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

Mohamad Alkadamani; Colin Brown; Halim Yanikomeroglu

cs.LGMar 11, 2026

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

Authors: Mohamad Alkadamani, Colin Brown, Halim Yanikomeroglu

Organizations: Innovation, Science and Economic Development Canada (ISED) · Carleton University · Communications Research Centre (CRC)

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.

Figures & tables

Fig. 2: Moran’s I versus distance (grid cells).

Fig. 3: Local Moran’s I clusters across five Canadian cities.

Fig. 4: Two-stage clustering and spatial error correction framework.

Fig. 5: Comparison of clustering techniques for Montreal.

City	Mean Absolute Error (MAE)			R 2 Gain (%)
	k-Means	Two-Stage	Two-Stage + SEM
Toronto	1532.8	1012.3	845.2	3.85
Montreal	1621.5	1123.6	825.0	2.87
Ottawa	1475.4	987.2	808.3	3.86
Vancouver	1398.6	953.5	783.4	4.88
Calgary	1450.7	1001.9	795.7	3.86

TABLE I: Performance Results Across Five Canadian Cities

Fig. 6: Learning curves comparison for different clustering strategies.

$\eta^{(\delta)}$ (bps/Hz)	k-Means	Two-Stage	Two-Stage + SEM
2.0	35.8	24.7	20.2
3.0	23.9	16.5	13.4
3.5	20.5	14.1	11.5

TABLE II: Sensitivity Table: “All Cities” BDE (MHz) for 3.5 GHz.

Fig. 7: Case study: bandwidth dimensioning error (BDE).

Fig. 8: All Cities: $P_{\mathrm{cong}}(B)$ versus $B$ under observed and predicted demand.

Traffic Forecasting

Traffic forecasting predicts future flows, speeds, demand, travel times, and congestion from spatial, temporal, and contextual data.

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Mar 10, 2026J/K move · Enter open · S save

A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks

Mohamad Alkadamani, Halim Yanikomeroglu, Amir Ghasemi

Department of Data Science, Communications Research Centre Canada · Department of Systems and Computer Engineering, Carleton University

Wireless Communications Graph Attention Networks

Open paper AskPDF arXiv

The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum sharing presents a promising avenue, although it demands precise characterization of spectrum demand for informed policy-making. This paper introduces HR-GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data. HR-GAT adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models, often resulting in poor generalization. Tested across five major Canadian cities, HR-GAT improves predictive accuracy of spectrum demand by 21% over eight baseline models, underscoring its superior performance and reliability.

Abstract

Traffic Forecasting

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