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Submitted
Apr 25, 2026
Accepted
Jun 1, 2026
Published
Jun 8, 2026
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Abstract

This study investigates discrepancies between classical propagation models (Okumura-Hata, CI, FI) and LoRa field measurements in urban and rural environments. While rural areas exhibited linear path loss trends, urban scenarios showed distinct signal saturation, resulting in significant residuals even after model optimization. To address this, a residual-based analysis using Machine Learning (Linear Regression, Decision Tree, Random Forest) was proposed to map these systematic errors. The evaluation reveals that Random Forest (RF) significantly outperforms other algorithms, achieving an of 0.961 and an RMSE of 3.291 dB. These findings demonstrate that model mismatches follow deterministic patterns driven by environmental features rather than random noise. The study concludes that integrating ML-based residual compensation is essential for accurate radio planning in heterogeneous network deployments.

Keywords

LoRa machine learning path loss propagation models residual analysis

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
1.
Alan MBF, Puspitaningayu P, Nurhayati N, Muhammad 'Aamir Nashrullah, Funabiki N, Sutanto E, et al. Residual-Based Analysis of the Mismatch Between LoRa Channel Models and Field Measurements in Urban and Rural Environments. EKSAKTA [Internet]. 2026 Jun. 8 [cited 2026 Jun. 10];27(03):296-313. Available from: http://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/683

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