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[2602.13282] GraFSTNet: Graph-based Frequency SpatioTemporal Network for Cellular Traffic Prediction
Summary
The paper presents GraFSTNet, a novel framework for cellular traffic prediction that integrates spatio-temporal modeling with time-frequency analysis, addressing limitations of previous methods.
Why It Matters
As cellular networks expand and mobile device usage increases, accurate traffic prediction becomes critical for network management. GraFSTNet improves prediction accuracy by effectively modeling complex spatio-temporal dependencies, which is essential for optimizing network performance and resource allocation.
Key Takeaways
- GraFSTNet combines spatio-temporal modeling with time-frequency analysis for better traffic prediction.
- The framework uses an attention mechanism to capture inter-cell dependencies without relying on predefined topologies.
- An adaptive-scale LogCosh loss function is introduced to stabilize prediction accuracy across varying traffic intensities.
- Experiments show GraFSTNet outperforms existing state-of-the-art methods on multiple datasets.
- The approach addresses the challenges posed by the complex dynamics of cellular traffic data.
Computer Science > Networking and Internet Architecture arXiv:2602.13282 (cs) [Submitted on 6 Feb 2026] Title:GraFSTNet: Graph-based Frequency SpatioTemporal Network for Cellular Traffic Prediction Authors:Ziyi Li, Hui Ma, Fei Xing, Chunjiong Zhang, Ming Yan View a PDF of the paper titled GraFSTNet: Graph-based Frequency SpatioTemporal Network for Cellular Traffic Prediction, by Ziyi Li and 4 other authors View PDF HTML (experimental) Abstract:With rapid expansion of cellular networks and the proliferation of mobile devices, cellular traffic data exhibits complex temporal dynamics and spatial correlations, posing challenges to accurate traffic prediction. Previous methods often focus predominantly on temporal modeling or depend on predefined spatial topologies, which limits their ability to jointly model spatio-temporal dependencies and effectively capture periodic patterns in cellular traffic. To address these issues, we propose a cellular traffic prediction framework that integrates spatio-temporal modeling with time-frequency analysis. First, we construct a spatial modeling branch to capture inter-cell dependencies through an attention mechanism, minimizing the reliance on predefined topological structures. Second, we build a time-frequency modeling branch to enhance the representation of periodic patterns. Furthermore, we introduce an adaptive-scale LogCosh loss function, which adjusts the error penalty based on traffic magnitude, preventing large errors from dominatin...
