# Mobile Network Operators' Assessment Based on Drive-Test Campaign in Urban Area for iPerf Scenario
## 1 Introduction  
Implementing and improving assessment mechanisms of the quality of service (QoS) and quality of network (QoN) in digital telecommunications have resulted in the dynamic development of cellular networks and a large diversity of services provided to subscribers and users. It is worth noting that QoS and QoN metrics are the basis for evaluating subsequent generations of mobile networks. For example, for the fifth-generation (5G) New Radio (NR), so-called International Mobile Telecommunications (IMT) for 2020 (IMT-2020), parameters such as user experienced or peak data rates, latency, area traffic capacity, connection density, and spectrum and network energy efficiency are used to show significant improvement in network performance and QoS compared to fourth-generation (4G), i.e., Long-Term Evolution (LTE) or IMT-Advanced, networks [1]. This type of approach and these metrics are also used in political, social, and economic solutions, e.g., in determining the targets and objectives for the development of the digital environment in Europe for 2030, the so-called European Digital Decade [2]. QoS assessment is carried out by mobile network operators (MNOs), providers of services and mobile applications, telecommunications market regulatory authorities [3], and, increasingly often, users of networks and services. QoS measurement from the MNO or user sides can be performed at the network level. Generally, the first approach is only available to MNOs. It is used to optimize the allocation of radio resources toindividual users/services [4; 5]. However, the second approach can be used by both users and professional companies that provide QoS measurement and MNO evaluation services, e.g., on behalf of regulatory authorities or MNOs themselves [6; 7]. Such measurements conducted by independent companies can give the MNOs objective information on QoS and QoN. From the viewpoint of the average network user, assessing QoS (e.g., specific values of throughput or latency) is usually too technical and incomprehensible. Therefore, the so-called quality of experience (QoE), related to the subjective perception of the service, is increasingly analyzed. It is crucial when assessing the smoothness and quality of video or voice transmissions, teleconferencing, online games, website and social media content's downloading speed, or data transmission speed. The need for user involvement to evaluate QoE is a significant drawback of this approach. Hence, automatons (i.e., machines) or artificial intelligence algorithms are increasingly used for this purpose [8; 9; 10; 11]. Implementing a specific service, e.g., data transmission or video streaming, is necessary to assess the QoS or QoE for such a service. From the viewpoint of professional companies providing services in the QoS/QoE/QoN evaluation, this forces the use of the so-called active measurements. For this purpose, user equipment (UE) operating in the network of a selected MNO is used to realize a specific service, which allows for the QoS/QoE assessment. This approach does not make it possible to assess QoS or MNO continuously and independently of the service provided. Hence, when performing active QoS/QoE measurements, the so-called passive measurements are carried out using scanners. Then, the power and quality of reference signals transmitted by the base stations of a selected MNO are measured, regardless of the provided connections or services [7]. In modern communication networks, QoS has witnessed substantial advancements, with a focus on addressing the challenges posed by the deployment of 4G, 5G, and emerging technologies. One of the innovative directions of development is the integration of machine learning (ML) models to optimize network performance [12; 13]. [PERSON] et al. propose an ML framework for predicting downlink (DL) throughput in 4G and 5G networks, showcasing the need for advanced design and optimization methodologies [13]. Similarly, [PERSON] et al.