Path Loss in Urban LoRa Networks: A Large-Scale Measurement Study

TL;DR

This large-scale measurement study evaluates various path loss models in urban LoRa networks, providing insights into model accuracy and data requirements for network planning in smart cities.

Contribution

The paper offers a comprehensive comparison of 11 path loss models using extensive real-world data and introduces new coefficients for the Log-distance model.

Findings

Log-distance and other empirical models perform well in urban environments.

Terrain-based models like ITM and ITWOM show no advantages in this setting.

Provides estimations for sample size needed in similar measurement campaigns.

Abstract

Urban LoRa networks promise to provide a cost-efficient and scalable communication backbone for smart cities. One core challenge in rolling out and operating these networks is radio network planning, i.e., precise predictions about possible new locations and their impact on network coverage. Path loss models aid in this task, but evaluating and comparing different models requires a sufficiently large set of high-quality received packet power samples. In this paper, we report on a corresponding large-scale measurement study covering an urban area of 200km2 over a period of 230 days using sensors deployed on garbage trucks, resulting in more than 112 thousand high-quality samples for received packet power. Using this data, we compare eleven previously proposed path loss models and additionally provide new coefficients for the Log-distance model. Our results reveal that the Log-distance model and other well-known empirical models such as Okumura or Winner+ provide reasonable estimations in an urban environment, and terrain based models such as ITM or ITWOM have no advantages. In addition, we derive estimations for the needed sample size in similar measurement campaigns. To stimulate further research in this direction, we make all our data publicly available.