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<p>Accurate traffic forecasting in wireless mesh networks is critical for optimizing resource allocation and ensuring ultra-reliable low-latency communication in 6G-enabled scenarios. However, existing models often suffer from feature entanglement in sequential spatio-temporal architectures, limiting their ability to decouple multi-domain dependencies (e.g., periodic, topological, and transient dynamics). To address this, we propose MeshHSTGT, a novel hierarchical spatio-temporal framework that synergizes TimesNet for multi-periodic temporal-frequency modeling and a Channel Capacity-Weighted Graph Convolutional Network (CCW-GCN) with Temporal Encoding GRU (TE-GRU) for topology-aware spatial-temporal dependency learning. Unlike conventional serial architectures, MeshHSTGT employs a parallel feature re-extraction paradigm to independently capture domain-specific patterns, followed by a Transformer-based adaptive alignment module to dynamically fuse multi-domain features via self-attention. Experiments on real-world mesh network datasets and the Milan cellular traffic benchmark demonstrate that MeshHSTGT reduces MAE by 5.4-31.4% and RMSE by 13.3-19.5% over state-of-the-art baselines (e.g., TSGAN, STFGNN) across short- to long-term forecasting tasks. Ablation studies validate the necessity of parallelized multi-domain modeling, highlighting a 40% improvement in handling irregular traffic spikes compared to serial counterparts.</p>

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MeshHSTGT: hierarchical spatio-temporal fusion for mesh network traffic forecasting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Sunlei Qian,
  • Xiaorong Zhu

摘要

Accurate traffic forecasting in wireless mesh networks is critical for optimizing resource allocation and ensuring ultra-reliable low-latency communication in 6G-enabled scenarios. However, existing models often suffer from feature entanglement in sequential spatio-temporal architectures, limiting their ability to decouple multi-domain dependencies (e.g., periodic, topological, and transient dynamics). To address this, we propose MeshHSTGT, a novel hierarchical spatio-temporal framework that synergizes TimesNet for multi-periodic temporal-frequency modeling and a Channel Capacity-Weighted Graph Convolutional Network (CCW-GCN) with Temporal Encoding GRU (TE-GRU) for topology-aware spatial-temporal dependency learning. Unlike conventional serial architectures, MeshHSTGT employs a parallel feature re-extraction paradigm to independently capture domain-specific patterns, followed by a Transformer-based adaptive alignment module to dynamically fuse multi-domain features via self-attention. Experiments on real-world mesh network datasets and the Milan cellular traffic benchmark demonstrate that MeshHSTGT reduces MAE by 5.4-31.4% and RMSE by 13.3-19.5% over state-of-the-art baselines (e.g., TSGAN, STFGNN) across short- to long-term forecasting tasks. Ablation studies validate the necessity of parallelized multi-domain modeling, highlighting a 40% improvement in handling irregular traffic spikes compared to serial counterparts.