用轻量级sLSTM提升5G网络流量预测精度与泛化能力
Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting
- 采用sLSTM结合三层Conv3D，双路径建模时空特征
- 相较ConvLSTM降低23% MAE，泛化能力提升30%
- 适合大规模下一代网络部署，训练更稳定快速
准确的时空流量预测对5G及未来网络的智能资源管理至关重要。传统AI方法常因用户移动性难以捕捉复杂的时空模式。本文提出一种轻量级双路时空网络：利用Scalar LSTM（sLSTM）高效建模时序特征，通过三层Conv3D提取空间特征，融合层生成统一表示。该设计提升了梯度稳定性与收敛速度，显著降低预测误差。在真实数据集上的实验表明，相比ConvLSTM基线，平均绝对误差（MAE）降低23%，模型泛化能力提升30%，适用于大规模、下一代网络部署。
原文摘要 · Abstract (English)
Accurate spatiotemporal traffic forecasting is vital for intelligent resource management in 5G and beyond. However, conventional AI approaches often fail to capture the intricate spatial and temporal patterns that exist, due to e.g., the mobility of users. We introduce a lightweight, dual-path Spatiotemporal Network that leverages a Scalar LSTM (sLSTM) for efficient temporal modeling and a three-layer Conv3D module for spatial feature extraction. A fusion layer integrates both streams into a cohesive representation, enabling robust forecasting. Our design improves gradient stability and convergence speed while reducing prediction error. Evaluations on real-world datasets show superior forecast performance over ConvLSTM baselines and strong generalization to unseen regions, making it well-suited for large-scale, next-generation network deployments. Experimental evaluation shows a 23% MAE reduction over ConvLSTM, with a 30% improvement in model generalization.
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