arXiv subjects

Colin Brown

Publications and source records attributed to Colin Brown.

3 recordsLinked to original sources

arXiv2026

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

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.

cs.LG↗

arXiv2026

AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation

Accurately forecasting spectrum demand is a key component for efficient spectrum resource allocation and management. With the rapid growth in demand for wireless services, mobile network operators and regulators face increasing challenges in ensuring adequate spectrum availability. This paper presents a data-driven approach leveraging artificial intelligence (AI) and machine learning (ML) to estimate and manage spectrum demand. The approach uses multiple proxies of spectrum demand, drawing from site license data and derived from crowdsourced data. These proxies are validated against real-world mobile network traffic data to ensure reliability, achieving an R$^2$ value of 0.89 for an enhanced proxy. The proposed ML models are tested and validated across five major Canadian cities, demonstrating their generalizability and robustness. These contributions assist spectrum regulators in dynamic spectrum planning, enabling better resource allocation and policy adjustments to meet future network demands.

eess.SY↗

arXiv2025

Similarity-Based Assessment of Computational Reproducibility in Jupyter Notebooks

Computational reproducibility refers to obtaining consistent results when rerunning an experiment. Jupyter Notebook, a web-based computational notebook application, facilitates running, publishing, and sharing computational experiments along with their results. However, rerunning a Jupyter Notebook may not always generate identical results due to various factors, such as randomness, changes in library versions, or variations in the computational environment. This paper introduces the Similarity-based Reproducibility Index (SRI) -- a metric for assessing the reproducibility of results in Jupyter Notebooks. SRI employs novel methods developed based on similarity metrics specific to different types of Python objects to compare rerun outputs against original outputs. For every cell generating an output in a rerun notebook, SRI reports a quantitative score in the range [0, 1] as well as some qualitative insights to assess reproducibility. The paper also includes a case study in which the proposed metric is applied to a set of Jupyter Notebooks, demonstrating how various similarity metrics can be leveraged to quantify computational reproducibility.

cs.SE↗