Radio environment maps (REMs) is an efficient coverage analysis solution for present-day cellular networks. REM concept consists of spatially interpolating geo-located measurements to build the coverage map using techniques originating from geo-statistics. Kriging is a powerful technique which results in high performance in terms of prediction accuracy. However, the computational complexity of Kriging precludes its straightforward implementation when dealing with large datasets. Fixed rank Kriging (FRK) has been implemented in a previous work [1] to overcome this issue. However, it tends to neglect the small scale structured variations of the data, which may result in a loss of accuracy. In this paper, we propose covariance tapering (CT) that consists in multiplying the covariance function by a compactly supported covariance function resulting in a sparse covariance matrix. This allows to reproduce well small scale variations while neglecting larger scale variations. We compare the performance of CT with FRK using both simulated and real datasets. The results show that CT performs better than FRK both in terms of computational complexity and prediction accuracy. However, the prediction accuracy of CT deteriorates when the dataset is sparse with longer range of dependencies compared to the tapering range.


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    Title :

    Performance Evaluation of Covariance Tapering for Coverage Mapping


    Contributors:


    Publication date :

    2018-06-01


    Size :

    624054 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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