提出新方法提升5G/6G网络流量预测精度，避免数据泄露导致的误差。
AI-Enhanced Spatial Cellular Traffic Demand Prediction with Contextual Clustering and Error Correction for 5G/6G Planning
- 分两阶段划分数据，结合上下文信息减少空间邻近区域的泄露问题。
- 在加拿大五大城市测试中，平均绝对误差显著低于仅依赖位置聚类的方法。
- 适合网络规划、频谱分配等需要高可靠性的通信系统设计者使用。
精确的蜂窝网络流量空间预测对5G NR容量规划、网络密化及数据驱动的6G规划至关重要。尽管机器学习能融合异构地理空间与社会经济数据以生成细粒度需求图，但空间自相关性在简单训练/测试划分下会导致邻域泄露，虚高准确率并削弱规划可靠性。本文提出一种AI驱动框架，通过上下文感知的两阶段划分策略与残差空间误差修正，降低泄露风险并提升空间泛化能力。基于加拿大五大主要城市的人群轨迹使用指标实验表明，该方法相较仅基于位置聚类的方案，在多个城市均实现一致的均方绝对误差（MAE）下降，支持更可靠的带宽配置及证据驱动的频谱规划与共享评估。
原文摘要 · Abstract (English)
Accurate spatial prediction of cellular traffic demand is essential for 5G NR capacity planning, network densification, and data-driven 6G planning. Although machine learning can fuse heterogeneous geospatial and socio-economic layers to estimate fine-grained demand maps, spatial autocorrelation can cause neighborhood leakage under naive train/test splits, inflating accuracy and weakening planning reliability. This paper presents an AI-driven framework that reduces leakage and improves spatial generalization via a context-aware two-stage splitting strategy with residual spatial error correction. Experiments using crowdsourced usage indicators across five major Canadian cities show consistent mean absolute error (MAE) reductions relative to location-only clustering, supporting more reliable bandwidth provisioning and evidence-based spectrum planning and sharing assessments.
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