Improving Coverage Estimation for Cellular Networks With Spatial Bayesian Prediction Based on Measurements
Cellular operators routinely use sophisticated planning tools to estimate the coverage of the network based on building and terrain data combined with detailed propagation modeling. Nevertheless, coverage holes still emerge due to equipment failures, or unforeseen changes in the propagation environment. For detecting these coverage holes, drive tests are typically used. Since carrying out drive tests is expensive and time consuming, there is significant interest in both improving the quality of the coverage estimates obtained from a limited number of drive test measurements, as well as enabling the incorporation of measurements from mobile terminals.