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Updated: May 28, 2026

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
Published on: June 25, 2021
Experimental Comparison and Empirical Path Loss Modeling of LoRa Communication in Line-of-Sight and Forest
Kamol Boonlom1, Jarun Khonrang1, Prayoot Akkaraekthalin2
1Faculty of Industrial Technology, Chiang Rai Rajabhat University, Chiang Rai 57100, Thailand.
None:
This study presents a measurement-driven comparison of LoRa communication performance in two tropical deployment scenarios at 923.2 MHz: an open line-of-sight (LOS) path and a forest-obstructed path. To ensure a controlled comparison, both scenarios were evaluated over the same transmission distance of 1.2 km using identical radio configuration, antenna heights, and hardware settings. Field measurements were conducted from 50 m to 1.2 km in 50 m increments, with three repeated measurements at each distance point. The measured RSSI decreased from -60.52 dBm to -89.48 dBm in the LOS case and from -77.62 dBm to -114.62 dBm in the forested case. Using a bandwidth of 125 kHz and a receiver noise figure of 6 dB, the corresponding estimated SNR at 1.2 km was 27.55 dB for the LOS path and 2.41 dB for the forested path. Relative to the free-space baseline, the measured LOS link showed a deviation of 31.14 dB at 1.2 km, while the forested link showed a deviation of 56.28 dB. The additional attenuation specifically associated with the forested environment was approximately 25.14 dB, with a mean excess loss of 24.70 dB over the full route. Regression analysis further yielded effective path-loss exponents of 2.31 for the LOS case and 3.22 for the forested case. Based on these results, a site-specific empirical correction approach and an approximate 25 dB first-order design margin are suggested for preliminary LoRa link-budget planning in similar tropical vegetated environments. The findings indicate that free-space-only prediction may be insufficient for practical deployment and that measurement-driven correction can improve the realism of wireless sensor network design in vegetation-rich environments.
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