Spatial Prediction Under Location Uncertainty in Cellular Networks

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.

On Geostatistical Methods for Radio Environment Maps Generation under Location Uncertainty

“…The Gaussian nature of the wireless channel shadowing makes the ML method better suited for the estimation problems. However, as we will see later, the location uncertainty affects the Gaussianity of the model [41], [69], [72]. Also, although the ML method has superior performance, the estimation can be computationally challenging for large data sets, as each likelihood evaluation requires a Cholesky factorization of covariance matrix or equivalent operations, which is O(N 3 ) [72].…”

“…Moreover, modern wireless communication systems require accurate and robust spatial predictions against adversities. On the latter, a problem that has recently been investigated consists in the study of the impacts of the location uncertainty on the performance of the spatial predictions [41][42][43][44].…”

“…In a wireless communication system, the location information depends mainly on the measurements sent by the network devices as well as the network-based location estimation methods, which gives an error between 50 and 300 meters, and the global positioning system (GPS), with an error between 5 and 30 meters [8], [41]. Despite the evolution of positioning techniques, due to channel adversities leading to imperfect location estimation as well as inaccuracy of measurements performed by network devices, it is reasonable to assume that the location information involved in the wireless communication system is not entirely accurate.…”

Forecast scheduling and its extensions to account for random events

“…We briefly present the KKT optimality conditions for the forcast scheduling problem (2). It is known to provide necessary and sufficient optimality conditions.…”

“…[2] for example utilizes coverage prediction algorithms based on geo-statistics Fixed Rank Kriging algorithm which is adapted to handle geo-location errors. The REM can be created and updated in a MDT server in the management plane and be downloaded into each Base Station (BS).…”