Method for QOE monitoring and increasing in cellular networks based on QOE-to-QOS mapping using spline approximation

Abstract

The current level of development of the cellular services market, qualitative changes in the means and methods of providing services, and the increased volume and diversity of information circulating in cellular networks have evoked the need for service quality assessment system improvement. To maintain competitiveness, the main efforts of operators are aimed at improving quality and increasing the service life of subscribers in the network by ensuring the required level of customer satisfaction in high-quality services through a system of organizational–technical and socioeconomic measures to bring the achieved level of quality-of-service (QoS) provision in accordance with the existing, emerging, or projected needs of subscribers. To achieve improved QoS provision, a functional relationship between network parameters has been established: The impact of key performance indicators on a key quality indicator through the use of cubic Hermitian splines (CHS) has been determined. The use of splines, as a signal model, can significantly improve the quality of signal processing due to the continuity of values and partial derivatives in the joints of spline gluing. CHS are characterized by calculation simplicity, as they provide high-speed computing, which, in turn, is important for real-time work when processing large data sets. Experimentally, it was shown that the use of splines allows for the ability to calculate statistical estimates of the required parameters of spline approximations, but also their confidence intervals, which increases the accuracy and probability of further calculations and is a distinct advantage of this approach. Also, the methods of service quality management through machine learning have been improved, which are effective tools for modeling integrated indicators of communication services quality control, monitoring their condition in terms of final copies of services, determining the causes of degradation, and reporting. It monitors the service performance provided by the cellular operator. Finally, the method checks the readiness and availability of services, detects network nodes through which quality degradation occurs, collects various quality metrics, and compares them with pre-installed quality indicators.

Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment

“…Based on social cloud computing, there have been suggestions of models that use image measurements to optimize the QoS and QoE [25][26][27][28] of wireless networks. The limitation, however, is that these suggestions do not include practically implementable mappings for achieving the models [11]. Similarly, in [27], the focus was on improving video quality in social clouds.…”

“…Thus, PSO is used in our search for optimal KPIs for the 4G LTE network. As the development of the cellular service market continues to advance radically, spurring high levels of competition between MNOs, sophisticated methods that foster better service provision, better network coverage, and reductions in cell reselection, handover time, and requirements have been highly encouraged and adopted by MNOs [11]. Several works show the relevance of establishing different relationships between network parameters and the QoS provided via an optimization process [3,12].…”