Abstract
Radio environment map (REM) is an efficient coverage analysis technique for cellular networks. REM concept consists of spatially interpolating geo-located measurements to build the coverage map using techniques originating from geo-statistics, namely Kriging. The power received by a user, also known as the reference signal received power (RSRP) in LTE system, is attenuated by a large scale variation, i.e., distance dependent median path- loss, and a small scale variation, i.e., spatially correlated log-normal shadowing. Both these variations are needed to be exploited in order to build up an accurate REM using Kriging. Generalized least square (GLS) method is used in this work to estimate the median path-loss. On the other hand, spatial correlations of the shadowing at different locations are captured by a tapered covariance function, proposed in \cite{Alam2018} for coverage mapping. The RSRP values might expose different characteristics in terms of mean, trend and variability due to different shadowing environments over the domain. This paper proposes clustering of the measured RSRPs based on the abrupt variation in the large scale behaviors, i.e., median RSRP and path-loss trend. These variations suggest the existence of drastically different environments in the measurement area. We divide the measurements into clusters depending on homogeneous behaviors and perform the interpolation separately. Computation time and prediction accuracy are then evaluated for the whole area, and a significant performance enhancement is achieved due to clustering when compared with its non-clustering counterpart.
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