# 5G/B5G Service Classification Using Supervised Learning
## 3 5G/B5G Service System Classifier Proposed
### Building the Dataset  
The main limitation in this project was the achievement of a real dataset, a dataset with 5G operating systems parameters. We considered building a synthetic dataset (manually) analyzing KPI and KQI parameters extracted from ITU standards documents and various European projects and analysis documents carried out by telecommunications companies. The dataset built by taking the threshold values consulted in the bibliography and oscillating these values randomly, causing diverse values for each KPI/KQI and each service individually.  
The first block of the scheme shown in Figure 1 corresponds to our database, which we used to train the ML algorithm to validate and verify the predictive model. We created the database manually, using parameter values corresponding to the KPIs and KQIs of the selected services in Comma Separated Values (CSV) format. The documents consulted in this stage were related to standards and various project reports on 5G networks, such as 3 GPP [14; 15; 16; 17], the 5G Public-Private Partnership (5G-PPP) [2], NGMN [18; 19], Speed [20; 21], 5G America [4], International Telecommunications Union (ITU) [7; 22; 23], Huawei [24], and others [12; 25; 26; 27]. The selected parameter values were the standard threshold values, and these were manipulated randomly until we obtained values that were sufficiently close to their limiting values. Appendix A shows the tables with the threshold parameters for the extracted KPI/KQI parameters.  
The database contained 165 rows and 14 columns. The rows were the parameter values of the 5G services to be classified and, the firsts 13 columns contained the KPI and KQI values, while the last column corresponded to the labels of the 5G services. Table 1 is a fragment of the database, and we can visualize some values of KPI, KQI and services 5G. This project is a work in progress, the database is a process that can grow over time, the proposal is to test the combination of KPI+KQI, and as the database grows, we will test our hypothesis. The dataset is available, see the statement section for the link.  
In this project, we work on a classification problem where different labels are used (5G services). We need to attribute or assign a label to the elements to be classified to distinguish them. The review and attribution of labels can be done manually or computationally, using a specifically designed program. When applying ML, a set of labeled data, both the characteristics of the services and their labels are used to solve a classification problem we are talking about the SL scheme for the classification of 5G/B5G services. Similarly, it is necessary to label (represented by variable \(y\)) the 5G services found in the database; labels \(y\) corresponds to parameters or characteristics for the arriving services represented by the variable \(x\) (see Figure 1). The labeling of the data is realized before creating the algorithm's predictive model, so it is possible to know which label (\(y\)) corresponds to the parameters (\(x\)) of each 5G service in the database (see Table 1).