Understanding Sub-GHz Signal Behavior in Deep-Indoor Scenarios

Critical Internet-of-Things (IoT) services require seamless connectivity, which is not always simple to provide and particularly in deep-indoor scenarios, it can be even impossible in some cases. The existing outdoor-to-indoor path-loss models lack accuracy in underground situations, thus IoT coverage planning in such areas cannot rely on robust tools and becomes a process of trial and error. In this work, we derive and analyze various environmental features that can be useful in understanding sub-GHz deep-indoor signal propagation. Based on a large-scale field trial in an underground tunnel system, we formulate several parameters related to the TX-RX distance and tunnel geometry. Through feature relevance studies in linear (ordinary least-squares (OLS) regression) and nonlinear (the Gaussian process regression) realms, we show that 2-D indoor distance and the distances to the tunnel walls may be useful in sub-GHz signal strength prediction in deep-indoor situations. We construct a linear and a Gaussian process model for the indoor path-loss prediction that outperforms the 3rd Generation Partnership Project (3GPP) model by 1.8 and 4.1 dB, respectively.