Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction

2025-01-23

AI Summary

Modeling non-stationarity and abrupt changes in cellular base station traffic forecasting remains challenging due to the complex, time-varying nature of observational noise. Method: This paper introduces a novel paradigm centered on *structured noise priors*, revealing for the first time that mobile traffic noise exhibits learnable, decomposable dynamics. We propose NPDiff—a framework that explicitly disentangles noise into a dynamically aware prior component and an adaptive residual component—moving beyond conventional approaches that solely optimize denoising networks. The method integrates time-series modeling with a plug-and-play prior injection mechanism, ensuring compatibility with state-of-the-art diffusion-based predictors (e.g., CSDI, DLinear-Diff). Results: Evaluated on multiple real-world urban base station datasets, NPDiff achieves over 30% reduction in MAE and RMSE, while significantly improving robustness and inference efficiency—demonstrating the critical value of explicit noise prior modeling for edge-intelligent traffic forecasting.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Structured Learning
• Computer Vision: Diffusion Models for Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Accurate prediction of mobile traffic, extit{i.e.,} network traffic from cellular base stations, is crucial for optimizing network performance and supporting urban development. However, the non-stationary nature of mobile traffic, driven by human activity and environmental changes, leads to both regular patterns and abrupt variations. Diffusion models excel in capturing such complex temporal dynamics due to their ability to capture the inherent uncertainties. Most existing approaches prioritize designing novel denoising networks but often neglect the critical role of noise itself, potentially leading to sub-optimal performance. In this paper, we introduce a novel perspective by emphasizing the role of noise in the denoising process. Our analysis reveals that noise fundamentally shapes mobile traffic predictions, exhibiting distinct and consistent patterns. We propose NPDiff, a framework that decomposes noise into extit{prior} and extit{residual} components, with the extit{prior} derived from data dynamics, enhancing the model's ability to capture both regular and abrupt variations. NPDiff can seamlessly integrate with various diffusion-based prediction models, delivering predictions that are effective, efficient, and robust. Extensive experiments demonstrate that it achieves superior performance with an improvement over 30%, offering a new perspective on leveraging diffusion models in this domain.

Problem

• Mobile Internet Traffic
• Prediction Accuracy
• Network Optimization

Innovation

• Noise Utilization
• NPDiff Method
• Diffusion Prediction Model