# 5G/B5G Service Classification Using Supervised Learning
## 2 Related Work  
The authors of [7] identified the different services that are implemented in 5G networks and services related to a generic use case, such as: Enhanced Mobile Broadband (eMBB), Ultra-reliable and Low Latency Communication (urLLC), and Massive Machine Type Communication (mMTC). Several services are shown in the middle of the triangle (see page 12 (Figure 2) in ref [7]), meaning that they may have characteristics of several use cases and different requirements in terms of KPIs and KQIs; for example, Augmented Reality (AR) is appearing between eMBB and urLLC. However, other services belong to specific use cases; for instance, smart cities are related to mMTC. The relevance of the specific essential requirements may vary significantly, depending on the use case or scenario in which a service is deployed. ITU in [7] (see page 15, Figure 4) shows the importance of some critical requirements with reference to the KPIs/KQIs for three generic use cases, based on a scale with three levels: low, medium and high. For example, for services associated with the urLLC use case, low latency is the most critical requirement, while the peak data rates and network energy efficiency are not key parameters. The KPI and KQI parameters affect the network performance so strongly that we consider it essential to incorporate them both, in an interrelated way, and this is the idea that underpins this work. Several research groups have implemented smart solutions to address the need for mobile network services, to standardize certain methods, and, to improve network performance, for example through the alliance of Third Generation Partnership Project (3 GPP), Next Generation Mobile Networks (NGMN), European Telecommunications Standards Institute (ETSI), and many other research initiatives [8; 9]. The authors of [9] highlighted the possibility of using SL and unsupervised learning algorithms to classify new services within use cases (eMBB, mMTC, and urLLC). They proposed the use of basic requirements or KPIs such as latency, bandwidth and data rate. In [10], the author demonstrated the possibility of classifying the services demanded by users using technologies such as Software Defined Networks (SDN), Network Function Virtualization (NFV), and ML. The objective of this work was to predict demand and dynamically allocate network resources [10]. The parameters were classified based on the bandwidth, latency, jitter and other KPIs. In [4], a system was established and a database was created in which SLAs were defined for each network slice. The authors used unsupervised learning techniques to classify 5G services based on the primary system. Although little information was provided on the elements considered in this classification, these are expected to have been related to the KPI requirements. The Network Machine Learning Research Group (NMLRG) has worked on novel methods of classifying 5G services using ML [11]. Some of the papers published by the NMLRG [11] present results obtained from the application of their models to the classification of 5G services. They focused on KPIs related to network traffic as the main factors when performing system classification. The authors used some SL techniques and compare it is, and they concluded that Decision Tree and Random Forest are best in this kind of problem. KQIs represent a shift from traditional network-based performance parameters (KPIs) to, a subjective quality-based perceived by the end-user, known as QoE. After they were defined, however, KQIs were not promoted or applied for some time [12]. The different variants used in the works described above were developed based on data collection and analysis to give a better classification of 5G services. These studies focused on KPI requirements, and none of them took into account the KQI parameters as elements for the classification of 5G services.