MeshHSTGT: hierarchical spatio-temporal fusion for mesh network traffic forecasting
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces MeshHSTGT, a new framework for accurate wireless mesh network traffic forecasting. It improves prediction accuracy by decoupling and fusing complex traffic dynamics, outperforming existing models.
Area Of Science
- Computer Science
- Network Engineering
- Telecommunications
Background
- Accurate traffic forecasting is crucial for wireless mesh networks (WMNs) in 6G scenarios.
- Existing spatio-temporal models struggle with feature entanglement, hindering the decoupling of periodic, topological, and transient traffic dynamics.
Purpose Of The Study
- To develop a novel hierarchical spatio-temporal framework, MeshHSTGT, for enhanced traffic forecasting in WMNs.
- To address the limitations of sequential architectures in capturing multi-domain dependencies.
Main Methods
- MeshHSTGT integrates TimesNet for multi-periodic temporal-frequency analysis.
- It employs a Channel Capacity-Weighted Graph Convolutional Network (CCW-GCN) with Temporal Encoding GRU (TE-GRU) for topology-aware learning.
- A parallel feature re-extraction paradigm and Transformer-based adaptive alignment module are used for dynamic multi-domain feature fusion.
Main Results
- MeshHSTGT demonstrated significant reductions in Mean Absolute Error (MAE) by 5.4-31.4% and Root Mean Square Error (RMSE) by 13.3-19.5% compared to state-of-the-art baselines.
- The framework showed a 40% improvement in handling irregular traffic spikes.
- Performance gains were observed across short- to long-term forecasting tasks on real-world datasets.
Conclusions
- MeshHSTGT effectively decouples and fuses multi-domain traffic dynamics for improved forecasting accuracy in WMNs.
- The parallelized multi-domain modeling approach is essential for superior performance, especially in handling traffic anomalies.
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