A Systematic Sample Size Analysis of ML-Based Path Loss Prediction for LPWAN
Low Power Wide Area Networks like LoRa are increasingly deployed for smart city applications, requiring accurate path loss prediction for effective network planning. Traditional (empirical) propagation models often exhibit limited accuracy in these scenarios. We investigate machine learning models for LoRa path loss prediction, systematically analyzing how prediction accuracy scales with training set size using real-world measurements from an urban deployment. Our approach employs a Random Forest with LiDAR-derived terrain features and k-Nearest Neighbors with coordinate data, comparing their performance against established empirical models and specialized LPWAN models. Under random poole...
| Authors | Robert Bitterling, Christian Nettersheim, Jörn Hees et al. |
|---|---|
| Primary category | cs.NI |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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