AI-Enhanced Spatial Cellular Traffic Demand Prediction with Contextual Clustering and Error Correction for 5G/6G Planning

• URL: http://arxiv.org/abs/2603.10800v1
• Date: Wed, 11 Mar 2026 14:11:37 GMT
• Title: AI-Enhanced Spatial Cellular Traffic Demand Prediction with Contextual Clustering and Error Correction for 5G/6G Planning
• Abstract summary: spatial autocorrelation can cause neighborhood leakage under naive train/test splits.<n>This paper presents an AI-driven framework that reduces leakage and improves spatial generalization.
• Score: 17.409018763425
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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