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. In this paper we introduce a spatial Bayesian prediction framework that can be used for both of these purposes. We show that using techniques from modern spatial statistics we can significantly increase the accuracy of coverage predictions from drive test data. Further, we carry out a detailed evaluation of our framework in urban and rural environments, using realistic coverage data obtained from an operator planning tool for an operational cellular network. Our results indicate that using spatial prediction techniques can more than double the likelihood of detecting coverage holes, while retaining a highly acceptable false alarm probability.