Spatial Interpolation based Cellular Coverage Prediction with Crowdsourced Measurements

Coverage extension and prediction has always been of great importance for mobile network operators. For coverage extension, the empirical and analytical path loss models assist in better positioning of the infrastructure. However post-deployment coverage prediction can be more cost effectively enabled by crowdsourced measurements. Unlike drive testing, crowdsourced measurements along with spatial interpolation techniques can help generate coverage maps with less expense and labor. Using controlled measurements taken with commodity smartphones, we empirically study the accuracy of a wide range of spatial interpolation techniques, including various forms of Kriging, in different scenarios that capture the unique characteristics of crowdsourced measurements (inaccurate locations, sparse and non-uniform measurements, etc.). Our results indicate that Ordinary Kriging is a fairly robust technique overall, across all scenarios.