IMPROVING THE QUALITY CHARACTERISTICS OF THE 5G / NR NETWORK USING THE DECOMPOSITION METHOD

Abstract

The paper examines a 5G mobile network based on the NR (New Radio) technology.The 5G network allows you to significantly expand the range of high-speed services, including Internet of Things services. Recommendation 3GPP TR 38.913 defines a group of 5G network services, which include technologies with defined packet latency. The development of these technologies requires the operator to ensure the required values of the QoE characteristics. 5G/NR is based on Software Defined Radio (SDR) and Software Defined Networking (SDN) backbones. To maintain the QoE characteristics for various groups of services in a 5G / NR network, a decomposition method is used. This method allows to create a topological network configuration for various conditions of providing services. In this case, an important issue in the implementation of the decomposition method is the ability to determine the QoE characteristics. The solution of this problem is complex due to the fact that while being dynamic, the network has a sophisticated architecture. Moreover, in the process of dividing one network into several subnets, there is a possibility for some physical resources to be simultaneously involved in several subnets. The purpose of this work is to study the average packet delay time for a network by means of the tensor contour method based on the decomposition. This method allows to simultaneously explore both the structural and functional characteristics of a network. We propose to perform decomposition of the 5G/NR network through dividing it into subnets. We have evaluated the following characteristics for the mentioned network: the average value of packet delay time in the loops of each subnet, the average value of packet delay time in the border branches of each subnet, the average value of packet delay time in the border branches for the whole network. These results have the practical significance for design and further operation of an e-health network. Precisely, they are applicable to select a network topology and to estimate characteristics of the necessary equipment.