Data-driven and GIS-based Coverage Estimation in a Heterogeneous Propagation Environment

We provide a data-driven coverage estimation technique that employs machine-learning based regression ideas for exploiting commonality of antenna-gain and other parameters across measurements made in multiple propagation environments. We then show how readily available geographic information system (GIS) data could be exploited for quick classification of geographic areas into various propagation environments, and how this could enable quick and automated estimation of coverage for faster and more efficient deployment of Internet of Things