• Computer Science > Networking and Internet Architecture This paper has been withdrawn by Hui Ma [Submitted on 6 Feb 2026 (v1), last revised 19 Feb 2026 (this version, v2)] Title:GraFSTNet: Graph-based Frequency SpatioTemporal Network for Cellular Traffic Prediction No PDF available, click to view other formatsAbstract: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 dominating the training process and helping the model maintain relatively stable prediction accuracy across different traffic intensities.
Article Summaries:
- Computer Science > Networking and Internet Architecture This paper has been withdrawn by Hui Ma [Submitted on 6 Feb 2026 (v1), last revised 19 Feb 2026 (this version, v2)] Title:GraFSTNet: Graph-based Frequency SpatioTemporal Network for Cellular Traffic Prediction No PDF available, click to view other formatsAbstract: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
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