Coverage planning and optimization is one of the most crucial tasks for a radio network operator. Efficient coverage optimization requires accurate coverage estimation. This estimation relies on geo-located field measurements that are gathered today during highly expensive drive tests (DT) and will be reported in the near future by users’ mobile devices thanks to the Third-Generation Partnership Project (3GPP) minimization of drive tests (MDT) feature. This feature consists of an automatic reporting of the radio measurements associated with the geographic location of the user's mobile device. Such a solution is still costly in terms of battery consumption and signaling overhead. Therefore, predicting the coverage on a location where no measurements are available remains a key and challenging task. This paper describes a powerful tool that gives an accurate coverage prediction on the whole area of interest: It builds a coverage map by spatially interpolating geo-located measurements using the Kriging technique. This paper focuses on the reduction of the computational complexity of the Kriging algorithm by applying fixed rank Kriging (FRK). The performance evaluation of the FRK algorithm both on simulated measurements and real field measurements shows a good tradeoff between prediction efficiency and computational complexity. In order to go a step further toward the operational application of the proposed algorithm, a multicellular use case is studied. Simulation results show good performance in terms of coverage prediction and detection of the best serving cell.
['Hajer Braham', 'Sana Ben Jemaa', 'Gersende Fort', 'Eric Moulines', 'Berna Sayrac']
Fixed Rank Kriging for Cellular Coverage Analysis