Coverage planning and optimization is one of the most crucial tasks for a radio network operator. Efficient coverage optimization requires accurate coverage estimation which relies on geo-located field measurements . These measurements are gathered today during highly expensive drive tests  and will be reported in the near future by users equipments thanks to the 3GPP MDT feature ( still costly in terms of battery consumption and signaling overhead ). In both cases , 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 , i.e. a coverage map , by spatially interpolating geo-located measurements using  Kriging technique. The 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 trade-off between prediction efficiency and computational complexity. In order to go a step further towards  operational application of the proposed algorithm, a scenario with multiple cells is studied. Simulation results show a good performance in terms of coverage prediction and detection of  best serving cell.
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 which are gathered today during highly expensive drive tests (DT); and will be reported in the near future by users ' mobile devices thanks to the 3GPP Minimizing Drive Tests (MDT) feature~\mbox{%DIFAUXCMD 3GPPproposal 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. The 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 trade-off between prediction efficiency and computational complexity. In order to go a step further towards the operational application of the proposed algorithm, a multicellular use-case is studied. Simulation results show a good performance in terms of coverage prediction and detection of the best serving cell.
R
which
. This estimation
R
. These measurements
which
A
(DT);
R
equipments
' mobile devices
R
MDT feature (
Minimizing Drive Tests (MDT) feature~\mbox{%DIFAUXCMD 3GPPproposal
R
). In both cases
. Therefore
R
, i.e.
: it builds
D
,
A
the
R
kriging
Kriging
A
the
R
scenario with multiple cells
multicellular use-case
A
the