Quality of service management method in a heterogeneous wireless network using Big Data technology and mobile QoE application

Today, with the increasing number of business users and their changing Quality of Service (QoS) requirements, there is a problem for mobile network operators to provide individual acceptable Quality of Experience (QoE) to their customers due to the lack of flexibility, agility, and scalability of traditional networks. For this reason, the latest scientific works are increasingly focusing on resource management, considering users' QoE, replacing the traditional system-oriented approach based on QoS. However, this problem has not been fully solved so far, as there are no approaches that take into account the user's intentions regarding the individualization of the specific types of services at a particular time for heterogeneous mobile networks. Thus, the development of means of feedback between the user and the operator on the adaptation of the QoE level or their intentions under changing user requirements is one of the important and unresolved tasks for future networks at the moment. A conceptual model for the construction of an Intent-Based Software-Defined Heterogeneous Network (IBSDHN) is proposed. This model ensures efficient management of shared network resources, adapting to business users' changing QoS requirements. The prototype of a mobile and operator application for adaptive clientoriented service delivery in a heterogeneous wireless network is developed, which allows obtaining the ordered QoE based on the feedback between the user and the operator. A method for QoS management in a heterogeneous wireless network using Big Data technology and a unique mobile (QoE) application is proposed. In contrast to the known, this method analyzes ordered QoE scores of service from users and searches for the optimal radio access network that meets these requirements based on the predicted QoE values. The experimental study proved that using the proposed method in a heterogeneous wireless network would reduce the number of dissatisfied users with the QoS up to 60%.