# Framework for Behavioral Analysis of Mobile Networks
## 2 State of the Art  
The concept of SON, standardized by 3 GPP from the definition of LTE onwards [10], has been widely researched in the area of mobile networks. The last releases of the standard have included SON functionalities addressing the potential applications of SON tasks in the new 5G networks [11]. The emerging scenarios enabled by 5G networks are diverse, ranging from emergency situations and telemedicine [12] to massive connection of IoT devices [13]. In this context, the application of SON functionalities to 5G networks is a key challenge to automate optimization and management tasks [14]. In the literature, many works propose methods to implement these SON functions. Most of them are based on ML algorithms, although automatic methods such as fuzzy or rule-based systems are also proposed. In [15], ML alternatives for management tasks in mobile networks are analyzed in detail. In particular, the authors study alternatives for automating tasks such as anomaly detection, diagnosis and compensation, combining them in a system for fault management in mobile networks. The main contributions to the different SON tasks found in the literature are discussed below. One of the main challenges in mobile networks is to achieve an efficient use of radio resources due to the limitation of availability. Hence, self-optimization is one of the most requested functionalities of SON, frequently mentioned in the literature. An approach for the optimization and management of radio resources in mobile networks is proposed in [16]. Based on network level, user level or user requirement, the best resource allocation in LTE and 5G networks is recommended. The authors of [17] develop an ML-based framework for optimizing network parameters to improve resource allocation and Quality of Experience (QoE). In [18], a system for optimizing handovers based on the SOM and K-Means is suggested. Parameters such as Time to Trigger (TTT) or handover hysteresis are modified to avoid unnecessary handovers between indoor and outdoor cells. In the context of self-healing, anomaly detection is one of the most common tasks in mobile networks, enabling automatic detection of network failures. The main objective of anomaly detection methods in mobile networks is to identify anomalous states or faults in the cells. Some approaches are proposed in [19; 20], which perform an SOM-based algorithm for the detection of faulty cells in mobile networks. The authors of [21] provide an anomaly detection system based on the Merge Growing Neural Gas (MGNG) algorithm. This algorithm uses data of Performance Indicators (PI) from the cells. Some works are focused on specific faults such as the system described in [22] for detecting sleeping cells and sudden increase in traffic. The authors implement a semi-supervised system that exploits statistical values to perform the detection. An automatic algorithm that aims to detect cell outages based on handover statistics is defined in [23]. Anomaly detection focuses on finding operational faults in the network, whereas diagnosis (also called Root Cause Analysis) tries to find the cause of the fault. Many works for automating the diagnosis task can also be found in the literature. A tool for diagnosis based on techniques such as tree classification and correlation is developed in [24]. In contrast to other systems, data are collected on a per-UE basis. It diagnoses the most common radio causes in an LTE network based on user throughput metrics. An automatic diagnosis system is implemented in [25] based on unsupervised ML techniques (SOM and Ward's Hierarchical). In addition, expert knowledge is included to get more accurate results. The authors of [26] develop a framework to automate the diagnosis of radio conditions in the cells of a mobile network. The system is implemented with an SOM algorithm and uses user traces as inputs. Lastly, prediction enables the anticipation of possible failures that may occur in the network.