## 2 Review of Relevant Published Research  
In a previous study [10], the authors created models for end-to-end delay prediction in Cellular Vehicle-to-Everything (C-V2X) communication using different ML techniques. Model training was performed on KPI-related variables, and data was collected from real LTE networks. In this paper, prediction is viewed as a delay classification problem depending on a given threshold. Similar research is conducted in [11] with a focus on delay prediction for V2X applications in Mobile Cloud/Edge Computing systems. The proposed prediction framework in this case consists of a component based on machine learning techniques and a statistical component. Paper [12] presents an algorithm for resource allocation prediction in LTE uplink (UL) connection for machine to machine (M2M) applications. Mathematical models for prediction probability, successful prediction probability, failed prediction probability, resource utilization/underutilization probability and a mean uplink delay model were developed. All these models are validated using a simulation model implemented on the OPNET platform. An original approach based on machine learning for delay prediction in 4G networks is presented in [13]. To create the model, the authors used real data from three different mobile networks. Paper [14] considers a case study related to the Industrial Internet of Things (IIoT), in which the potential of digitization of mines is investigated. For this purpose, a software tool for sending sensor data using the LTE network is presented, and predictive delay models are created in order to evaluate the network performance. [PERSON] and [PERSON] (2013), in their paper [15], developed a Packet Prediction Mechanism (PPM), based on mathematical models, for delay prediction when using real-time services. The main research focus was on a virtual queue concept, which has the function of predicting the behavior of incoming packets in the future based on the packets currently in the queue. Due to the increasing user demand for real-time services, the development of wireless access technologies that provide greater bandwidth is evident every day. Therefore, the same team of authors, in the published research paper [16], proposed and designed an LTE scheduling mechanism and PPM. In doing so, the authors assume that the proposed PPM will increase capacity, reduce resource consumption and thereby increase network efficiency. The assumption is that the monitoring and prediction of QoS indicators are the basic prerequisites for user satisfaction in the use of LTE network services. Thus, delay and average user throughput are considered as key indicators of network performance in [17]. The authors created models to estimate the values of these dependent variables as linear functions of total network traffic and an average Channel Quality Indicator (CQI). In [18], the subject of research is the changes in Round-trip time (RTT) delay and the prediction of the increase in these values in mobile broadband networks. Four classification models based on machine learning were developed, using data from a large number of probes in the network, and the best classification performance was shown by the binary ensemble model. The essential characteristics of the previously analyzed papers are shown in Table 1. It provides information on reference numbers of the papers, the models and techniques used, the prediction problem being solved (regression/classification), the service/application being observed, some of the dimensionality reduction methods and techniques, if applied, and performance. Compared to previously analyzed published research papers, the following five contributions stand out as the main improvements and novelties presented in this paper:  
* Network delay is investigated by observing a real geospatial and LTE network segment as very important factors affecting KPIs;