## 2 Overview of Published Relevant Research  
Numerous published studies deal with modeling and analysis of the influence of various factors on the throughput and delay, but also on other key performance indicators in the mobile network where different types of data are transmitted [1]. The paper [2] lists some of the most important factors that affect the throughput, namely radio technology, hardware configuration and limitations, negative effects of signal propagation, user mobility, network infrastructure of the mobile operator. The authors developed a model based on machine learning methods for classifying mobile network stability based on user activity, the mentioned infrastructure, the mobile device model, the link stability, the location, and the day period. Based on empirical research in the paper [3], an analysis of network performance such as throughput, delay, packet loss [4] is given, and it is determined that there is a significant influence of several factors on those variables. An interesting methodology for evaluating the performance of 3G networks and mobile applications is presented in [5]. TCP throughput, Round Trip Time (RTT) and retransmission rate are considered as indicators of network performance, and their dependence on factors such as signal strength, topology, locations, the day period. Research in [6] has shown that there is a correlation between signal strength and throughput, but that it depends on the time of day at which performance measurement was performed. Modeling of the LTE network performance of 4G networks is the subject of the research of [7]. A similar study was conducted and published by the authors in [8] who developed a stochastic model for throughput prediction in an LTE network. The authors in [9] use the _Random Forest_ algorithm to create a model that predicts the throughput in a cellular network based on the operator's data on its performance, radio link quality data and contextual data. A significant result from previous research is made by numerous algorithms for network performance prediction, the systematic comparison of which is presented in [10]. The analysis of latency, as an important indicator of network performance for M2M traffic and online game traffic in the High Speed Packet Access (HSPA) network technology is given in [11], and a similar study was performed in [12]. The model for predicting the level of user experience in web search, based on logistic regression, was created and presented in [13]. For the prediction of the DL and uplink (UL) traffic in the base stations of the mobile network along the highway, a model based on convolutional artificial neural networks is presented in [14]. Using multilayer artificial neural networks in [15], [PERSON] and his team modeled the DL throughput in an LTE network based on several independent variables related to the radio traffic conditions in the networks. Following the example of that research, with conceptual similarities, in this paper, an MLP model is created in which the metaparameters are completely different, a significantly larger set of independent variables as inputs to the model, but also a significantly larger set of training and testing data. After testing multiple model variants with different training-testing ratios, it was found that a training and testing data ratio of 70%:30% gives good results, although a 50%:50% ratio also gives good model results. From the practical aspect, the purposeful application of the MLP model in a specific geo-space and analytically explained interactions of virtual and physical devices of the M:tel network and the M9J set an additional dimension of the quality of predictive traffic modeling in networks. The following Table 1 presents an overview of the referenced previous research in the subject of this paper with highlighted novelties presented in this paper.