Abstract
The estimation of user experience in a wireless network has always been a research hotspot, especially for the realization of network automation. In order to solve the problem of user experience estimation in wireless networks, we propose a two-step optimization method for the selection of the kernel function and bandwidth in a naive Bayesian classifier based on kernel density estimation. This optimization method can effectively improve the accuracy of estimation. At present, research on user experience estimation in wireless networks does not include an in-depth analysis of the reasons for the decline of user experience. We established a scheme integrating user experience prediction and network fault diagnosis. Key performance indicator (KPI) data collected from an actual network were divided into five categories, which were used to estimate user experience. The results of these five estimates were counted through the voting mechanism, and the final estimation results could be obtained. At the same time, this voting mechanism can also feed back to us which KPIs lead to the reduction of user experience. In addition, this paper also puts forward the evaluation standard of the multi-service perception capability of cell-level wireless networks. We summarize the user experience estimation for three main services in a cell to obtain a cell-level user experience evaluation. The results showed that the proposed method can accurately estimate user experience and diagnosis abnormal values in a timely manner. This method can improve the efficiency of network management.
Highlights
The quality of service (QoS) is a performance indicator of every network, and it can be truly measured and used in network services’ improvement
Through the results shown in these three tables, it can be seen that compared with the other two common methods, naive Bayes based on kernel density estimation (KDE) with two-step optimization had better accuracy for the association between Key performance indicator (KPI) and key quality indicators (KQIs)
In mainstream research on KQI estimation for wireless networks, discretization of KPIs is always adopted, which will lead to some error
Summary
The QoS is a performance indicator of every network, and it can be truly measured and used in network services’ improvement. It is worth noting that for any wireless network, whether 3G, 4G, or 5G, the measurement indicators of network performance are basically the same. The QoS has always been the focus of research and the basis of network management. QoS parameters only reflect the network performance, which can not directly reflect the user satisfaction [2]. The specific indicators of the QoS are the KPIs, which are often the data collected from the base station in a cellular network. The indicator of the QoE is different from the QoS, which is composed of subjective and objective parts
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