想搞LoRa网络规划？这篇用真实数据告诉你ML模型比传统模型准多少，还分析了换网关会翻车的情况，挺实在的。
针对LoRa智能城市部署，研究者用真实城市数据系统分析机器学习路径损耗预测精度随训练集规模的变化。随机森林结合LiDAR地形特征，k近邻使用坐标数据，两者在随机池化分割下均优于经验模型和专用LPWAN模型。最大训练规模时，ML模型RMSE低于6.5 dB，最佳基线为9.7 dB。留一网关验证显示，随机森林对未见网关有位置依赖的迁移，坐标k近邻在网关位置未见时大幅退化。
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 pooled splits, both ML models consistently outperform the considered baseline models across the evaluated training-set sizes. At maximum training size, they achieve RMSE values below 6.5 dB compared to 9.7 dB for the best baseline, indicating accurate within-deployment interpolation. A leave-one-gateway-out check qualifies this result: RF shows placement-dependent transfer to held-out gateways, with moderate degradation for several gateways but larger errors for others, whereas coordinate-only k-NN degrades substantially when the gateway location is unseen