Spatial Prediction Under Location Uncertainty In Cellular Networks

TL;DR

This paper enhances coverage prediction in cellular networks by incorporating location uncertainty into a low complexity geostatistical algorithm, validated with real and simulated data, improving accuracy without high computational costs.

Contribution

It extends the Fixed Rank Kriging algorithm to account for geo-location errors, improving prediction accuracy in coverage modeling.

Findings

Inclusion of location uncertainty improves prediction accuracy.

The extended algorithm maintains low computational complexity.

Validated with both simulated and real measurements.

Abstract

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.