# 5G/B5G Service Classification Using Supervised Learning
## 1 Introduction  
The complexity, flexibility, and dynamism of 5G/B5G networks means that they need to be managed automatically [1]. Variations in behaviour patterns limit the identification of network activity. Furthermore, the traditional management model is insufficient, and due to the correlations between multiple variables and the extensive datasets handled in a single analysis, computational assistance is required [1]. Artificial Intelligence (AI) can be used to support the cognitive management of 5G/B5G [2], and ML is one of the most promising tools in this area. 5G is a network that focuses on services, and the classification of these services therefore requires an efficient scheme for network resource allocation. In [3], a variety of new services for 5G are described, some of which have very similar performance and quality requirements. The growth and heterogeneity of the different services implemented in 5G networks have undergone fast development due to their particular characteristics and specifications. If services are classified without the help of ML, it is difficult to monitor and control network resources and to predict and avoid SLA violations, which can affect both the QoS performance and the QoE perceived by users, that is, the management of the network as a whole. In the field of telecommunications, and, particularly in the deployment of 5G/B5G networks, the correct classification of services offers a way of providing a better QoS of the network and of optimizing the QoE [3], and is therefore important. The User Equipment (UE) requests services that require precise categorization, in order to allow network operators to select specific network slices for each service, to improve the QoS of the network and the QoE perceived by the users, and to define the SLAs for each network slice [3,4]. The critical requirements for 5G can be considered from two points of view: from the user's perspective, and from a network performance perspective [5]. Current, 5G service classification systems use ML, and consider KPIs as the main factor when performing the classification. However, it is necessary to consider KQIs in order to achieve the best possible classification, since these allow us to consider the performance of the network and the user's requests for a service, and our proposed scheme therefore takes these into account. The main objective of this work was to prove the hypothesis that when considering the quality parameters (KQI) in addition to the traditional performance parameters (KPI), there would be a better identification (through classification) of the services. Consequently, providers can optimally allocate resources with proper QoS management. The inclusion of KQIs, which reflect the performance and quality of End To End (E2E) services, makes it possible to achieve a better customer experience in practice [5,6]. Incorporating the KQIs reflects the customer's experience in terms of indicators that include their requirements, and can therefore provide them with better network performance and better QoE. To improve the management of 5G networks in general, and the QoS and QoE in particular, we present a 5G/B5G service classifier system based on Supervised Machine Learning (SML) techniques. This system is based on the KPIs and KQIs of the different services to offer a better classification. One contribution of our scheme is the feedback made once a new service is classified, introducing its KPI/KQI parameters in the database to retrain dynamically and consequently regenerate a new predictive model. To make it more robust (from the structural point of view) concerning the previously generated predictive model. The rest of this paper is organized as follows. Section 2 gives an overview of previous works on 5G services, and discusses some characteristics of the KPIs and KQIs involved in service classification. Section 3 describes our 5G/B5G service classifier system and the creation of our database.