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Signal-to-interference-plus-noise ratio (SINR) evaluates the quality of the link between the base station (BS) and the user equipment (UE), taking into account the interference coming from neighboring cells and the thermal noise. Predicting the SINR with good accuracy ensures proper planning and optimization of radio coverage and good estimation of end-user throughput. In this paper, SINR is predicted at any user location based on UE geo-located measurements, using the Kriging technique and assuming that the received signals from serving and interfering cells undergo shadowing fading. Unlike previous works where only the prediction of the received powers is studied, the current functionality also considers the prediction error on the received signals. We consider both spatial correlation of shadowing signals for each cell and also inter-cell correlation and we propose a new model to generate multiple links shadowing signals for the downlink of cellular networks. For validation, we perform Monte-Carlo network simulations in which the shadowing samples are generated based on the proposed model. Results show a good prediction accuracy of the SINR at a new location resulting in a good estimation of the end-user perceived average data rate. [ABSTRACT FROM AUTHOR]

Signal-to-Interference-plus-Noise Ratio (SINR) measures the quality of the radio link between a base station and a User Equipment (UE). This quality indicator is important for the operator as it is used to measure and to optimize network coverage and the maximum user bitrate (which can be deduced from the SINR using the Shannon formula). In this paper, we study the SINR distribution for the downlink assuming that the received signals from serving and interfering cells undergo a path-loss and log-normal shadowing fading. We show that SINR can be approximated by a log-normal random variable and we give analytical expressions of characteristics parameters of its distribution. We then derive the average SINR expression. Also, we give a closed-expression of the rate probability density function and the outage probability and we obtain closed expressions of the average rate and lower and upper bounds of the average rate. For validation, we first compare theoretical results to Monte-Carlo network simulations and show that all the theoretical curves approximate well those obtained by simulations. Then using real measurements obtained by a measurement campaign, we validate the SINR distribution assumption. Based on a test of normality, we find that the recorded SINR samples are normally distributed in the logarithmic domain. We also show that the theoretical probability density function curves and the histogram of the measured samples of SINR are close. [ABSTRACT FROM AUTHOR]

Coverage optimization is an important process for the operator, as it is a crucial prerequisite toward 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 user’s equipments. In the 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 is extended to take into account that the location uncertainty proves to enhance the prediction accuracy while keeping a reasonable computational complexity. [ABSTRACT FROM PUBLISHER]