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

TL;DR

ZoomDiff is a diffusion-based model that generates multi-scale mobile network traffic by capturing hierarchical spatial and temporal patterns, improving accuracy and generalization over existing single-resolution methods.

Contribution

The paper introduces ZoomDiff, a novel multi-stage diffusion model that jointly generates multi-scale mobile traffic aligned with network hierarchy, addressing limitations of single-resolution approaches.

Findings

Achieves at least 18.4% improvement over baselines in multi-scale traffic generation

Demonstrates strong efficiency and cross-city generalization

Effectively models hierarchical network layers from base stations to grids

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.