Experimental Evaluation of Empirical NB-IoT Propagation Modelling in a Deep-Indoor Scenario

TL;DR

This study experimentally evaluates NB-IoT signal propagation in deep-indoor tunnel environments, revealing limitations of existing models and identifying new influential features for more accurate path-loss prediction.

Contribution

The paper provides an experimental analysis of NB-IoT propagation in deep-indoor tunnels and introduces the average distance to the nearest corridor as a new predictive feature.

Findings

Existing empirical models are inaccurate in deep-indoor scenarios.

Indoor distance and penetration depth poorly explain signal attenuation.

Average distance to the nearest corridor is a promising feature for modeling.

Abstract

Path-loss modelling in deep-indoor scenarios is a difficult task. On one hand, the theoretical formulae solely dependent on transmitter-receiver distance are too simple; on the other hand, discovering all significant factors affecting the loss of signal power in a given situation may often be infeasible. In this paper, we experimentally investigate the influence of deep-indoor features such as indoor depth, indoor distance and distance to the closest tunnel corridor and the effect on received power using NB-IoT. We describe a measurement campaign performed in a system of long underground tunnels, and we analyse linear regression models involving the engineered features. We show that the current empirical models for NB-IoT signal attenuation are inaccurate in a deep-indoor scenario. We observe that 1) indoor distance and penetration depth do not explain the signal attenuation well and increase the error of the prediction by 2-12 dB using existing models, and 2) a promising feature of average distance to the nearest corridor is identified.