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.

当前蜂窝服务市场的发展水平、提供服务的手段和方法发生质的变化，以及蜂窝网络中流通的信息量和多样性的增加，都引发了对服务质量评估体系改进的需求。为了保持竞争力，运营商的主要努力旨在通过组织技术和社会经济措施系统确保客户对高质量服务的满意程度，从而提高网络中用户的质量和延长服务寿命根据用户现有的、新兴的或预计的需求，达到服务质量 (QoS) 水平。为了实现改进的 QoS 提供，已经建立了网络参数之间的函数关系：通过使用三次 Hermitian 样条 (CHS) 确定了关键性能指标对关键质量指标的影响。样条作为信号模型的使用，由于样条胶合接头处数值和偏导数的连续性，可以显着提高信号处理的质量。CHS 的特点是计算简单，因为它们提供了高速计算，这反过来又对处理大型数据集时的实时工作很重要。实验表明，样条曲线的使用允许计算样条曲线逼近所需参数的统计估计值，以及它们的置信区间，这增加了进一步计算的准确性和概率，并且是这种方法的一个明显优势。还，改进了通过机器学习进行服务质量管理的方法，这是对通信服务质量控制综合指标进行建模、根据服务的最终副本监控其状况、确定退化原因和报告的有效工具。它监控蜂窝运营商提供的服务性能。最后，该方法检查服务的就绪性和可用性，检测发生质量下降的网络节点，收集各种质量指标，并将其与预先安装的质量指标进行比较。和报告。它监控蜂窝运营商提供的服务性能。最后，该方法检查服务的就绪性和可用性，检测发生质量下降的网络节点，收集各种质量指标，并将其与预先安装的质量指标进行比较。和报告。它监控蜂窝运营商提供的服务性能。最后，该方法检查服务的就绪性和可用性，检测发生质量下降的网络节点，收集各种质量指标，并将其与预先安装的质量指标进行比较。