A quarter of the world population uses the smartphones to access the Internet and various types of multimedia services on the cellular networks, leading to the importance of focusing on user-centric approach, based on the Quality of Experience (QoE) metric, to measure the business success for the Mobile Network Operator (MNO). Although, the quality of the network can be improved with the help of Quality of Service (QoS), but it does not indicate the user satisfaction. Therefore, it is of vital importance to use the QoE along with the QoS parameters to evaluate and improve the quality of the network. In this paper, we propose the QoE modelling, based on the QoS parameters, by using the Artificial Neural Network (ANN) method to evaluate and synthesize the QoE in the actual environment, with the help of Drive Tests. The relationship between the QoS parameters and Opinion Score (OS) has been analyzed and investigated, prior to the selection of QoS parameters for the creation of QoE model and the process of parameter synthesis. The datasets of QoS parameters and OS have been collected with the help of end devices and the subjective evaluation method from the group of defined users, for each of the following multimedia services, i.e., YouTube, Facebook, Line, and the Web browser, in the real environment. The human behavior can be efficiently learned by using the properties of ANN from the collected datasets, to generate the QoE model along with the estimation of QoE score, instead of using the real humans. The results obtained from the process of parameter synthesis have been used as the main guiding paradigm for improving the performance of the networks in terms of user-centric approach based on the QoS parameters.

中文翻译：

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基于 QoS 测量的 QoE 估计和综合用于使用 ANN 方法改善蜂窝网络上的多媒体服务

全球四分之一的人口使用智能手机访问互联网和蜂窝网络上的各种类型的多媒体服务，因此基于体验质量 (QoE) 指标关注以用户为中心的方法来衡量移动网络运营商 (MNO) 的业务成功。虽然可以借助服务质量（QoS）来提高网络质量，但这并不代表用户的满意度。因此，使用 QoE 和 QoS 参数来评估和改善网络质量至关重要。在本文中，我们提出了基于 QoS 参数的 QoE 建模，通过使用人工神经网络 (ANN) 方法在路测的帮助下评估和综合实际环境中的 QoE。在选择用于创建 QoE 模型和参数合成过程的 QoS 参数之前，已经分析和研究了 QoS 参数与意见得分 (OS) 之间的关系。QoS 参数和操作系统的数据集是在终端设备和定义用户组的主观评估方法的帮助下收集的，用于以下每个多媒体服务，即 YouTube、Facebook、Line 和 Web 浏览器，在真实的环境。通过使用来自收集到的数据集的 ANN 的特性，可以有效地学习人类行为，以生成 QoE 模型以及 QoE 分数的估计，而不是使用真实的人类。