Enhancing Wi-Fi Router Placement with Unsupervised Machine Learning for Improved Network Coverage and Performance

Abstract

The optimal placement of Wi-Fi routers is essential for ensuring strong and consistent wireless coverage, yet traditional approaches often fail to deliver comprehensive solutions. This study presents a novel application of unsupervised machine learning (ML) to improve Wi-Fi router placement by analyzing environmental factors, historical signal strength data, and user behavior patterns. Using a dataset of signal strength measurements from a multi-story building, we conducted feature engineering to identify key predictors and trained various ML models to predict the impact of router placement on signal performance. The models were assessed based on accuracy, robustness, and computational efficiency. Our results show that ML-driven placement strategies significantly reduce dead zones and enhance overall network performance. Moreover, the proposed approach streamlines the installation process and supports adaptive, realtime adjustments to changes in the environment or usage patterns, offering a scalable solution for modern network infrastructure. These findings have practical implications for network engineers and set the stage for future innovations in intelligent network design and optimization.