Coverage mapping using spatial interpolation with field measurements

Abstract

Coverage optimization is a crucial task for a radio network operator. An accurate coverage estimation is a key prerequisite for efficient coverage analysis and optimization. In this paper, we propose a coverage prediction method based on statistical modeling of the wireless environment. We build a Radio Environment Map by interpolating geo-located measurements using the Kriging spatial prediction technique. Moreover, as we perform Kriging on massive observation datasets obtained through field measurement campaigns, we use Fixed Rank Kriging, to reduce the complexity of the Kriging algorithm. We apply the FRK algorithm for Long Term Evolution (LTE) network coverage prediction. We consider as observation data, the coverage measurements obtained by operational drive tests in a rural area. Numerical results show that by using the FRK algorithm, we fulfill a good trade-off between computational complexity and prediction accuracy.