Improving predicted coverage accuracy in macrocells by use of measurement-based predictions

Measurement-based prediction (or MbP) is a unique radio propagation process, which increases the accuracy of conventional propagation model predictions by making use of measured data to improve the model predictions around sites. The process accounts for the first and second order statistics of the survey data by including the specific propagation features of terrain, buildings and trees revealed within the data, and encompasses the electromagnetic effects specified by the model. MbP extracts the main features of the slow fading characteristics of the propagation from the measurements and applies these to predict the shadowing across a wider area. The results are used to provide improved predictions for the surveyed site, regardless of which antenna configurations are subsequently used. All cells associated with the site can share the same survey data. This paper presents the results of an investigation into the performance of MbP compared to a conventional model prediction.