## 1 Introduction
### Self-Healing Networks
#### 1.5.1 Fault Detection  
This section is responsible for identifying faulty cells. Self-healing mechanisms continue to function only on these cells. Identifying defective cells is the most superficial aspect of the work, which may be assessed using alarms and indicator values. If the efficiency graph of a cell has a steep decreasing slope, that cell is considered disabled. Establishing a threshold for index values is a straightforward, but typical, method in this area [11]. For instance, a cell is recognized as faulty if the value of an index is less than (or greater than) the predetermined threshold. Other approaches, such as in [12], consider a profile for each index, which depicts the indicator's normal state. The issues with employing a thresh old are avoided since the fault detection stage continuously measures each indicator's divergence from its profile. A solution is offered in [13] to issues like personnel needing to correctly configure the parameters for profile building and deviation detection [14]. The Kolmogorov-Smirnov test compares the index's distribution with its ideal state as the functional approach. Each index's profile contains a record of the statistical distribution of the index in normal mode [14]. One of the current issues is that alarms may not detect all network faults. As a result, the network problem might not be discovered for several hours or days. Finding cells that are referred to as sleeping cells, and which are unable to send an alert to the center, is the key issue in this subject. On the other hand, it is impossible to guarantee the presence or absence of faults in the system by looking at the value of just one index. So, it is essential to assess multiple indicators' values [13], simultaneously. It is possible to implement an intelligent and dynamic fault detection mechanism. According to the study in [15], defective cells can be divided into three categories: deteriorated cells that are still functional while having subpar quality, paralyzed cells, in which a severe fault occurred, but the fault has little to no negative impact on the user experience, and does not result in disgruntled customers, and network cells that eventually shut down and cannot carry traffic. In [16], the process of detecting disabled cells is carried out by creating observation graphs, binary classification, and a list of nearby cells. Along with classifier quality, accuracy in recognizing the frequency pattern of network issues should also be considered. It was demonstrated that, despite the classifiers' high sensitivity in diagnosis, they produce many false alarms, making them unsuitable for industrial use. Network faults have been found in [7] utilizing the Self-Organization Map (SOM) and K-means clustering methods. SOM is not only used in the scenario mentioned above. By creating the techniques for cell categorization, the researcher in [17] improved the control of the state of the cells in the cell network. An anomaly is defined as a significant data disagreement with SOM nodes. The use of a local threshold to identify abnormalities distinguishes this study from similar ones, because local approaches improve diagnosis accuracy and decrease the production of false alarms. The distribution of deviations in the network can be used with the adaptive threshold in this investigation [18]. Garc sets, categorization, and SOM are three data mining techniques that have been used to measure the performance of mobile networks [19]. These techniques are used to discretize the value of the indicators and to categorize the status of a network into good, normal, bad, and unacceptable network states. In [20], data vectors representing the system's normal state were employed to perform the learning step of a neural network used to construct the normal state profile. The confidence interval for the normal/non-normal state was established through experimental analysis. To determine the reason for the fault, inference rules were produced using the trained network.