Coverage prediction is one of the most important aspects of cellular network optimization for a mobile operator . Spatial statistics can be used for coverage prediction. This approach is based on the collected geo-located measurements performed usually by drive test campaigns. Notice that uncertainty in reporting the location can result in inaccurate coverage prediction . In urban environments the location error can reach important levels up to 30 m using the Global Positioning System (GPS) . In this paper, we propose to consider the location uncertainty in the spatial prediction technique. We focus also on the complexity problem. We therefore propose to use the Fixed Rank Kriging (FRK) as spatial prediction technique . We validate the model using a field-like dataset obtained from a sophisticated simulation/planning tool. With the location uncertainty , the FRK proves to reduce the computational complexity of the spatial interpolation while keeping an acceptable prediction error .
Coverage optimization is an important process for the operator as it is a crucial prerequisite towards offering a satisfactory quality of service to the end-users. The first step of this process is coverage prediction, which can be performed by interpolating geo-located measurements reported to the network by mobile users' equipments. In previous works, we proposed a low complexity coverage prediction algorithm based on the adaptation of the Geo-statistics Fixed Rank Kriging (FRK) algorithm. We supposed that the geo-location information reported with the radio measurements was perfect, which is not the case in reality. In this paper, we study the impact of location uncertainty on the coverage prediction accuracy and we extend the previously proposed algorithm to include geo-location error in the prediction model . We validate the proposed algorithm using both simulated and real field measurements. The FRK extended to take into account the location uncertainty proves to enhance the prediction accuracy while keeping a reasonable computational complexity .
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prediction is one of the most important aspects of cellular network optimization for a mobile operator . Spatial statistics can be used for coverage prediction. This approach is based on the collected
optimization is an important process for the operator as it is a crucial prerequisite towards offering a satisfactory quality of service to the end-users. The first step of this process is coverage prediction, which can be performed by interpolating
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performed usually by drive test campaigns. Notice that uncertainty in reporting the location can result in inaccurate coverage prediction . In urban environments the location error can reach important levels up to 30 m using the Global Positioning System (GPS) .
reported to the network by mobile users' equipments. In previous works, we proposed a low complexity coverage prediction algorithm based on the adaptation of the Geo-statistics Fixed Rank Kriging (FRK) algorithm. We supposed that the geo-location information reported with the radio measurements was perfect, which is not the case in reality.
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propose to consider the location uncertainty in the spatial prediction technique. We focus also on the complexity problem. We therefore propose to use the Fixed Rank Kriging (FRK) as spatial prediction technique
study the impact of location uncertainty on the coverage prediction accuracy and we extend the previously proposed algorithm to include geo-location error in the prediction model
R
model using a field-like dataset obtained from a sophisticated simulation/planning tool. With
proposed algorithm using both simulated and real field measurements. The FRK extended to take into account
R
, the FRK proves to reduce the computational complexity of the spatial interpolation while keeping an acceptable prediction error
proves to enhance the prediction accuracy while keeping a reasonable computational complexity