Determination of the Relationship between Cell Network Parameters with a Cubic Hermite Spline

One of the most urgent tasks during the organization of cellular networks is the task of improving the quality of service provision by the cellular operator. The paper presents a method for determining the relationship between the cellular network parameters KPI and KQI by using cubic Hermite spline. First of all, the theoretical principle of calculating and constructing a cubic Hermite spline is given in satisfaction of the conditions of the least squares’ method. The use of splines, as a signal model, can significantly improve the quality of signal processing due to the continuity of values and part of the derivatives in the nodes of spline gluing. Cubic Hermite splines, in turn, differ in their simplicity of calculations from the global one, which ensures high performance in computing, which is important for real-time operation when processing large amounts of data. On the basis of this, to evaluate the use of this spline an experimental study was conducted to establish the functional dependence between KPI and KQI network parameters. Namely, within the framework of the work, the Internet access service has been tested on the basis of the use of 3G cellular technology. The experiment consists of 50 experiments. Data was obtained under constant external conditions to ensure the purity of the experiment. On the basis of the measured data for each parameter, the ordinates vector of the spline bonding nodes was calculated and an approximation of the parameters was made using Cubic Hermite splines. The next step is to establish the influence degree of each KPI parameter on the single KQI parameter. The analytical connection coefficients were found taking into account that the mean square error of the KQI parameter prediction from the KPI parameters would be minimal. As a result, on the above data basis, the statistical alignment of the time series of the KQI parameter was performed, that is, it was leveled the KQI physics process using already found KPI approximated parameters. The use of splines allows us to find not only statistical estimates of the desired parameters of the spline approximations, but also their confidence intervals, which increases the accuracy and reliability of the results and it is undoubtedly the advantage of the chosen approach. In our case, this allows you to get a KQI forecast from KPI. In the future, research under such conditions will be followed by the implementation of a specific KPI parameter choice in order to its improvement to improve the quality of service provided to users of the cellular communication operator. Ref. 11, fig. 10.