AI-Enhanced Spatial Cellular Traffic Demand Prediction with Contextual Clustering and Error Correction for 5G/6G Planning
This work addresses the need for more reliable bandwidth provisioning and spectrum planning in 5G/6G networks, though it appears incremental by improving an existing method for a known bottleneck.
The paper tackled the problem of spatial autocorrelation causing neighborhood leakage in cellular traffic demand prediction for 5G/6G planning, resulting in consistent mean absolute error reductions relative to location-only clustering across five major Canadian cities.
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.