# Mobile Network Operators' Assessment Based on Drive-Test Campaign in Urban Area for iPerf Scenario
## 1 Introduction
adopt a comprehensive approach by utilizing ML models for throughput prediction in LTE and 5G networks, with a particular emphasis on real-life measurements and network slices' performance benchmarking [12]. Another prevalent area of investigation in the literature is the performance analysis of mobile broadband networks in diverse scenarios. [PERSON] et al. provide a detailed analysis of mobile broadband performance in urban settings [14]. The authors of [15; 16; 17] show results of drive test campaign for LTE and 5G networks in Malaysia. Throughput measurements performed in urban [16] and rural [15; 17] environments allowed us to assess the benefits and transition potential from 4G to 5G non-standalone (NSA) networks. Meanwhile, Ref. [18] focuses on 5G performance measurements in the context of bus transportation systems, emphasizing the need for tools to identify and enhance services. [PERSON] et al. focus on studying 4G/5G mobile network technologies applied to smart city unmanned aerial vehicle (UAV) services [19]. The results indicate that 5G is more sensitive to altitude changes than 4G, which should be considered in future mobile network planning. On the other hand, an analysis of specific services could be valuable from the end-user perspective. Therefore, some measurement campaigns concentrate on selected services, such as WhatsApp [20], YouTube [7], and web browsing [21]. Aspects of QoE evaluation and measurement, in addition to QoS, are often considered in the literature, e.g., [22; 23; 24]. A mapping model of 5G key performance indices (KPIs) to QoS and, finally, to QoE metrics is proposed in [23]. Meanwhile, the authors of [24] analyze the impact of software components and virtualization usage on 5G network performance. Furthermore, the authors of [22] delves into the measurement processes of QoS and QoE in 5G technology, highlighting the diverse approaches and the absence of specific standards for 5G. The paper emphasizes the importance of tailoring measurement processes to the specific location and conditions of 5G implementation, offering valuable insights for effective QoS and QoE assessments in diverse scenarios. KPI measurements in a 5G NSA network are considered in [25]. The paper compares the results against actual hands-on 4G network measurements, confirming the expected superiority of 5G in basic KPIs, including up- and DL throughput, latency, and jitter, using different tools, e.g., iPerf, iPerf3 [26], and Cisco TREx. Moreover, utilization of other KPI measurement tools to assess the throughput and latency for mobile phones is described in [27]. Signal metrics are often the basis for estimating QoS metrics, especially those related to transmission rate or spectrum efficiency. It results from certain theoretical premises, i.e., the Shannon-Hartley theorem [28]:  
\[C=B\cdot\log_{2}(1+SNR), \tag{1}\]  
where \(C\) and \(B\) are the channel capacity and bandwidth, respectively; and \(SNR\) is the signal-to-noise ratio (SNR). Capacity is the boundary value of the throughput that can be achieved for the given bandwidth and signal quality (i.e., SNR). On the other hand, the parameter \(C/B\) is referred to as spectrum efficiency. Equation (1) implies that throughput should be directly related to a signal quality measure. In the case of mobile networks, the primary metric determining the received signal quality is the signal-to-interference-plus-noise ratio (SINR). However, in practice, a parameter determining the received signal power, i.e., reference signal received power (RSRP), is more often used to estimate throughput. This approach is proposed by [PERSON] [29], an expert from Rohde & Schwarz (R&S) company. A similar methodology was also used to determine the capacity (i.e., boundary throughput) in LTE and 5G networks by the Polish regulatory authority (i.e., the Office of Electronic Communications) when developing requirements for MNOs related to a 5G auction for the C band [30].