# Framework for Behavioral Analysis of Mobile Networks
## 2 State of the Art
Thus, this task mitigates failures beforehand, although it is difficult to automate and achieve accurate results. The authors of [27] review the most common ML techniques and their possible application to automatic prediction. In [28], an ML-based approach to prediction is presented. The aim is to predict uplink transmission power levels using quality indicators and application level information. The authors of [29] propose a systemfor forecasting received signal levels. The approach is based on the supervised Random Forest algorithm. As the complexity of the network increases, the number of data generated and collected increases significantly. A key factor in reaching suitable effectiveness in SON tasks consists in extracting useful information from a huge number of data. Characterization of the network through behavioral patterns is one of the most common methods of obtaining such useful information. In the area of mobile networks, several alternatives based on unsupervised ML algorithms are proposed, due to the fact that data collected in these networks are usually unlabeled. The authors of [30] base their proposal on the SOM algorithm to describe the global behavior of 3G networks. The SOM algorithm is also the basis of the framework proposed in [31], which also includes K-Means to fit the patterns detected by SOM. This combination is also implemented by the authors of [32] to classify behavior patterns of the radio access network of a mobile network. Most of the studied works provide a global overview of the network behavior. However, there are other works that are focused on specific aspects of the network. An SOM-based approach for traffic characterization of a network is proposed in [33]. In [34], cells are classified according to network traffic using the Naive Bayes technique (which applies [PERSON] theorem). The proposed system is a radio technology independent of the network, covering GSM to the New Radio of 5G. A method for extracting different patterns of application traffic is described in [35], which is based on Random Forest. The authors of [36] also use Random Forest to detect traffic patterns at the application level, including the importance of attributes as inputs for the classification. A brief summary of each state-of-the-art work is presented in Table 1, pointing out the algorithms used and the purpose. As described before and as Table 1 shows, most works that propose SON functions are based on automatic methods or ML techniques and even combine two or more of these techniques in the same system, such as [31; 32]. In these two works, SOM and K-Means are included in the same approach to detect patterns of behavior. As Table 1 reflects, some works employs the same algorithms for different objectives. This is the case of SOM, which is used with different aims such as anomaly detection or cell pattern detection. Random Forest is also used with different purposes such as classification and prediction in different works. However, combining the advantages of supervised and unsupervised algorithms in the same system is not sufficiently explored in the literature, since there are only a few works that address this approach. The authors of [35] propose the use of supervised and unsupervised algorithms in the same system for a mobile network scenario for the analysis of a specific type of traffic data (i.e., traffic of apps). In this respect, our work provides the ability to combine the advantages of unsupervised and supervised algorithms for the analysis and classification of the performance of a whole network without focusing on a specific aspect (e.g., type of traffic, context information) of the network. In addition, although most of the last works included in Table 1 are focused on the extraction of useful information to improve SON tasks, they do not propose any system for the monitoring in real time of the cell's behavior in order to detect possible changes.