# Framework for Behavioral Analysis of Mobile Networks
## 1 Introduction  
The incoming Fifth Generation (5G) [1] networks are envisioned to overcome the boundaries of current Long-Term Evolution (LTE) networks, i.e., higher data rates, lower latency, reliable communications, increased number of connected devices or user coverage. In order to achieve this, new architecture and radio access network are standardized by 3 rd Generation Partnership Project (3 GPP) [2, 3], enabling new services and scenarios in mobile communications [4]. Among these new services, the most noteworthy are: enhanced Mobile Broadband (eMBB) which is an improvement on current LTE data connections in terms of data rates and bandwidth; Ultra-Reliable Low Latency Communications (URLLC), which is intended for scenarios where reliability and latency are critical; and lastly, Massive Machine Type Communications (mMTC) which enables a scenario with a massive connection of devices, but with a lower volume of data exchange. The changes and improvements of this new 5G network mean an increase in the complexity of the network, and consequently in its management. In this context, Self-Organizing Networks (SON) [5] appear as a great alternative to automate the management of networks reducing the need of human intervention. SON functionalities include self-configuration, self-optimization, and self-healing methods to perform tasks related to network planning, performance improvement, and failure management, respectively. For the successful operation of these functions, certain key information gathered from different elements of the network, such as nodes or users, is required, such as network configuration parameters, network performance indicators, or context information. The amount of information available in networks is usually proportional to their complexity, so the high complexity expected in 5G networks with new scenarios and services will mean a large increase in the amount of data collected. However, a larger number of data does not always imply higher efficiency in SON tasks, as not all collected information turns out to be useful for the network management. For thisreason, extracting as much useful information as possible from all available data is one of the challenges to be addressed to achieve an efficient performance of SONs. To cope with this issue, Machine Learning (ML) algorithms [6] have become one of the most common alternatives. These techniques aim to learn from a set of data, extracting the useful information. A typical classification of ML methods divides them into supervised algorithms, which require labeled data (i.e., the dataset includes information about the state of the network at the time the data was collected) and unsupervised algorithms, which are able to learn from an unlabeled dataset [7]. These techniques have already been widely applied in mobile networks, so their application to 5G networks is expected to be essential for the automation of management tasks. Among these types of ML techniques, unsupervised methods present as an advantage that new network states may be found for dynamic scenarios that adapt to the new services of this new technology. Supervised methods usually achieve better results than unsupervised ones, but data in mobile networks are unlabeled in most cases. In this paper, a framework for extracting useful information from a mobile network based on ML is proposed. Specifically, the system detects, classifies and monitors cell behavior patterns from an unlabeled dataset. For this purpose, the Self-Organizing Maps (SOM) [8] algorithm was firstly employed as an unsupervised algorithm, which clusters cells with similar behavior in the same group. Then, a supervised algorithm, namely Random Forest [9], is applied, to perform cell behavior classifications and monitoring in real time. As described in the next section, there are works in the literature that propose methods for cell classification or information extraction applied to mobile networks. However, most of them present important limitations.