation process across different regions in large-scale urban environments, ultimately affecting the effectiveness of optimization strategies. In this paper, we propose a Spatio-Temporal Knowledge-driven Diffusion model (STK-Diff) for controllable mobile traffic generation. We construct an Urban Knowledge Graph (UKG) to fully characterize the urban features, which incorporates both the spatial and semantic relations of different entities, such as base stations, business areas, and functional regions. Based on the constructed UKG, we design the denoising network of diffusion model with a temporal extraction module and a spatial connection module. These two modules capture the correlations of mobile traffic and environment features via a frequency attention mechanism and spatial graph learning scheme, so as to make a strong controllability on the generated mobile traffic. Extensive experiments on three real-world datasets show that the proposed framework not only improves generation fidelity by up to 19%, but also enhances the controllability to generate specific patterns, with a gain of surpassing 15%.