Denoising Refinement Diffusion Models for Simultaneous Generation of Multi-scale Mobile Network Traffic

• URL: http://arxiv.org/abs/2511.17532v2
• Date: Tue, 25 Nov 2025 03:14:35 GMT
• Title: Denoising Refinement Diffusion Models for Simultaneous Generation of Multi-scale Mobile Network Traffic
• Authors: Xiaoqian Qi, Haoye Chai, Sichang Liu, Lei Yue, Raoyuan Pan, Yue Wang, Yong Li,
• Abstract summary: We propose ZoomDiff, a diffusion-based model for multi-scale mobile traffic generation.<n>DRDM employs a multi-stage noise-adding and denoising mechanism, enabling different stages to generate traffic attemporal resolutions.<n>Experiments on real-world mobile traffic datasets show that ZoomDiff at least an 18.4% improvement over state-of-the-art baselines in multi-scale traffic generation tasks.
• Score: 7.300362420979762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The planning, management, and resource scheduling of cellular mobile networks require joint estimation of mobile traffic across different layers and nodes. Mobile traffic generation can proactively anticipate user demands and capture the dynamics of network load. However, existing methods mainly focus on generating traffic at a single spatiotemporal resolution, making it difficult to jointly model multi-scale traffic patterns. In this paper, we propose ZoomDiff, a diffusion-based model for multi-scale mobile traffic generation. ZoomDiff maps urban environmental context into mobile traffic with multiple spatial and temporal resolutions through a set of customized Denoising Refinement Diffusion Models (DRDM). DRDM employs a multi-stage noise-adding and denoising mechanism, enabling different stages to generate traffic at distinct spatiotemporal resolutions. This design aligns the progressive denoising process with hierarchical network layers, including base stations, cells, and grids of varying granularities. Experiments on real-world mobile traffic datasets show that ZoomDiff achieves at least an 18.4% improvement over state-of-the-art baselines in multi-scale traffic generation tasks. Moreover, ZoomDiff demonstrates strong efficiency and cross-city generalization, highlighting its potential as a powerful generative framework for modeling multi-scale mobile network dynamics.

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