## 1 Introduction
### Compensating the Occurred Fault  
The purpose of this section is to suggest relevant action to solve problems. In [10], these measures were divided into simple and parametric. Simple activities are those that can be executed without additional arguments. In other words, there is a direct relationship between the cause of the fault and the necessary measures to fix it. For example, if the cause of the fault is, in part, a hardware problem, the corresponding action is to send a technician to the site and replace that part. On the other hand, parametric measures are not easily determined and require the implementation of different algorithms. For example, if it is necessary to change the parameter value, its new value should be calculated. Since the number of parametric measures is large, it would be valuable to introduce an algorithm to identify the standards. To compensate for the fault in the study in [34], changing and resetting the parameters of neighboring cells, and optimizing the covered area by them, was utilized. Things such as filling the space of the disabled cell, resetting the antennas' power consumption, and choosing the correct value of the parameters through fuzzy logic and reinforcement learning were performed in this study. In general, the necessary measures in correcting the occurred fault depend entirely on the detected fault in the first step and the cause of the detected fault in the second step. Among the measures to fix the fault are resetting the parameters, reducing the angle of the antenna, or increasing the strength of the received signal by the user.  
The distinctive feature of all past works is the limited view of the faults in the network, because, by considering this field's data complexity and the uncertainty in the number of parameters assigned by experts for a model's relationships, it was difficult to consider a large number of network events. On the other hand, the accuracy of the obtained model from this method strongly depends on the expert's knowledge and the assigned value to each parameter. Thus, this article focuses on designing a model that can perform the initial stages of model construction with minimal dependence on expert opinion and only using available statistical information. In addition to reducing the model's dependency on humans, the accuracy of the produced model is increased by removing the effects of human error. The general framework of the conducted work of this article is illustrated in the following figure. As seen in Figure 2, this paper uses three types of data: performance support system data, drive test data, and customer contact center data. This paper's second and third sections discuss the first two sources of information. These three sources of information on the network mode can determine the existence of a problem in an area. In addition, using the information fusion methods, this system can automatically identify the fault causes in cellular networks with the help of three different data types.  
The structure of this article is as follows. In the second section, after getting familiar with the indicators used in the qualitative assessment of the network condition and the types of faults affecting the value of each indicator, the essential source of available data, called the performance support system, is examined in detail. The results are evaluated to analyze the cause of a fault. The third section introduces another source of available fault diagnosis: drive test data. In the fourth section, after submitting the third source of data, subscriber complaints, the results of the previous two sections are reviewed and examined. Finally, in the fifth section, we summarize, conclude and provide a perspective for future work.