Per-Link Path Loss Estimation Method in Low-Power Wide-Area Networks via Geographical Clustering: Experimental Results Using LoRa

Path loss modeling is essential for the design, analysis, and applications (e.g., localization) of low-power wide-area networks (LPWANs). Conventional models typically rely on coarse regional land cover classifications (e.g., urban or suburban), which fail to capture the direction-dependent path loss variations of long-range LPWAN links that traverse heterogeneous environments. Although per-link modeling and geographical clustering have individually shown promise in addressing these limitations, their combined potential remains unexplored. This paper presents GeoSeg, a path loss modeling approach that integrates per-link modeling with geographical clustering. GeoSeg represents the propagation environment between each transmitter-receiver pair as a variable-length sequence that encodes both land cover types and their spatial arrangement and employs a hidden Markov model (HMM)-based clustering method to group these sequences into subregions. A per-subregion path loss exponent is then estimated for each identified subregion, enabling spatially adaptive path loss estimation. Evaluated using an open-access LoRaWAN dataset, the preliminary results demonstrate median MAE reductions of up to 96% across the evaluated clusters compared with the standard log-distance path loss model. These results suggest that integrating per-link environmental characterization with geographical clustering can potentially improve path loss estimation accuracy in heterogeneous LPWAN deployments.