Radio Environment Maps through Spatial Interpolation: A Web-based Approach

The 5G era has seen the largest number of studies around Radio Environment Maps (REM) than in the previous three generations combined. Visualization of network coverage on interactive maps provides contextual information and numerous benefits to operators, regulators and to the public. In this context, spatial interpolation and extrapolation techniques are used to add synthetic data points between measurements to fill gaps in the data, where techniques such as machine learning, Ordinary Kriging (OK) and Inverse Distance Weighted (IDW) are used to enhance the quality of REMs. In this paper we present a state-of-the-art software package, which integrates a series of interpolation methods, augmented with polygon intersection queries functionality to control data used for estimation of coverage on the roads. The proposed web-based application is powered by a set of modular Python packages, making it future-proof and real-world ready, enabling efficient and precise network management.