Mobile Network Operators’ Assessment Based on Drive-Test Campaign in Urban Area for iPerf Scenario

Abstract

The development of new telecommunication services requires the implementation of advanced technologies and the next generations of networks. Currently, the Long-Term Evolution (LTE) is a widely used standard. On the other hand, more and more mobile network operators (MNOs) are implementing the fifth-generation (5G) New Radio standard in their networks. It allows for increasing throughput, spectral, and energy efficiency and maximizing coverage, while reducing latency. The effectiveness of the introduced changes is measured by assessing the quality of service (QoS) in mobile networks. The paper presents the result evaluation of the QoS measurement campaign carried out using the drive test method in an urban area for four MNOs. We analyze the data transmission scenario, which is the basis of most modern telecommunications services. The result comparison provides an assessment of the 5G service implementation advancement by MNOs. In this analysis, we consider many QoS metrics (e.g., session time, throughput, and round-trip time) and parameters defining the radio signal quality (i.e., reference signal received power, signal-to-interference-plus-noise ratio). Our work also included searching for relationships between these parameters, using a correlation analysis. It allows for the selection of uncorrelated parameters to assess the quality of network, i.e., MNO evaluation, in terms of the provided QoS.

Full text

Citation: Zmysłowski, D.; Kelner, J.M.
Mobile Network Operators’
Assessment Based on Drive-Test
Campaign in Urban Area for iPerf
Scenario. Appl. Sci. 2024,14, 1268.
https://doi.org/10.3390/
app14031268
Academic Editor: Andrea Prati
Received: 4 January 2024
Revised: 24 January 2024
Accepted: 26 January 2024
Published: 3 February 2024
Correction Statement: This article
has been republished with a minor
change. The change does not affect
the scientific content of the article and
further details are available within the
backmatter of the website version of
this article.
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
applied
sciences
Article
Mobile Network Operators’ Assessment Based on Drive-Test
Campaign in Urban Area for iPerf Scenario
Dariusz Zmysłowski * and Jan M. Kelner
Institute of Communications Systems, Faculty of Electronics, Military University of Technology,
00-908 Warsaw, Poland; jan.kelner@wat.edu.pl
*Correspondence: dariusz.zmyslowski@wat.edu.pl
Abstract: The development of new telecommunication services requires the implementation of
advanced technologies and the next generations of networks. Currently, the Long-Term Evolution
(LTE) is a widely used standard. On the other hand, more and more mobile network operators
(MNOs) are implementing the fifth-generation (5G) New Radio standard in their networks. It
allows for increasing throughput, spectral, and energy efficiency and maximizing coverage, while
reducing latency. The effectiveness of the introduced changes is measured by assessing the quality of
service (QoS) in mobile networks. The paper presents the result evaluation of the QoS measurement
campaign carried out using the drive test method in an urban area for four MNOs. We analyze the
data transmission scenario, which is the basis of most modern telecommunications services. The
result comparison provides an assessment of the 5G service implementation advancement by MNOs.
In this analysis, we consider many QoS metrics (e.g., session time, throughput, and round-trip time)
and parameters defining the radio signal quality (i.e., reference signal received power, signal-to-
interference-plus-noise ratio). Our work also included searching for relationships between these
parameters, using a correlation analysis. It allows for the selection of uncorrelated parameters to
assess the quality of network, i.e., MNO evaluation, in terms of the provided QoS.
Keywords: 5G New Radio; mobile network; mobile network operator (MNO); quality of service
(QoS); throughput; reference signal received power (RSRP); signal-to-interference-plus-noise ratio
(SINR)
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 sub-
sequent 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, con-
nection 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 to
Appl. Sci. 2024,14, 1268. https://doi.org/10.3390/app14031268 https://www.mdpi.com/journal/applsci

Appl. Sci. 2024,14, 1268 2 of 25
individual 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 [8–11].
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 QoN 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 emerg-
ing technologies. One of the innovative directions of development is the integration of
machine learning (ML) models to optimize network performance [
12
,
13
]. A. Al-Thaedan
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, D. Minovski et al. 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. A.A. El-Saleh et al. provide a detailed
analysis of mobile broadband performance in urban settings [
14
]. The authors of [
15
–
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.
S. Horsmanheimo 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
–
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.

Appl. Sci. 2024,14, 1268 3 of 25
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·log2(1+SN R), (1)
where
C
and
B
are the channel capacity and bandwidth, respectively; and
SN R
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 M.
Lorenz [
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
]. However,
we propose a novel approach: using RSRP and SINR for throughput and jitter estimation
in UL and DL, respectively.
The main aim of this paper is an analysis of relationships between radio reference
signal parameters (such as RSRP, SINR, or Reference Signal Received Quality (RSRQ)) and
QoS metrics (i.e., throughput and jitter) in the 5G network. We analyzed the scenario of
throughput assessment by using the iPerf application [
26
,
31
,
32
] and a measurement testbed
based on R&S equipment. Measurements in Warsaw for four MNOs were performed by
the Systemics-PAB company as part of the drive test campaign. Systemics-PAB provides
QoS/QoE assessment services in mobile networks. In the analyzed scenario, throughput
and lost datagrams rates are measured for Transmission Control Protocol (TCP) and User
Datagram Protocol (UDP) in both uplink (UL) and DL. Additionally, jitter is measured for
UDP. In the drive tests, the measurement of signal metrics, i.e., RSRP, SINR, and RSRQ, was
performed. The measurements were carried out at the 2.1 and 2.6 GHz bands. In the mobile
network, the coverage and capacity issues are connected with the QoS and radio signal
parameters. However, based on the collected measurement data, we cannot unambiguously
draw conclusions about coverage and capacity. From a propagation viewpoint, the range
of base stations for the analyzed bands are similar. Therefore, we may expect the areal
aspect of the coverage to be similar for these bands and all MNOs. We calculate correlation
coefficients for various pairs of measured parameters, similarly to [
13
]. The obtained results
are the basis for selecting the best signal parameters for estimating throughput or other
signal metrics. This approach can also be used in modeling some parameters based on
others. Finally, the obtained results allow for the assessment of the MNOs regarding 5G
network availability and provided QoS.
Our key contributions are as follows:
•
Selection of measurement data and correlation analysis of QoS metrics for iPerf sce-
nario, four MNOs, TCP and UDP, and UL and DL based on drive-test measurement
campaign in Warsaw;
•
Analysis of linear regression between QoS parameters, which can be used in their
modeling or estimation;

Appl. Sci. 2024,14, 1268 4 of 25
•
Linking the obtained results with radio resources used by individual MNOs and their
radio access network (RAN);
•
Discussion of the results and MNO comparison based on the assessment of the corre-
lation level of QoS parameters;
•
Original approach to the classification/identification of MNOs in Poland based on the
RSRQ-5G measurement.
The remainder of the paper is organized as follows. In Section 2, we describe the
measurement scenario, testbed, iPerf application, and the QoS and reference signal metrics
measured in the drive test campaign. The correlation analysis and relationships between
QoS parameters are presented in Sections 3and 4, respectively. A discussion of the results,
including the comparison of MNOs, is contained in Section 5. Section 6provides a summary.
2. Drive Test Campaign
2.1. Measurement Scenario
The QoS analysis was carried out based on a 33.5-h drive test campaign in an urban
environment that was performed on 23–27 September of the 2021 year by the Systemics-
PAB company, which provides a QoS/QoE assessment in mobile networks for MNOs,
regulatory authorities, or other entities [
33
]. The time scheme of the tests is presented in
Table 1.
Table 1. Time schedule of measurement campaign.
Data Time
23 September 2021 14:42–18:18
24 September 2021 08:34–17:39
25 September 2021 08:11–16:41
26 September 2021 08:41–18:19
27 September 2021 07:59–09:19
The measurement campaign was carried out for four MNOs: Orange, T-Mobile, Play,
and Plus, called Orange Polska, T-Mobile Polska, P4, and Polkomtel, respectively. They are
global MNOs providing 4G/5G services in Poland. In the following, we use the shorter
brand-name MNOs.
Nowadays, the MNOs use 5G NSA technology. Two methods of ensuring transmission
for 5G services are implemented in their networks [33]:
•
Dynamic sharing of 10 or 15 MHz channels within a frequency band at 2.1 GHz for 5G
or LTE services, used by Orange, T-Mobile, and Play;
•Providing a 40 MHz channel within a frequency band at 2.6 GHz used by Plus.
The detailed description of frequency allocation for each MNO is presented in Appendix A.
The purpose of the drive tests was to collect statistically representative measurement
data enabling the assessment of the network readiness of four Polish MNOs in Warsaw to
support 5G services concerning the service scenarios defined in ‘Mobile and wireless com-
munications Enablers for the Twenty-twenty Information Society-II’ (METIS II) project [
34
].
It was possible by conducting a complete set of tests, such as interactive data transmission,
iPerf-supported tests, sequential web browsing, and multimedia streaming transfers based
on YouTube service. Table 2presents the number of measurements (so-called bins) for the
tested services per each MNO. The total number of performed measurements (bins) was
46,608, of which 340 were incorrect (i.e., lack of a complete set of measured parameters).
Figure 1presents the percentage of the analyzed service bins for each MNO and the per-
centage of MNO bins in the iPerf test. In this paper, we only analyzed the measurement
results for the iPerf tests. We will continue our studies for other services shortly.

Appl. Sci. 2024,14, 1268 5 of 25
Table 2. Number of performed bins for tested services per each MNO.
MNO iPerf HTTP Browsing Interactive YouTube All Services
Orange 2859 4846 1027 1900 10,632
Play 2879 6382 1071 6105 16,437
Plus 3005 3762 716 795 8278
T-Mobile 3060 4293 903 2665 10,921
All MNOs 11,803 19,283 3717 11,465 46,268
Appl. Sci. 2024, 14, x FOR PEER REVIEW 5 of 27
and the percentage of MNO bins in the iPerf test. In this paper, we only analyzed the
measurement results for the iPerf tests. We will continue our studies for other services
shortly.
Table 2. Number of performed bins for tested services per each MNO.
MNO iPerf HTTP Browsing Interactive YouTube All Services
Orange 2859 4846 1027 1900 10,632
Play 2879 6382 1071 6105 16,437
Plus 3005 3762 716 795 8278
T-Mobile 3060 4293 903 2665 10,921
All MNOs 11,803 19,283 3717 11,465 46,268
(a) (b)
Figure 1. (a) Percentage of analyzed service bins for each MNO and (b) percentage of MNO bins in
iPerf test.
The measurement campaign was carried out in the Capital City of Warsaw. Figure 2
illustrates the measurement vehicle route during the drive test campaign [33]. The average
speed of the measurement vehicle varied between 0 and 105 km/h.
Figure 1. (a) Percentage of analyzed service bins for each MNO and (b) percentage of MNO bins in
iPerf test.
The measurement campaign was carried out in the Capital City of Warsaw. Figure 2
illustrates the measurement vehicle route during the drive test campaign [
33
]. The average
speed of the measurement vehicle varied between 0 and 105 km/h.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 6 of 27
Figure 2. Measurement route during drive test campaign in Warsaw [33].
2.2. Measurement Testbed
Drive tests were realized using the mobile testbed installed on special cars’ equipped
with a set of professional measuring R&S equipment (see Figure 3 [35]), such as [33,35]:
• R&S Smart Benchmarker Rel. v20.3.95;
• Measuring terminals (UEs) based on Samsung Galaxy S21 + 5G (SM-G996BDS)
(Suwon, Republic of Korea) working as active test probes;
• R&S SwissQual QualiPoc software(Rel. 20.3.121);
• Passive Radio Frequency (RF) scanner R&S TSME6 (Munich, Germany);
• Global Positioning System (GPS) receiver with the GPS antenna.
Figure 3. Drive-test cars with R&S Smart Bechmarker [35].
The R&S Smart Benchmarker Rel. v20.3.95 worked as the main control, synchroniza-
tion, and measurement unit that controlled and managed all devices of the test station
within the test campaigns. The used measuring equipment was fully operational and per-
formed reliable measurements, which were checked and verified during pretests. The
measuring stations were operated by qualified, experienced personnel from the System-
ics-PAB company. The measurement terminals were used to ensure a fair assessment of
all MNOs, enabling the aggregation of all frequency bands used by the measured net-
works. The main aim of using the passive scanner was to measure the QoS metrics that
Figure 2. Measurement route during drive test campaign in Warsaw [33].

Appl. Sci. 2024,14, 1268 6 of 25
2.2. Measurement Testbed
Drive tests were realized using the mobile testbed installed on special cars’ equipped
with a set of professional measuring R&S equipment (see Figure 3[35]), such as [33,35]:
•R&S Smart Benchmarker Rel. v20.3.95;
•
Measuring terminals (UEs) based on Samsung Galaxy S21 + 5G (SM-G996BDS) (Suwon,
Republic of Korea) working as active test probes;
•R&S SwissQual QualiPoc software(Rel. 20.3.121);
•Passive Radio Frequency (RF) scanner R&S TSME6 (Munich, Germany);
•Global Positioning System (GPS) receiver with the GPS antenna.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 6 of 27
Figure 2. Measurement route during drive test campaign in Warsaw [33].
2.2. Measurement Testbed
Drive tests were realized using the mobile testbed installed on special cars’ equipped
with a set of professional measuring R&S equipment (see Figure 3 [35]), such as [33,35]:
• R&S Smart Benchmarker Rel. v20.3.95;
• Measuring terminals (UEs) based on Samsung Galaxy S21 + 5G (SM-G996BDS)
(Suwon, Republic of Korea) working as active test probes;
• R&S SwissQual QualiPoc software(Rel. 20.3.121);
• Passive Radio Frequency (RF) scanner R&S TSME6 (Munich, Germany);
• Global Positioning System (GPS) receiver with the GPS antenna.
Figure 3. Drive-test cars with R&S Smart Bechmarker [35].
The R&S Smart Benchmarker Rel. v20.3.95 worked as the main control, synchroniza-
tion, and measurement unit that controlled and managed all devices of the test station
within the test campaigns. The used measuring equipment was fully operational and per-
formed reliable measurements, which were checked and verified during pretests. The
measuring stations were operated by qualified, experienced personnel from the System-
ics-PAB company. The measurement terminals were used to ensure a fair assessment of
all MNOs, enabling the aggregation of all frequency bands used by the measured net-
works. The main aim of using the passive scanner was to measure the QoS metrics that
Figure 3. Drive-test cars with R&S Smart Bechmarker [35].
The R&S Smart Benchmarker Rel. v20.3.95 worked as the main control, synchroniza-
tion, and measurement unit that controlled and managed all devices of the test station
within the test campaigns. The used measuring equipment was fully operational and
performed reliable measurements, which were checked and verified during pretests. The
measuring stations were operated by qualified, experienced personnel from the Systemics-
PAB company. The measurement terminals were used to ensure a fair assessment of all
MNOs, enabling the aggregation of all frequency bands used by the measured networks.
The main aim of using the passive scanner was to measure the QoS metrics that represented
the quality and power of radio reference signals, e.g., RSRP, SINR, or RSRQ. The scanner
supports all frequency bands used in tested mobile networks.
The measurements, data processing, and data analysis were carried out under stan-
dards defined by ETSI [36–42] and best industry practices described by R&S in [43,44].
During the tests, controlled measurement conditions were ensured:
•The measuring terminals were in conditions with a uniform, controlled temperature;
•
The parameters of the measurement terminals affecting the measurement quality, such
as processor load or processor/battery temperature, were continuously monitored;
The equipment was mounted in special housings in the roof box at a height of about
1.8 m.
2.3. iPerf Tool
The iPerf tool is used for network performance measurement, testing, and tuning. It is
free (i.e., based on Berkeley Software Distribution (BSD) license) and available for various
operating systems, including CentOS Linux, FreeBSD, macOS, OpenBSD, Android, other
Linux distributions, and Windows. The iPerf is compatible with various protocols, i.e., TCP,
UDP, and Stream Control Transmission Protocol (SCTP) with both Internet Protocol (IP)
version 4 (IPv4) and 6 (IPv6), and offers a range of customizable parameters. This tool is a
staple for network administrators and engineers seeking to troubleshoot network problems,
enhance performance, and undertake network experiments. Furthermore, the iPerf plays a
crucial role in the performance testing and benchmarking of various network devices. In
December 2023, the iPerf3-16 was released [26,31,32].
Tests using the iPerf application were aimed at examining the stability and perfor-
mance of the radio transmission link. They were carried out for two types of ISO/OSI

Appl. Sci. 2024,14, 1268 7 of 25
(International Organization for Standardization/Open Systems Interconnection reference
model) transport layer protocols, i.e., TCP and UDP. The set data collected during tests
were sequenced into UL and DL streams. This approach allows us to present the behavior
of applications using TCP and UDP.
TCP provides connection transmission using acknowledgment mechanisms for uniquely
numbered transferred packets called three-way handshakes. It means that the established
connection is open for the duration of data transmission, which guarantees a relatively
high degree of reliability of flow control connections and increases transmission security.
It comes at the cost of information overhead on packet numbering and the need to check
the correct transmission of each packet, which reduces the efficiency of data transfer. TCP
supports the applications for which the certainty of data delivery is crucial, requiring
transfer integrity control, but not time-critical, such as applications based on the following
protocols, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Secure Shell
(SSH), Simple Mail Transfer Protocol (SMTP), and Internet Message Access Protocol (IMAP)
4 [45].
UDP is a connectionless protocol without mechanisms to control the correctness of
data flow, which affects the transfer speed and efficiency. UDP implementation ensures
faster data transfer, but there is no guarantee of data delivery. This protocol is used by real-
time applications, mainly those that exchange large volumes of traffic and do not expect
confirmations. It is used primarily by videoconferences, data streaming, and network
gaming, as well as applications based on Voice over Internet Protocol (VoIP) or Domain
Name System (DNS) [46].
The parameter values adopted in the iPerf tests are contained in Table 3. In TCP, the
following test profile was used during iPerf testing: 3 streams of 40 Mbps (7 s transfer time)
and 3 streams of 8 Mbps each (5 sec transfer time) for DL and UL, respectively.
Table 3. Parameters of iPerf tests for TCP and UDP.
Parameter TCP UDP
Target throughput (Mbps) 8 for UL/40 for DL 100
Total target throughput (Mbps) 24 for UL/120 for DL 100
Parallel streams 3 10
Buffer size 8192 1200
Transfer duration (s) 5 for UL/7 for DL 3
UDP is mainly used for network stress testing because TCP automatically limits the
throughput to adapt to the available bandwidth. To this aim, a bandwidth for UDP should
be chosen far above what the connection can handle, e.g., if a user wants to stress a 10 Mbps
connection, he/she should send about 100 Mbps of traffic [
31
]. Hence, the Lost Datagrams
Rate (LDR) for UDP obtained in the test is very high compared to TCP.
2.4. Measured QoS and Signal Metrics
In the analyzed scenario, we evaluated QoS and radio signal quality measures for UL
and DL, using the iPerf application for two protocols, TCP and UDP. As basic QoS metrics,
we use the following:
•
Th-Sx (Mbps)—Throughput from the sender is defined as an average throughput
measurer on the sender side. Basically, in [
43
], throughput is defined as an average UL
or DL throughput over the configured active transfer duration. In the tests, Th-Sx was
calculated separately for UL and DL.
•
Th-Rx (Mbps)—Throughput at the receiver is defined as an average throughput mea-
sured on the receiver side. Similarly to Th-Sx, throughput is defined as an average UL
or DL throughput over the configured active transfer duration [
43
]. In this research,
Th-Rx was calculated separately for UL and DL.
•
Th-Rate (%)—Throughput at Receiver Rate, which is calculated based on the following
formula:

Appl. Sci. 2024,14, 1268 8 of 25
Th-Rate =100% ·Th-Rx
Th-Sx , (2)
•
JT (ms)—Jitter is a measure of the Internet Protocol (IP) packet jitter reported by the
iPerf application. It is usually equal to or more than 0 on the sender or receiver sides,
respectively. Only available as an intermediate result for UDP [43].
•
LDR (%)—Lost Datagrams Rate is the percentage of lost datagrams in relation to the
number of total datagrams. Lost Datagrams are defined as the number of datagrams
that did not reach the receiver before the end of the active transfer duration and count
as lost. Total Datagrams is the number of datagrams sent on the sender side. If the
datagram size is smaller than one Ethernet frame, this usually corresponds to the
number of IP packets. The parameters mentioned above are usually analyzed for
UDP [43].
•RDR (%)—Received Datagrams Rate, which is determined as follows:
RDR =100% −LDR, (3)
Additionally, using the R&S TSME6 scanner, the following radio signal quality param-
eters are measured:
•
RSRP (dBm)—reference signal received power (RSRP) is utilized mainly to rank
among different candidate cells in accordance with their signal strength. Generally,
the reference signals on the first antenna port are used to determine this parameter.
However, the reference signals sent on the second port can also be used in addition to
the radio stations on the first port if UE can detect that they are being transmitted [
47
].
Basically, RSRP is defined as the linear average over the power contributions (in watts)
of the resource elements that carry cell-specific reference signals within the considered
measurement frequency bandwidth [48,49].
•
SINR (dB)—Signal-to-interference-plus-noise ratio (SINR) is defined as the linear
average over the power contribution (in watts) of the resource elements carrying cell-
specific reference signals divided by the linear average of the noise and interference
power contribution (in watts) over the resource elements carrying cell-specific refer-
ence signals within the same frequency bandwidth [
47
]. In fact, it is only measured for
the reference signal. Therefore, it is called a Reference Signal-SINR (RS-SINR) [
48
,
49
].
•
RSRP-5G (dBm)—RSRP for 5G NR is widely named as a Second Synchronization-RSRP
(SS-RSRP) [
50
,
51
]. It is defined as the linear average over the power contributions
(in watts) of the resource elements that carry secondary synchronization signals. It
represents the quantitative measure of SS-RSRP. The RSRP-5G range is from –156 to –31
dBm [
52
]. This parameter gives possibilities to compare the strengths of signals from
individual cells in 5G networks, which is key for cell selection or handover. SS-RSRP
is the equivalent of RSRP used in LTE [53].
•
RSRQ-5G (dB)—Reference Signal Received Quality (RSRQ) for 5G NR is named as a
Second Synchronization-RSRQ (SS-RSRQ) [
50
,
51
]. It is defined as the ratio of N
×
SS-
RSRP/NR carrier Received Signal Strength Indication (RSSI), where N is the number
of resource blocks in the NR carrier RSSI measurement bandwidth. The measurements
in the numerator and denominator shall be made over the same set of resource blocks.
The SS-RSRQ measurements can be used for cell selection, cell reselection, and mobility
procedures. The RSRQ-5G range is defined from –43 to 20 dB [
52
]. SS-RSRQ is used in
5G NR networks to determine the quality of the radio channel. RSRQ, unlike RSRP,
also includes interference level due to the inclusion of RSSI in the calculation. This
parameter is also used for cell selection and handover if the RSRP is insufficient. It
happens mainly in border parts of a cell. It is similar to RSRQ determined in LTE [
53
].
•
SINR-5G (dB)—SINR for 5G is often called a Second Synchronization-SINR (SS-
SINR) [
50
,
51
]. It is defined as the linear average over the power contribution (in
watts) of the resource elements carrying secondary synchronization signals divided
by the linear average of the noise and interference power contribution (in watts). In

Appl. Sci. 2024,14, 1268 9 of 25
5G networks, SINR-5G is reported as a coded value via measurement report to a base
station. The reported range is from 0 to 127, total 128 values, where values 0 and
127 represent SINR < −23 dB and SINR > 40 dB, respectively [52].
The method of determining the radio signal quality parameters in the 5G-NR system
is defined by the 3GPP standard, particularly by the specification [50,51].
3. Correlation Analysis of QoS Metrics
3.1. PCC Results for TCP
Our analysis is based on the Pearson Correlation Coefficient (PCC) [
7
] between a
pair of two variables represented by analyzed parameters. The description of PCC and
interpretation of its values [
54
] is shown in Appendix B. Based on Equation (A1), we
determined the correlation degree for every pair of two measured metrics defined in
Section 2.4. The PCC results for TCP are presented in Figure 4. This figure depicts one
heatmap averaged for all MNOs (a); and four heatmaps for each MNO, i.e., (b) Orange,
(c) Play, (d) Plus, and (e) T-Mobile, respectively. Each heatmap shows PCCs for UL and DL
illustrated in upper-right and lower-left edges, respectively.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 10 of 27
(a)
(b) (c)
(d) (e)
Figure 4. PCCs for TCP, UL/DL: (a) averaged all MNOs, (b) Orange, (c) Play, (d) Plus, and (e) T-
Mobile.
Figure 4 shows that =−1PCC for LDR and RDR. It means that the relationship be-
tween these parameters is linear, as shown in the results from Equation (3). So, next, we
only analyze RDR.
For TCP and UL, we may see that Th-Rx is very strongly correlated with the Tx-Sx,
Th-Rate, and RDR for all MNOs. In the case of TCP and DL, Th-Rx is very strongly corre-
lated only with Tx-Sx for all MNOs. The correlation between Tx-Rx and Th-Rate is weak
or moderate, and the correlation between Th-Rx and RDR is moderate or strong. Gener-
ally, for UL and DL, we can classify the relationship between QoS metrics (i.e., Th-Rx, Th-
Rate, and RDR) and signal parameters (i.e., RSRP, SINR, RSRP-5G, SINR-5G, RSRQ-5G)
as moderate. In the DL case, only the Th-Rate weakly correlates with the signal metrics,
whereas the PCCs for signal parameters indicate a moderate or strong correlation between
them. By determining PCCs for the entire data set, i.e., for all MNOs (see Figure 4a), we
can notice that the correlation degree is usually smaller than for individual MNOs. This is
due to the fact that the data set of analyzed parameters for MNOs varies, as is illustrated
later in the paper. The RSRQ-5G example shows the best. In this case, the averaged PCCs
for all MNOs indicate a very weak correlation, while the corresponding PCCs for individ-
ual MNOs are much larger.
Figure 4. PCCs for TCP, UL/DL: (a) averaged all MNOs, (b) Orange, (c) Play, (d) Plus, and (e) T-
Mobile.

Appl. Sci. 2024,14, 1268 10 of 25
Figure 4shows that
PCC =−
1 for LDR and RDR. It means that the relationship
between these parameters is linear, as shown in the results from Equation (3). So, next, we
only analyze RDR.
For TCP and UL, we may see that Th-Rx is very strongly correlated with the Tx-Sx, Th-
Rate, and RDR for all MNOs. In the case of TCP and DL, Th-Rx is very strongly correlated
only with Tx-Sx for all MNOs. The correlation between Tx-Rx and Th-Rate is weak or
moderate, and the correlation between Th-Rx and RDR is moderate or strong. Generally, for
UL and DL, we can classify the relationship between QoS metrics (i.e., Th-Rx, Th-Rate, and
RDR) and signal parameters (i.e., RSRP, SINR, RSRP-5G, SINR-5G, RSRQ-5G) as moderate.
In the DL case, only the Th-Rate weakly correlates with the signal metrics, whereas the
PCCs for signal parameters indicate a moderate or strong correlation between them. By
determining PCCs for the entire data set, i.e., for all MNOs (see Figure 4a), we can notice
that the correlation degree is usually smaller than for individual MNOs. This is due to the
fact that the data set of analyzed parameters for MNOs varies, as is illustrated later in the
paper. The RSRQ-5G example shows the best. In this case, the averaged PCCs for all MNOs
indicate a very weak correlation, while the corresponding PCCs for individual MNOs are
much larger.
3.2. PCC Results for UDP
The PCC results for UDP are presented in Figure 5. Similar to TCP (see Figure 4),
Figure 5illustrates one heatmap averaged for all MNOs (a); and four heatmaps for each
MNO, i.e., (b) Orange, (c) Play, (d) Plus, and (e) T-Mobile, respectively. Each heatmap
shows PCCs for UL and DL illustrated in the upper-right and lower-left edges, respectively.
The evaluation of PCCs for UDP generally shows that the correlation degrees of
parameters are lower than for TCP. Th-Rx is very weakly correlated with Th-Sx and RDR.
Meanwhile, the correlation of Th-Rx with Th-Rate is very strong (i.e., linear), and with JT, it
is moderate or strong. As mentioned in Section 2.3, for UDP, Th-Sx should be set to a large
value that exceeds the throughput achievable. When analyzing measured Th-Sx values,
they are usually constant values, much larger than Th-Rx. Therefore, considering Equation
(2), we should expect a small absolute value of PCC for Th-Rx and Th-Sx and a very large
one for Th-Rx and Th-Rate. On the other hand, the practically constant value of Th-Sx is
the reason for a very weak correlation with other analyzed metrics. In the UDP analysis, JT
is additionally included. As mentioned, this parameter is moderately (Plus), borderline
moderately/strongly (Orange, Play), or strongly (T-Mobile) correlated with Th-Rx and
Th-Rate. On the other hand, the correlation of JT with signal metrics is weak. In the case of
Th-Rx and Th-Rate, correlation degrees with signal parameters are usually moderate for
UL and DL. The nature of the correlation between the signal parameters is similar for UDP
and TCP, i.e., from moderate to strongly correlated. This is due to the fact that the signal
parameters depend primarily on the propagation conditions and the mutual location of the
UE and base station and not on the provided services.

Appl. Sci. 2024,14, 1268 11 of 25
Appl. Sci. 2024, 14, x FOR PEER REVIEW 11 of 27
3.2. PCC Results for UDP
The PCC results for UDP are presented in Figure 5. Similar to TCP (see Figure 4),
Figure 5 illustrates one heatmap averaged for all MNOs (a); and four heatmaps for each
MNO, i.e., (b) Orange, (c) Play, (d) Plus, and (e) T-Mobile, respectively. Each heatmap
shows PCCs for UL and DL illustrated in the upper-right and lower-left edges, respec-
tively.
(a)
(b) (c)
(d) (e)
Figure 5. PCCs for UDP, UL/DL: (a) average of all MNOs, (b) Orange, (c) Play, (d) Plus, and (e) T-
Mobile.
The evaluation of PCCs for UDP generally shows that the correlation degrees of pa-
rameters are lower than for TCP. Th-Rx is very weakly correlated with Th-Sx and RDR.
Meanwhile, the correlation of Th-Rx with Th-Rate is very strong (i.e., linear), and with JT,
it is moderate or strong. As mentioned in Section 2.3, for UDP, Th-Sx should be set to a
large value that exceeds the throughput achievable. When analyzing measured Th-Sx val-
ues, they are usually constant values, much larger than Th-Rx. Therefore, considering
Equation (2), we should expect a small absolute value of PCC for Th-Rx and Th-Sx and a
very large one for Th-Rx and Th-Rate. On the other hand, the practically constant value of
Th-Sx is the reason for a very weak correlation with other analyzed metrics. In the UDP
analysis, JT is additionally included. As mentioned, this parameter is moderately (Plus),
borderline moderately/strongly (Orange, Play), or strongly (T-Mobile) correlated with Th-
Figure 5. PCCs for UDP, UL/DL: (a) average of all MNOs, (b) Orange, (c) Play, (d) Plus, and
(e) T-Mobile.
4. Relationship between QoS Metrics
4.1. Analysis for TCP Results
For TCP, we analyzed three QoS parameters, i.e., Th-Rx, Th-Rate, and RDR. From a
practical viewpoint, Th-Rx is crucial. Th-Rx is generally moderately correlated with the
signal metrics for the three MNOs, i.e., Orange, Play, and T-Mobile. For UL, the largest
PCC (i.e., 0.45
<PCC <
0.53) was obtained for RSRP (for Orange and Play) or RSRP-5G
(for T-Mobile), while the more minor (i.e., 0.29
<PCC <
0.4) was obtained for SINR or
SINR-5G. For DL and the same three MNOs, higher correlations were obtained between
Th-Rx and SINR or SINR-5G (i.e., 0.54
<PCC <
0.62) than between Th-Rx and RSRP
or RSRP-5G (i.e., 0.28
<PCC <
0.45). For Plus, the highest Th-Rx correlation occurs for
RSRQ-5G, and PCCs are equal to 0.35 or 0.57 for UL and DL, respectively.
Considering the obtained results, we conclude that RSRP (or possibly RSRP-5G) should
be used to estimate Th-Rx for UL TCP. It can be regarded as a similar approach to the one
proposed by M. Lorenz [
29
] and the Office of Electronic Communications, i.e., the Polish
regulatory authority [
30
]. However, for DL TCP, we propose using SINR (or alternatively
SINR-5G) for Th-Rx estimation. Figures 6and 7illustrate measured Th-Rx versus RSRP or
SINR and appropriate regression lines for (a) UL and (b) DL, respectively.

Appl. Sci. 2024,14, 1268 12 of 25
Appl. Sci. 2024, 14, x FOR PEER REVIEW 12 of 27
Rx and Th-Rate. On the other hand, the correlation of JT with signal metrics is weak. In
the case of Th-Rx and Th-Rate, correlation degrees with signal parameters are usually
moderate for UL and DL. The nature of the correlation between the signal parameters is
similar for UDP and TCP, i.e., from moderate to strongly correlated. This is due to the fact
that the signal parameters depend primarily on the propagation conditions and the mu-
tual location of the UE and base station and not on the provided services.
4. Relationship between QoS Metrics
4.1. Analysis for TCP Results
For TCP, we analyzed three QoS parameters, i.e., Th-Rx, Th-Rate, and RDR. From a
practical viewpoint, Th-Rx is crucial. Th-Rx is generally moderately correlated with the
signal metrics for the three MNOs, i.e., Orange, Play, and T-Mobile. For UL, the largest
PCC (i.e., 0.45 0.53PCC<<) was obtained for RSRP (for Orange and Play) or RSRP-5G
(for T-Mobile), while the more minor (i.e., 0.29 0.4PCC<<) was obtained for SINR or
SINR-5G. For DL and the same three MNOs, higher correlations were obtained between
Th-Rx and SINR or SINR-5G (i.e., 0.54 0.62PCC<<) than between Th-Rx and RSRP or
RSRP-5G (i.e., 0.28 0.45PCC<< ). For Plus, the highest Th-Rx correlation occurs for
RSRQ-5G, and PCCs are equal to 0.35 or 0.57 for UL and DL, respectively.
Considering the obtained results, we conclude that RSRP (or possibly RSRP-5G)
should be used to estimate Th-Rx for UL TCP. It can be regarded as a similar approach to
the one proposed by M. Lorenz [29] and the Office of Electronic Communications, i.e., the
Polish regulatory authority [30]. However, for DL TCP, we propose using SINR (or alter-
natively SINR-5G) for Th-Rx estimation. Figures 6 and 7 illustrate measured Th-Rx versus
RSRP or SINR and appropriate regression lines for (a) UL and (b) DL, respectively.
(a) (b)
Figure 6. Measured Th-Rx versus (a) RSRP for four MNOs, TCP, and UL; or (b) SINR for four MNOs,
TCP, and DL.
Figure 6. Measured Th-Rx versus (a) RSRP for four MNOs, TCP, and UL; or (b) SINR for four MNOs,
TCP, and DL.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 13 of 27
(a) (b)
Figure 7. Regression lines of Th-Rx versus (a) RSRP for four MNOs, TCP, and UL; or (b) SINR for
four MNOs, TCP, and DL.
Throughput measurement data for TCP are limited to 22.83 and 118.24 Mbps for UL
and DL (see Figure 6a,b), respectively. It is due to the specificity of this protocol. The as-
sessment of the throughput of individual MNOs is complex based on measurement data
due to their spreading. This evaluation is facilitated by the regression lines presented in
Figure 7a,b. Basically, we can see that, in UL, the lines for Orange and T-Mobile overlap,
and the line for Play is close to them. In the DL case, the Orange and Play lines overlap,
while the T-Mobile line has a similar slope. The similarity of the lines for Orange and T-
Mobile may result from the mentioned RAN sharing and the same base station locations.
Therefore, we may generally assume that the throughput versus radio signal parameters
for these three MNOs can be modeled with one average curve. The Plus regression lines
for both UL and DL are clearly different from other MNOs. The throughput at a given
RSRP/SINR value is worse or beer than the other MNOs for UL and DL, respectively. A
more significant DL throughput may result from the use of the 40 MHz band at the 2.6
GHz frequency, which is used only by Plus in Poland.
For four MNOs and TCP, Figures 8–11 present RSRP versus SINR or RSRP-5G versus
SINR-5G, respectively. In each figure, we show the measurement data (see Figures 8 and
10) or appropriate regression lines (see Figures 9 and 11) for (a) UL and (b) DL, respec-
tively.
(a) (b)
Figure 8. Measured RSRP versus SINR for four MNOs, TCP: (a) UL or (b) DL.
Figure 7. Regression lines of Th-Rx versus (a) RSRP for four MNOs, TCP, and UL; or (b) SINR for
four MNOs, TCP, and DL.
Throughput measurement data for TCP are limited to 22.83 and 118.24 Mbps for UL
and DL (see Figure 6a,b), respectively. It is due to the specificity of this protocol. The
assessment of the throughput of individual MNOs is complex based on measurement data
due to their spreading. This evaluation is facilitated by the regression lines presented in
Figure 7a,b. Basically, we can see that, in UL, the lines for Orange and T-Mobile overlap,
and the line for Play is close to them. In the DL case, the Orange and Play lines overlap,
while the T-Mobile line has a similar slope. The similarity of the lines for Orange and
T-Mobile may result from the mentioned RAN sharing and the same base station locations.
Therefore, we may generally assume that the throughput versus radio signal parameters
for these three MNOs can be modeled with one average curve. The Plus regression lines
for both UL and DL are clearly different from other MNOs. The throughput at a given
RSRP/SINR value is worse or better than the other MNOs for UL and DL, respectively. A
more significant DL throughput may result from the use of the 40 MHz band at the 2.6 GHz
frequency, which is used only by Plus in Poland.

Appl. Sci. 2024,14, 1268 13 of 25
For four MNOs and TCP, Figures 8–11 present RSRP versus SINR or RSRP-5G versus
SINR-5G, respectively. In each figure, we show the measurement data (see Figures 8and 10)
or appropriate regression lines (see Figures 9and 11) for (a) UL and (b) DL, respectively.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 13 of 27
(a) (b)
Figure 7. Regression lines of Th-Rx versus (a) RSRP for four MNOs, TCP, and UL; or (b) SINR for
four MNOs, TCP, and DL.
Throughput measurement data for TCP are limited to 22.83 and 118.24 Mbps for UL
and DL (see Figure 6a,b), respectively. It is due to the specificity of this protocol. The as-
sessment of the throughput of individual MNOs is complex based on measurement data
due to their spreading. This evaluation is facilitated by the regression lines presented in
Figure 7a,b. Basically, we can see that, in UL, the lines for Orange and T-Mobile overlap,
and the line for Play is close to them. In the DL case, the Orange and Play lines overlap,
while the T-Mobile line has a similar slope. The similarity of the lines for Orange and T-
Mobile may result from the mentioned RAN sharing and the same base station locations.
Therefore, we may generally assume that the throughput versus radio signal parameters
for these three MNOs can be modeled with one average curve. The Plus regression lines
for both UL and DL are clearly different from other MNOs. The throughput at a given
RSRP/SINR value is worse or beer than the other MNOs for UL and DL, respectively. A
more significant DL throughput may result from the use of the 40 MHz band at the 2.6
GHz frequency, which is used only by Plus in Poland.
For four MNOs and TCP, Figures 8–11 present RSRP versus SINR or RSRP-5G versus
SINR-5G, respectively. In each figure, we show the measurement data (see Figures 8 and
10) or appropriate regression lines (see Figures 9 and 11) for (a) UL and (b) DL, respec-
tively.
(a) (b)
Figure 8. Measured RSRP versus SINR for four MNOs, TCP: (a) UL or (b) DL.
Figure 8. Measured RSRP versus SINR for four MNOs, TCP: (a) UL or (b) DL.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 14 of 27
(a) (b)
Figure 9. Regression lines of RSRP versus SINR for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 10. Measured RSRP-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 11. Regression lines of RSRP-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
Similar to the above, analyzing the measured parameters is difficult due to their
spreading. Therefore, our assessment is based on appropriate regression lines. Generally,
the results of the power and quality of reference signals measured according to the meth-
odology for the LTE and 5G standards (see Figures 8 and 9 or Figures 10 and 11,
Figure 9. Regression lines of RSRP versus SINR for four MNOs, TCP: (a) UL or (b) DL.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 14 of 27
(a) (b)
Figure 9. Regression lines of RSRP versus SINR for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 10. Measured RSRP-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 11. Regression lines of RSRP-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
Similar to the above, analyzing the measured parameters is difficult due to their
spreading. Therefore, our assessment is based on appropriate regression lines. Generally,
the results of the power and quality of reference signals measured according to the meth-
odology for the LTE and 5G standards (see Figures 8 and 9 or Figures 10 and 11,
Figure 10. Measured RSRP-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.

Appl. Sci. 2024,14, 1268 14 of 25
Appl. Sci. 2024, 14, x FOR PEER REVIEW 14 of 27
(a) (b)
Figure 9. Regression lines of RSRP versus SINR for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 10. Measured RSRP-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 11. Regression lines of RSRP-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
Similar to the above, analyzing the measured parameters is difficult due to their
spreading. Therefore, our assessment is based on appropriate regression lines. Generally,
the results of the power and quality of reference signals measured according to the meth-
odology for the LTE and 5G standards (see Figures 8 and 9 or Figures 10 and 11,
Figure 11. Regression lines of RSRP-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
Similar to the above, analyzing the measured parameters is difficult due to their
spreading. Therefore, our assessment is based on appropriate regression lines. Gener-
ally, the results of the power and quality of reference signals measured according to the
methodology for the LTE and 5G standards (see Figures 8and 9or Figures 10 and 11,
respectively) are similar for all MNOs. In Figure 9, the lines of two MNOs, i.e., for Orange
and T-Mobile, or Plus and Play, practically overlap. However, the differences between these
two line groups are minor. However, in Figure 11, the lines of three MNOs (i.e., for Orange,
T-Mobile, and Play) overlap. For RSRP-5G versus SINR-5G, there is a significant difference
only for Plus. In this case, we can see that, with a lower RSRP-5G value, a higher quality,
i.e., SINR-5G, may be obtained. It allows for this MNO to provide higher QoS, probably
due to using the 2.6 GHz band for 5G technology.
The parameters of all regression lines for TCP are listed in Table A3 in Appendix C.
4.2. Analysis for UDP Results
For UDP, we considered four QoS parameters, i.e., Th-Rx, Th-Rate, JT, and RDR. From
a data analysis perspective, Th-Rx and JT are crucial. For all MNOs, Th-Rx is moderately
or strongly correlated with the signal metrics. For UL, the largest PCC (i.e.,
PCC =
0.62)
is obtained for RSRP-5G (Orange and T-Mobile). The PCCs between Th-Rx and RSRP are
equal between 0.47 and 0.59 for all MNOs. The correlation between Th-Rx and SINR-5G
or SINR is generally weak (i.e., 0.24
<PCC <
0.39) for all MNOs, but for SINR and Plus,
the PCC is equal to 0.47. For DL and three MNOs (i.e., Orange, Play, and T-Mobile), we
obtain a higher correlation between Th-Rx and SINR/SINR-5G (i.e., 0.34
<PCC <
0.54)
than between Th-Rx and RSRP/RSRP-5G (i.e., 0.28
<PCC <
0.47). The correlation degree
of signal parameters with JT is weaker than with Th-Rx, which results in a large spreading
of data. Due to the specificity of JT measurement as a time indicator, it has a lower limit
equal to 0. Therefore, the estimation is subject to a larger error, similar to Th-Rx for TCP,
which has an upper limit. PCCs between JT and RSRP are in the range from –0.40 to –0.24
and are relatively the best among the signal metrics (e.g.,
−
0.40
<PCC <−
0.21 for SINR).
Therefore, for UDP, we propose a Th-Rx estimation based on RSRP or SINR for UL and
DL, respectively, similarly to TCP. Figures 12 and 13 depict measured Th-Rx versus RSRP
or SINR and appropriate regression lines for (a) UL and (b) DL, respectively. Analogous
graphs for JT are presented in Figures 14 and 15.

Appl. Sci. 2024,14, 1268 15 of 25
Appl. Sci. 2024, 14, x FOR PEER REVIEW 15 of 27
respectively) are similar for all MNOs. In Figure 9, the lines of two MNOs, i.e., for Orange
and T-Mobile, or Plus and Play, practically overlap. However, the differences between
these two line groups are minor. However, in Figure 11, the lines of three MNOs (i.e., for
Orange, T-Mobile, and Play) overlap. For RSRP-5G versus SINR-5G, there is a significant
difference only for Plus. In this case, we can see that, with a lower RSRP-5G value, a higher
quality, i.e., SINR-5G, may be obtained. It allows for this MNO to provide higher QoS,
probably due to using the 2.6 GHz band for 5G technology.
The parameters of all regression lines for TCP are listed in Table A3 in Appendix C.
4.2. Analysis for UDP Results
For UDP, we considered four QoS parameters, i.e., Th-Rx, Th-Rate, JT, and RDR.
From a data analysis perspective, Th-Rx and JT are crucial. For all MNOs, Th-Rx is mod-
erately or strongly correlated with the signal metrics. For UL, the largest PCC (i.e.,
0.62PCC =) is obtained for RSRP-5G (Orange and T-Mobile). The PCCs between Th-Rx
and RSRP are equal between 0.47 and 0.59 for all MNOs. The correlation between Th-Rx
and SINR-5G or SINR is generally weak (i.e., 0.24 0.39PCC<<) for all MNOs, but for
SINR and Plus, the PCC is equal to 0.47. For DL and three MNOs (i.e., Orange, Play, and
T-Mobile), we obtain a higher correlation between Th-Rx and SINR/SINR-5G (i.e.,
0.34 0.54PCC<<) than between Th-Rx and RSRP/RSRP-5G (i.e., 0.28 0.47PCC<<). The
correlation degree of signal parameters with JT is weaker than with Th-Rx, which results
in a large spreading of data. Due to the specificity of JT measurement as a time indicator,
it has a lower limit equal to 0. Therefore, the estimation is subject to a larger error, similar
to Th-Rx for TCP, which has an upper limit. PCCs between JT and RSRP are in the range
from –0.40 to –0.24 and are relatively the best among the signal metrics (e.g.,
0.40 0.21PCC−< <− for SINR). Therefore, for UDP, we propose a Th-Rx estimation based
on RSRP or SINR for UL and DL, respectively, similarly to TCP. Figures 12 and 13 depict
measured Th-Rx versus RSRP or SINR and appropriate regression lines for (a) UL and (b)
DL, respectively. Analogous graphs for JT are presented in Figures 14 and 15.
(a) (b)
Figure 12. Measured Th-Rx versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for four
MNOs, UDP, and DL.
Figure 12. Measured Th-Rx versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for four
MNOs, UDP, and DL.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 16 of 27
(a) (b)
Figure 13. Regression lines of Th-Rx versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for
four MNOs, UDP, and DL.
(a) (b)
Figure 14. Measured JT versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for four MNOs,
UDP, and DL.
(a) (b)
Figure 15. Regression lines of JT versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for four
MNOs, UDP, and DL.
Figure 13. Regression lines of Th-Rx versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for
four MNOs, UDP, and DL.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 16 of 27
(a) (b)
Figure 13. Regression lines of Th-Rx versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for
four MNOs, UDP, and DL.
(a) (b)
Figure 14. Measured JT versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for four MNOs,
UDP, and DL.
(a) (b)
Figure 15. Regression lines of JT versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for four
MNOs, UDP, and DL.
Figure 14. Measured JT versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for four MNOs,
UDP, and DL.

Appl. Sci. 2024,14, 1268 16 of 25
Appl. Sci. 2024, 14, x FOR PEER REVIEW 16 of 27
(a) (b)
Figure 13. Regression lines of Th-Rx versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for
four MNOs, UDP, and DL.
(a) (b)
Figure 14. Measured JT versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for four MNOs,
UDP, and DL.
(a) (b)
Figure 15. Regression lines of JT versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for four
MNOs, UDP, and DL.
Figure 15. Regression lines of JT versus (a) RSRP for four MNOs, UDP, and UL; or (b) SINR for four
MNOs, UDP, and DL.
For UDP and Th-Rx, we can draw similar conclusions as for TCP. The regression lines
for three MNOs (i.e., Orange, T-Mobile, and Play) have similar slopes and intercept points.
Primarily, two lines for Orange and T-Mobile in UL and Play and T-Mobile overlap. Only
regression lines for Plus, UL and DL, are significantly different. Similar to TCP, at the same
RSRP/SINR level, Plus in UDP has lower or higher throughput than other MNOs for UL
and DL, respectively.
In the case of JT, regression lines for Orange and T-Mobile overlap for UL and DL.
On the other hand, jitter is higher for Play and Plus in UL, and for Play also in DL,
respectively. For Plus, JT is lower than Orange/T-Mobile for
SI NR <
10
dB
and higher for
SI NR >10 dB.
The results obtained for UDP confirm the two main conclusions formulated for TCP:
using RAN sharing technology by Orange and T-Mobile provides similar throughput
results, and using the 2.6 GHz band by Plus gives higher throughput for DL.
For UDP, we also analyze the relationship between the reference signal parameters
representing the received power level and its quality. Measured data and regression lines of
RSRP versus SINR and RSRP-5G versus SINR-5G are shown in Figures 16–19, respectively.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 17 of 27
For UDP and Th-Rx, we can draw similar conclusions as for TCP. The regression lines
for three MNOs (i.e., Orange, T-Mobile, and Play) have similar slopes and intercept points.
Primarily, two lines for Orange and T-Mobile in UL and Play and T-Mobile overlap. Only
regression lines for Plus, UL and DL, are significantly different. Similar to TCP, at the same
RSRP/SINR level, Plus in UDP has lower or higher throughput than other MNOs for UL
and DL, respectively.
In the case of JT, regression lines for Orange and T-Mobile overlap for UL and DL.
On the other hand, jier is higher for Play and Plus in UL, and for Play also in DL, respec-
tively. For Plus, JT is lower than Orange/T-Mobile for 10 dBSINR < and higher for
10 dBSINR >.
The results obtained for UDP confirm the two main conclusions formulated for TCP:
using RAN sharing technology by Orange and T-Mobile provides similar throughput re-
sults, and using the 2.6 GHz band by Plus gives higher throughput for DL.
For UDP, we also analyze the relationship between the reference signal parameters
representing the received power level and its quality. Measured data and regression lines
of RSRP versus SINR and RSRP-5G versus SINR-5G are shown in Figures 16–19, respec-
tively.
(a) (b)
Figure 16. Measured RSRP versus SINR for four MNOs, UDP: (a) UL or (b) DL.
(a) (b)
Figure 17. Regression lines of RSRP versus SINR for four MNOs, UDP: (a) UL or (b) DL.
Figure 16. Measured RSRP versus SINR for four MNOs, UDP: (a) UL or (b) DL.

Appl. Sci. 2024,14, 1268 17 of 25
Appl. Sci. 2024, 14, x FOR PEER REVIEW 17 of 27
For UDP and Th-Rx, we can draw similar conclusions as for TCP. The regression lines
for three MNOs (i.e., Orange, T-Mobile, and Play) have similar slopes and intercept points.
Primarily, two lines for Orange and T-Mobile in UL and Play and T-Mobile overlap. Only
regression lines for Plus, UL and DL, are significantly different. Similar to TCP, at the same
RSRP/SINR level, Plus in UDP has lower or higher throughput than other MNOs for UL
and DL, respectively.
In the case of JT, regression lines for Orange and T-Mobile overlap for UL and DL.
On the other hand, jier is higher for Play and Plus in UL, and for Play also in DL, respec-
tively. For Plus, JT is lower than Orange/T-Mobile for 10 dBSINR < and higher for
10 dBSINR >.
The results obtained for UDP confirm the two main conclusions formulated for TCP:
using RAN sharing technology by Orange and T-Mobile provides similar throughput re-
sults, and using the 2.6 GHz band by Plus gives higher throughput for DL.
For UDP, we also analyze the relationship between the reference signal parameters
representing the received power level and its quality. Measured data and regression lines
of RSRP versus SINR and RSRP-5G versus SINR-5G are shown in Figures 16–19, respec-
tively.
(a) (b)
Figure 16. Measured RSRP versus SINR for four MNOs, UDP: (a) UL or (b) DL.
(a) (b)
Figure 17. Regression lines of RSRP versus SINR for four MNOs, UDP: (a) UL or (b) DL.
Figure 17. Regression lines of RSRP versus SINR for four MNOs, UDP: (a) UL or (b) DL.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 18 of 27
(a) (b)
Figure 18. Measured RSRP-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
(a) (b)
Figure 19. Regression lines of RSRP-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
Based on the results obtained for UDP, we can draw the same conclusions as for TCP
(see Section 4.1, Figures 8–11). It proves that the analysis of the level and quality of the
received signal in mobile networks is independent of the provided services but depends
on the radio resources (i.e., used frequency band), the location of the UE in relation to the
base station (i.e., the distance between them), the shape and urbanization degree of the
terrain, and the related radio wave propagation conditions.
The parameters of all regression lines for UDP are contained in Table A4 in Appendix
C.
5. Discussion
5.1. Synthesis of Results
The basic QoS measures used in the assessment of mobile networks are throughput
and latency [1]. In the iPerf scenario, we mainly analyzed throughput for TCP and UDP,
and additionally jier for UDP. These parameters are strongly correlated with lost or re-
ceived datagram rates (i.e., LDR or RDR). Considering the specificity of both analyzed
protocols, UDP is usually used for network stress testing and the assessment of the maxi-
mum throughput or capacity [26,31,32]. In the context of iPerf tests, it is worth emphasiz-
ing that throughputs obtained for TCP and UDP should not be directly compared. This is
due to the main advantages and considerations of each protocol, as are shown in Table 4
[43].
Figure 18. Measured RSRP-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 18 of 27
(a) (b)
Figure 18. Measured RSRP-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
(a) (b)
Figure 19. Regression lines of RSRP-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
Based on the results obtained for UDP, we can draw the same conclusions as for TCP
(see Section 4.1, Figures 8–11). It proves that the analysis of the level and quality of the
received signal in mobile networks is independent of the provided services but depends
on the radio resources (i.e., used frequency band), the location of the UE in relation to the
base station (i.e., the distance between them), the shape and urbanization degree of the
terrain, and the related radio wave propagation conditions.
The parameters of all regression lines for UDP are contained in Table A4 in Appendix
C.
5. Discussion
5.1. Synthesis of Results
The basic QoS measures used in the assessment of mobile networks are throughput
and latency [1]. In the iPerf scenario, we mainly analyzed throughput for TCP and UDP,
and additionally jier for UDP. These parameters are strongly correlated with lost or re-
ceived datagram rates (i.e., LDR or RDR). Considering the specificity of both analyzed
protocols, UDP is usually used for network stress testing and the assessment of the maxi-
mum throughput or capacity [26,31,32]. In the context of iPerf tests, it is worth emphasiz-
ing that throughputs obtained for TCP and UDP should not be directly compared. This is
due to the main advantages and considerations of each protocol, as are shown in Table 4
[43].
Figure 19. Regression lines of RSRP-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
Based on the results obtained for UDP, we can draw the same conclusions as for TCP
(see Section 4.1, Figures 8–11). It proves that the analysis of the level and quality of the
received signal in mobile networks is independent of the provided services but depends on
the radio resources (i.e., used frequency band), the location of the UE in relation to the base

Appl. Sci. 2024,14, 1268 18 of 25
station (i.e., the distance between them), the shape and urbanization degree of the terrain,
and the related radio wave propagation conditions.
The parameters of all regression lines for UDP are contained in Table A4 in Appendix C.
5. Discussion
5.1. Synthesis of Results
The basic QoS measures used in the assessment of mobile networks are throughput
and latency [
1
]. In the iPerf scenario, we mainly analyzed throughput for TCP and UDP,
and additionally jitter for UDP. These parameters are strongly correlated with lost or re-
ceived datagram rates (i.e., LDR or RDR). Considering the specificity of both analyzed
protocols, UDP is usually used for network stress testing and the assessment of the maxi-
mum throughput or capacity [
26
,
31
,
32
]. In the context of iPerf tests, it is worth emphasizing
that throughputs obtained for TCP and UDP should not be directly compared. This is due
to the main advantages and considerations of each protocol, as are shown in Table 4[43].
Table 4. Main considerations and advantages of UDP and TCP [43].
Property UDP
HTTP/TCP
FTP/TCP
Focus on unidirectional traffic without
dependencies on return channel Yes No No
Uplink throughput is measured on receiving side,
i.e., as the true network throughput Yes No No
Sustainable capacity required as result Yes Yes No
Intermediate throughput is measured on IP-level Yes Yes No
Applicable for the Network Performance Score No Yes No
In the iPerf scenario, we had no way to evaluate latency. Therefore, we use the global
assessment presented by Systemics-PAB in terms of the four implemented measurement
scenarios mentioned in Section 2.1. Based on them, three QoS metrics, so-called main KPIs
for Internet access, i.e., average DL and UL throughput and latency, were determined,
which are depicted in Figure 20 [
33
]. With regard to the comparison of four Polish MNOs,
the obtained results coincide with the conclusions received by Systemics-PAB: throughput
for Plus is the highest and lowest for DL and UL, respectively, and Orange and T-Mobile
have practical the same KPI values.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 19 of 27
Table 4. Main considerations and advantages of UDP and TCP [43].
Property UDP HTTP/TCP FTP/TCP
Focus on unidirectional traffic without dependencies
on return channel Yes No No
Uplink throughput is measured on receiving side,
i.e., as the true network throughput Yes No No
Sustainable capacity required as result Yes Yes No
Intermediate throughput is measured on IP-level Yes Yes No
Applicable for the Network Performance Score No Yes No
In the iPerf scenario, we had no way to evaluate latency. Therefore, we use the global
assessment presented by Systemics-PAB in terms of the four implemented measurement
scenarios mentioned in Section 2.1. Based on them, three QoS metrics, so-called main KPIs
for Internet access, i.e., average DL and UL throughput and latency, were determined,
which are depicted in Figure 20 [33]. With regard to the comparison of four Polish MNOs,
the obtained results coincide with the conclusions received by Systemics-PAB: throughput
for Plus is the highest and lowest for DL and UL, respectively, and Orange and T-Mobile
have practical the same KPI values.
Figure 20. Main KPIs for Internet access for four Polish MNOs based on drive test campaign
(based on [33]).
When analyzing PCCs averaged for all MNOs (see Figures 4a and 5a), we noticed
some discrepancy associated with the RSRQ-5G metric in relation to the PCC for individ-
ual MNOs. Well, the RSRQ-5G-associated correlations are usually much lower for all than
for the selected MNO. This could indicate that the analyzed data set (in the XY plane,
where RSRQ-5G is one of the parameters) for each MNO is clustered and the complete
data set for all MNOs is characterized by high dispersion. Therefore, we hypothesized
that RSRQ-5G can be used to classify/identify MNOs based on measurements. Consider-
ing that RSRQ-5G is strongly correlated with SINR-5G (i.e., 0.64 0.79PCC<<) for all an-
alyzed cases (i.e., all MNOs, TCP and UDP, and UL and DL), we present the measurement
data and regression lines in Figures 21–24 for TCP and UDP, respectively.
Figure 20. Main KPIs for Internet access for four Polish MNOs based on drive test campaign (based
on [33]).

Appl. Sci. 2024,14, 1268 19 of 25
When analyzing PCCs averaged for all MNOs (see Figures 4a and 5a), we noticed
some discrepancy associated with the RSRQ-5G metric in relation to the PCC for individual
MNOs. Well, the RSRQ-5G-associated correlations are usually much lower for all than for
the selected MNO. This could indicate that the analyzed data set (in the XY plane, where
RSRQ-5G is one of the parameters) for each MNO is clustered and the complete data set for
all MNOs is characterized by high dispersion. Therefore, we hypothesized that RSRQ-5G
can be used to classify/identify MNOs based on measurements. Considering that RSRQ-5G
is strongly correlated with SINR-5G (i.e., 0.64
<PCC <
0.79) for all analyzed cases (i.e., all
MNOs, TCP and UDP, and UL and DL), we present the measurement data and regression
lines in Figures 21–24 for TCP and UDP, respectively.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 20 of 27
(a) (b)
Figure 21. Measured RSRQ-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 22. Regression lines of RSRQ-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 23. Measured RSRQ-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
Figure 21. Measured RSRQ-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 20 of 27
(a) (b)
Figure 21. Measured RSRQ-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 22. Regression lines of RSRQ-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 23. Measured RSRQ-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
Figure 22. Regression lines of RSRQ-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.

Appl. Sci. 2024,14, 1268 20 of 25
Appl. Sci. 2024, 14, x FOR PEER REVIEW 20 of 27
(a) (b)
Figure 21. Measured RSRQ-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 22. Regression lines of RSRQ-5G versus SINR-5G for four MNOs, TCP: (a) UL or (b) DL.
(a) (b)
Figure 23. Measured RSRQ-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
Figure 23. Measured RSRQ-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 21 of 27
Figure 24. Regression lines of RSRQ-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
Based on the obtained regression curves (see Figures 22 and 24), we can notice that
the lines do not overlap, thus allowing for the identication or classication of MNOs
based on averaged measurement data, as well as the assessment of their QoS (i.e., KPIs).
For the selected SINR-5G, we can rank MNOs, i.e., Play, T-Mobile, Orange, and Plus, by
the increasing RSRQ-5G. In this way, the obtained assessment of MNOs based on the ref-
erence signal quality coincides with the assessment of MNOs in terms of KPIs analyzed
by Systemics-PAB (see Figure 20).
5.2. Comparison of MNOs
To evaluate the similarity of individual MNOs in terms of the obtained PCC results,
we calculated PCCs for each pair of MNOs, using the heatmaps from Figures 4 and 5 as
input, as shown in Figure 25. In this case, the heatmap shows PCCs for TCP and UDP
illustrated in the upper-right and lower-left edges, respectively. Please note that the dif-
ferent colormap scale ranges from 0.87 to 1.
Figure 25. Similarity assessment of PCC heatmaps for the MNOs, TCP and UDP.
Heatmaps are more similar for TCP than for UDP. Generally, the smallest PCCs occur
for the entire data set (i.e., all MNOs), thus conrming its large variation. The PCCs are
most similar for Orange and T-Mobile. It may result from the fact that these MNOs are
using the same base stations or mast locations. Already, during the development of the
LTE network, these MNOs commissioned the management of RAN, using the RAN shar-
ing technique, for the Net-WorkS! Company (Daytona Beach, FL, USA). Hence, similar
values of the relevant QoS metrics and signal parameters were obtained during drive tests
Figure 24. Regression lines of RSRQ-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
Based on the obtained regression curves (see Figures 22 and 24), we can notice that
the lines do not overlap, thus allowing for the identification or classification of MNOs
based on averaged measurement data, as well as the assessment of their QoS (i.e., KPIs).
For the selected SINR-5G, we can rank MNOs, i.e., Play, T-Mobile, Orange, and Plus, by
the increasing RSRQ-5G. In this way, the obtained assessment of MNOs based on the
reference signal quality coincides with the assessment of MNOs in terms of KPIs analyzed
by Systemics-PAB (see Figure 20).
5.2. Comparison of MNOs
To evaluate the similarity of individual MNOs in terms of the obtained PCC results, we
calculated PCCs for each pair of MNOs, using the heatmaps from Figures 4and 5as input,
as shown in Figure 25. In this case, the heatmap shows PCCs for TCP and UDP illustrated
in the upper-right and lower-left edges, respectively. Please note that the different colormap
scale ranges from 0.87 to 1.

Appl. Sci. 2024,14, 1268 21 of 25
Appl. Sci. 2024, 14, x FOR PEER REVIEW 21 of 27
(a) (b)
Figure 24. Regression lines of RSRQ-5G versus SINR-5G for four MNOs, UDP: (a) UL or (b) DL.
Based on the obtained regression curves (see Figures 22 and 24), we can notice that
the lines do not overlap, thus allowing for the identification or classification of MNOs
based on averaged measurement data, as well as the assessment of their QoS (i.e., KPIs).
For the selected SINR-5G, we can rank MNOs, i.e., Play, T-Mobile, Orange, and Plus, by
the increasing RSRQ-5G. In this way, the obtained assessment of MNOs based on the ref-
erence signal quality coincides with the assessment of MNOs in terms of KPIs analyzed
by Systemics-PAB (see Figure 20).
5.2. Comparison of MNOs
To evaluate the similarity of individual MNOs in terms of the obtained PCC results,
we calculated PCCs for each pair of MNOs, using the heatmaps from Figures 4 and 5 as
input, as shown in Figure 25. In this case, the heatmap shows PCCs for TCP and UDP
illustrated in the upper-right and lower-left edges, respectively. Please note that the dif-
ferent colormap scale ranges from 0.87 to 1.
Figure 25. Similarity assessment of PCC heatmaps for the MNOs, TCP and UDP.
Heatmaps are more similar for TCP than for UDP. Generally, the smallest PCCs occur
for the entire data set (i.e., all MNOs), thus confirming its large variation. The PCCs are
most similar for Orange and T-Mobile. It may result from the fact that these MNOs are
using the same base stations or mast locations. Already, during the development of the
LTE network, these MNOs commissioned the management of RAN, using the RAN shar-
ing technique, for the Net-WorkS! Company (Daytona Beach, FL, USA). Hence, similar
values of the relevant QoS metrics and signal parameters were obtained during drive tests
for these MNOs. The most significant differences in the analyzed heatmap occur for Plus
and Play for TCP and UDP, respectively.
Figure 25. Similarity assessment of PCC heatmaps for the MNOs, TCP and UDP.
Heatmaps are more similar for TCP than for UDP. Generally, the smallest PCCs occur
for the entire data set (i.e., all MNOs), thus confirming its large variation. The PCCs are
most similar for Orange and T-Mobile. It may result from the fact that these MNOs are
using the same base stations or mast locations. Already, during the development of the LTE
network, these MNOs commissioned the management of RAN, using the RAN sharing
technique, for the Net-WorkS! Company (Daytona Beach, FL, USA). Hence, similar values
of the relevant QoS metrics and signal parameters were obtained during drive tests for
these MNOs. The most significant differences in the analyzed heatmap occur for Plus and
Play for TCP and UDP, respectively.
6. Summary
In this paper, we analyzed the QoS metrics and reference signal parameters used in LTE
and 5G for the iPerf scenario, which were obtained in the drive test campaign conducted
by Systemics-PAB in the urban area for four Polish MNOs. These measurement data were
the basis for the correlation analysis performed for TCP and UDP, and UL and DL. Based
on PCC, we selected the best signal parameters that can be used to estimate throughput
for TCP/UDP, and jitter only for UDP. In this regard, we propose to use RSRP for UL
and SINR for DL. In terms of assessing the relationship between radio signal metrics (i.e.,
RSRP and SINR), we find that these metrics are independent of the provided services and
can generally be analyzed independently of the MNO. Only the Plus for the relationship
between RSRP-5G and SINR-5G is characterized by a certain deviation.
On the other hand, analyzing the relationship between RSRQ-5G and SINR-5G makes
it possible to identify MNOs based on averaged measurement data and reflects their KPIs
in a broader scope. The analysis of the measurement results confirmed that the use of the
same RAN contributed to a very similar QoS assessment for two MNOs, i.e., Orange and
T-Mobile. On the other hand, the additional use of the 40 MHz band at the 2.6 GHz band
by Plus for the needs of the emerging 5G NR network allowed for higher RSRQ-5G and
significantly greater throughput for DL. The remaining MNOs do not have radio resources
in this frequency band. Before the auction for the C band, these MNOs (i.e., Orange, Play,
and T-Mobile) could only use bands typical for LTE or older generations (i.e., 900, 1800,
1900, or 2700 MHz) for the 5G NR network. This approach is possible thanks to the principle
of technological neutrality.
In the near future, the authors plan to analyze other scenarios for QoS tests in Polish
mobile networks.
Author Contributions: Conceptualization, D.Z. and J.M.K.; methodology, D.Z. and J.M.K.; software,
D.Z. and J.M.K.; validation, D.Z.; formal analysis, D.Z. and J.M.K.; investigation, D.Z.; resources,
D.Z.; writing—original draft preparation, D.Z. and J.M.K.; writing—review and editing, D.Z. and

Appl. Sci. 2024,14, 1268 22 of 25
J.M.K.; visualization, D.Z. and J.M.K.; supervision, J.M.K.; project administration, J.M.K.; funding
acquisition, J.M.K. All authors have read and agreed to the published version of the manuscript.
Funding: This work was financed by the Military University of Technology (WAT) under project
no. UGB/22-863/2023/WAT on “Modern technologies of wireless communication and emitter
localization in various system applications”.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study was provided by the Systemics-PAB
company. The authors are not authorized to provide them.
Acknowledgments: The authors would like to thank the Systemics-PAB company for providing the
measurement data.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1 presents radio frequency resource allocation for four Polish MNOs in the 2.1
and 2.6 GHz bands [55,56].
Table A1. Radio frequency resource allocation for four Polish MNOs in the 2.1 and 2.6 GHz bands.
Band Frequency (MHz)
Orange Play Plus T-Mobile
2.1 GHz UL FDD * 1920.1–1934.9 1965.1–1979.9 2500–2520 1935.1–1949.9
2.1 GHz DL FDD 2110.1–2124.9 2155.1–2169.9 2620–2640 2125.1–2139.9
2.6 GHz UL FDD 2520–2535 2550–2570 2500–2520 2535–2550
2.6 GHz DL FDD 2640–2655 2670–2690 2620–2640 2655–2670
2.6 GHz UL TDD * – – 2570–2620 –
2.6 GHz DL TDD – – 2570–2620 –
* FDD—Frequency Division Duplex; TDD—Time Division Duplex.
Appendix B
The PCC between a pair of two variables (X,Y)is defined as follows [7]:
PCC =ρ(X,Y)=E{(X−µX)(Y−µY)}
σXσY
, (A1)
where
X={xi}
and
Y={yi}
represent sets of two analyzed parameters;
E{·}
is the
expectation operator,
E{(X−µX)(Y−µY)}
is the covariance of
(X,Y)
; and
µX
,
µY
,
σX
,
and σYrepresent the mean values and standard deviations of Xand Y, respectively, i.e.,
µX=E{X},σX=rEn(X−µX)2o,
µY=E{Y},σY=rEn(Y−µY)2o.
(A2)
In the further analysis, we adopted the interpretation of the PCC according to Table A2 [
54
].
Table A2. Correlation degrees.
Absolute Value of PCC—|PCC| Correlation Degree
0.00 ÷0.19 Very weak correlation
0.20 ÷0.39 Weak correlation
0.40 ÷0.59 Moderate correlation
0.60 ÷0.79 Strong correlation
0.80 ÷1.00 Very strong correlation

Appl. Sci. 2024,14, 1268 23 of 25
Appendix C
Section 4shows the relationships between the QoS and radio signal metrics. The
results are presented in two forms, i.e., the measured data and regression lines, which we
may describe with a general formula:
Y=A·X+B+ξσ, (A3)
where
X
and
Y
are two analyzed QoSs or signal metrics (described in Section 2.4) considered
as abscissa and ordinate variables, respectively;
A
and
B
are line parameters, i.e., a slope
(gradient) and ordinate axe intercept of the line, respectively; and
ξσ
is a zero-mean normal
random variable with a deviation equal to σ.
In Tables A3 and A4, we show the parameter values of the regression lines presented
in Sections 4and 5for all MNOs, TCP and UDP, respectively.
Table A3. Parameters of regression lines for TCP.
Figure
Link Metrics Orange Play Plus T-Mobile
XYA B σA B σA B σA B σ
7 (a) UL RSRP Th-Rx
0.241
42.20
3.832
0.250 41.21 4.256 0.221 35.41 7.340 0.208 39.17 3.783
7 (b) DL SINR Th-Rx
2.613
42.11
24.06
2.706 40.93 26.85 1.687 81.74 28.56 3.006 50.04 26.03
9 (a) UL SINR RSRP
0.752
−
91.62 8.599
0.701 −
88.83
8.563 0.686 −
89.28
8.146 0.710 −
91.87
8.014
9 (b) DL SINR RSRP
0.685
−
91.14 8.625
0.716 −
89.28
8.930 0.774 −
90.13
8.143 0.690 −
91.90
8.225
11 (a) UL SINR-5G RSRP-5G
0.706
−
86.68 8.058
0.820 −
88.22
8.365 0.884 −
95.19
8.637 0.680 −
87.26
8.466
11 (b) DL SINR-5G RSRP-5G
0.683
−
86.79 8.135
0.762 −
87.90
8.399 0.953 −
96.07
8.549 0.652 −
87.95
8.788
22 (a) UL SINR-5G RSRQ-5G
0.277
−
9.180 1.835
0.262 −
10.85
1.854 0.271 −
0.454
1.854 0.264 −
9.847
1.737
22 (b) DL SINR-5G RSRQ-5G
0.271
−
9.259 1.849
0.263 −
10.93
1.963 0.301 −
0.715
1.999 0.289 −
10.14
1.816
Table A4. Parameters of regression lines for UDP.
Figure
Link Metrics Orange Play Plus T-Mobile
XYA B σA B σA B σA B σ
13 (a) UL RSRP Th-Rx
1.150
151.7
15.51
1.150 142.2 20.80 1.020 121.2 12.76 1.180 154.7 18.49
13 (b) DL SINR Th-Rx
6.197
106.8
63.26
5.718 57.28 66.59 4.617 195.1 66.70 5.977 104.6 59.57
15 (a) UL RSRP JT −
0.131
−
7.992 3.338
−
0.174
−
10.13
3.835 −
0.153
−
7.791
5.807 −
0.125
−
7.564
2.873
15 (b) DL SINR JT −
0.088
2.462
1.289
−
0.197
5.738 4.865 −
0.030
1.903 3.418 −
0.071
2.271 4.299
17 (a) UL SINR RSRP
0.667
−
90.41 8.019
0.630 −
88.87
8.482 0.656 −
87.92
7.902 0.571 −
90.69
7.988
17 (b) DL SINR RSRP
0.636
−
90.26 7.633
0.679 −
88.87
8.383 0.630 −
88.15
8.010 0.592 −
90.28
8.213
19 (a) UL SINR-5G RSRP-5G
0.728
−
86.84 7.900
0.694 −
86.93
7.992 0.919 −
94.50
8105 0.630 −
87.04
8.104
19 (b) DL SINR-5G RSRP-5G
0.743
−
87.32 7.071
0.732 −
87.10
8.137 0.917 −
94.78
8.664 0.538 −
86.88
8.534
24 (a) UL SINR-5G RSRQ-5G
0.254
−
9.090 1.862
0.248 −
10.76
1.905 0.275 −
0.279
1.747 0.284 −
10.05
1.808
24 (b) DL SINR-5G RSRQ-5G
0.210
−
8.692 1.927
0.269 −
10.88
2.135 0.270 −
0.396
1.961 0.269 −
10.21
1.857
References
1.
ITU-R. Recommendation ITU-R M.2083-0: IMT Vision—Framework and Overall Objectives of the Future Development of IMT for 2020
and Beyond; M Series Mobile, Radiodetermination, Amateur and Related Satellite Services; International Telecommunication Union
(ITU): Geneva, Switzerland, 2015.
2.
European Commission. Europe’s Digital Decade|Shaping Europe’s Digital Future. Available online: https://digital-strategy.ec.
europa.eu/en/policies/europes-digital-decade (accessed on 30 December 2023).
3.
Mongay Batalla, J.; Sujecki, S.; Kelner, J.M.; ´
Sliwka, P.; Zmysłowski, D. On Studying Active Radio Measurements Estimating the
Mobile Network Quality of Service for the Regulatory Authority’s Purposes. Comput. Netw. 2023,235, 109980. [CrossRef]
4.
Li, J.; Zheng, T.; Guan, W.; Lian, X. An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under
Unlicensed Band. Appl. Sci. 2023,13, 10638. [CrossRef]
5.
Agarwal, B.; Togou, M.A.; Marco, M.; Muntean, G.-M. A Comprehensive Survey on Radio Resource Management in 5G HetNets:
Current Solutions, Future Trends and Open Issues. IEEE Commun. Surv. Tutor. 2022,24, 2495–2534. [CrossRef]
6.
Zmysłowski, D.; Kelner, J.M.; Falkowski-Gilski, P. Mobile Networks’ Analysis in Terms of QoS Performance Assessment. In
Proceedings of the 2022 19th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and
Services (EAI MobiQuitous), Pittsburgh, PA, USA, 14–17 November 2022; EAI: Pittsburgh, PA, USA, 2022.
7.
Zmysłowski, D.; Kelner, J.M. Drive Test-Based Correlation Assessment of QoS Parameters for Exemplary Measurements
Scenario in Suburban Environment. In Proceedings of the 2022 18th International Conference on Web Information Systems and
Technologies (WEBIST), Valletta, Malta, 25–27 October 2022; SciTech Publishing: Valletta, Malta, 2022; pp. 497–504.

Appl. Sci. 2024,14, 1268 24 of 25
8.
Hewage, C.; Ekmekcioglu, E. Multimedia Quality of Experience (QoE): Current Status and Future Direction. Future Internet 2020,
12, 121. [CrossRef]
9.
Dinaki, H.E.; Shirmohammadi, S.; Janulewicz, E.; Côté, D. Deep Learning-Based Fault Localization in Video Networks Using
Only Client-Side QoE. IEEE Trans. Artif. Intell. 2020,1, 130–138. [CrossRef]
10.
Barakabitze, A.A.; Walshe, R. SDN and NFV for QoE-Driven Multimedia Services Delivery: The Road towards 6G and beyond
Networks. Comput. Netw. 2022,214, 109133. [CrossRef]
11. Zheng, G.; Yuan, L. A Review of QoE Research Progress in Metaverse. Displays 2023,77, 102389. [CrossRef]
12.
Minovski, D.; Ögren, N.; Mitra, K.; Åhlund, C. Throughput Prediction Using Machine Learning in LTE and 5G Networks. IEEE
Trans. Mob. Comput. 2023,22, 1825–1840. [CrossRef]
13.
Al-Thaedan, A.; Shakir, Z.; Mjhool, A.Y.; Alsabah, R.; Al-Sabbagh, A.; Nembhard, F.; Salah, M. A Machine Learning Framework
for Predicting Downlink Throughput in 4G-LTE/5G Cellular Networks. Int. J. Inf. Technol. 2024. [CrossRef]
14.
El-Saleh, A.A.; Alhammadi, A.; Shayea, I.; Alsharif, N.; Alzahrani, N.M.; Khalaf, O.I.; Aldhyani, T.H.H. Measuring and Assessing
Performance of Mobile Broadband Networks and Future 5G Trends. Sustainability 2022,14, 829. [CrossRef]
15.
Shayea, I.; Ergen, M.; Azmi, M.H.; Nandi, D.; El-Saleh, A.A.; Zahedi, A. Corrections to “Performance Analysis of Mobile
Broadband Networks With 5G Trends and Beyond: Rural Areas Scope in Malaysia”. IEEE Access 2020,8, 80173–80174. [CrossRef]
16.
Shayea, I.; Azmi, M.H.; Ergen, M.; El-Saleh, A.A.; Han, C.T.; Arsad, A.; Rahman, T.A.; Alhammadi, A.; Daradkeh, Y.I.; Nandi, D.
Performance Analysis of Mobile Broadband Networks With 5G Trends and Beyond: Urban Areas Scope in Malaysia. IEEE Access
2021,9, 90767–90794. [CrossRef]
17.
Shayea, I.; Ergen, M.; Azmi, M.H.; Nandi, D.; El-Salah, A.A.; Zahedi, A. Performance Analysis of Mobile Broadband Networks
With 5G Trends and Beyond: Rural Areas Scope in Malaysia. IEEE Access 2020,8, 65211–65229. [CrossRef]
18.
Fernández-Berrueta, N.; Figueroa-Lorenzo, S.; Bustamante, P.; Arrizabalaga, S.; Velez, I. 5G Performance Measurements in
Mobility for the Bus Transportation System in an Urban Environment. IEEE Access 2023,11, 127132–127144. [CrossRef]
19.
Horsmanheimo, S.; Tuomimäki, L.; Semkin, V.; Mehnert, S.; Chen, T.; Ojennus, M.; Nykänen, L. 5G Communication QoS
Measurements for Smart City UAV Services. In Proceedings of the 2022 16th European Conference on Antennas and Propagation
(EuCAP), Madrid, Spain, 27 March–1 April 2022; pp. 1–5.
20.
Abbas, M. Modelling WhatsApp Traffic Control Time-Based (WTCTB) for 5G Mobile Network. Int. J. Comput. Electron. Asp. Eng.
2023,4, 110–118.
21.
Zmysłowski, D.; Kelner, J.M. Correlational Analysis in QoS Parameter Assessment for 5G Emerging Networks in Poland. In
Proceedings of the 2023 Signal Processing Symposium (SPSympo), Karpacz, Poland, 26–28 September 2023; pp. 226–229.
22.
Mardian, R.D.; Suryanegara, M.; Ramli, K. Measuring Quality of Service (QoS) and Quality of Experience (QoE) on 5G Technology:
A Review. In Proceedings of the 2019 IEEE International Conference on Innovative Research and Development (ICIRD), Jakarta,
Indonesia, 28–30 June 2019; pp. 1–6.
23.
Banovi´c- ´
Curguz, N.; Iliševi´c, D. Mapping of QoS/QoE in 5G Networks. In Proceedings of the 2019 42nd International Convention
on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 20–24 May 2019;
pp. 404–408.
24.
Galluccio, L.; Grasso, C.; Grasso, M.; Raftopoulos, R.; Schembra, G. Measuring QoS and QoE for a Softwarized Video Surveillance
System in a 5G Network. In Proceedings of the 2019 IEEE International Symposium on Measurements & Networking (M&N),
Catania, Italy, 8–10 July 2019; pp. 1–6.
25.
Soós, G.; Ficzere, D.; Varga, P.; Szalay, Z. Practical 5G KPI Measurement Results on a Non-Standalone Architecture. In Proceedings
of the 2020 IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest, Hungary, 20–24 April 2020;
pp. 1–5.
26.
Iperf3: A TCP, UDP, and SCTP Network Bandwidth Measurement Tool. 2023. Available online: https://iperf.fr/ (accessed on 3
January 2024).
27.
Daengsi, T.; Ungkap, P.; Pornpongtechavanich, P.; Wuttidittachotti, P. QoS Measurement: A Comparative Study of Speeds
and Latency for 5G Network Using Different Speed Test Applications for Mobile Phones. In Proceedings of the 2021 IEEE
7th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), Bandung, Indonesia, 23–25
August 2021; pp. 206–210.
28.
Ziółkowski, C.; Kelner, J.M.; Krygier, J.; Chandra, A.; Prokeš, A. Radio Channel Capacity with Directivity Control of Antenna
Beams in Multipath Propagation Environment. Sensors 2021,21, 8296. [CrossRef] [PubMed]
29.
Lorenz, M. How Fast Is My Data Highway? Cost-Effective Estimation of Achievable Data Throughput Based on Network
Scanners. In Proceedings of the 2023 11th Mobile Network Testing Forum (MNT), Paris, France, 23–24 May 2023.
30.
Office of Electronic Communications. Capacity Measurements in 4G/5G Networks (In Polish: Pomiary Przepustowo´sci w Sieciach
4G/5G); Office of Electronic Communications: Warsaw, Poland, 2023.
31.
Wachowski, M. iPerf Testing—Overview and Basic Use Case. Available online: https://codilime.com/blog/iperf-testing/
(accessed on 27 December 2023).
32. Iperf3—Iperf3 3.15 Documentation. Available online: https://software.es.net/iperf/ (accessed on 27 December 2023).
33.
Kondej, J. Readiness of Mobile Networks to Provide 5G Services—Report on the Results of the Research in Warsaw, 23–27 August
2021|Systemics-PAB. Available online: https://www.syspab.eu/readiness-of-mobile-networks-to-provide-5g-services-report-
on-the-results-of-the-research-in-warsaw/ (accessed on 2 January 2024).

Appl. Sci. 2024,14, 1268 25 of 25
34.
Ayoubi, S.E.E.; Jeux, S.; Marache, F.; Pujol, F.; Fallgren, M.; Spapis, P.; Yang, C.; Widaa, A.; Markendahl, J.; Ghanbari, A.; et al.
METIS II. Deliverable D1.1. Refined Scenarios and Requirements, Consolidated Use Cases, and Qualitative Techno-Economic
Feasibility Assessment. 2016. Available online: https://metis-ii.5g-ppp.eu/wp-content/uploads/deliverables/METIS-II_D1.1
_v1.0.pdf (accessed on 3 January 2024).
35.
Maximize Network Quality and Performance. In Test Solution Guide for Targeted, QoE Centric Network Improvements; Rohde &
Schwarz GmbH & Co. KG: Munich, Germany, 2023.
36.
ETSI TR 103 559 V1.1.1 (2019-08); Speech and Multimedia Transmission Quality (STQ)l Best Practices for Robust Network QoS
Benchmark Testing and Scoring. European Telecommunications Standards Institute (ETSI): Sophia Antipolis, France, 2019.
37.
ETSI TS 102 250-6 V1.3.1 (2019-11); Speech and Multimedia Transmission Quality (STQ); QoS Aspects for Popular Services in
Mobile Networks; Part 6: Post Processing and Statistical Methods. European Telecommunications Standards Institute (ETSI):
Sophia Antipolis, France, 2019.
38.
ETSI TS 102 250-1 V2.3.1 (2019-11); Speech and Multimedia Transmission Quality (STQ); QoS Aspects for Popular Services in
Mobile Networks; Part 1: Assessment of Quality of Service. European Telecommunications Standards Institute (ETSI): Sophia
Antipolis, France, 2019.
39.
ETSI TS 102 250-2 V2.7.1 (2019-11); Speech and Multimedia Transmission Quality (STQ); QoS Aspects for Popular Services in
Mobile Networks; Part 2: Definition of Quality of Service Parameters and Their Computation. European Telecommunications
Standards Institute (ETSI): Sophia Antipolis, France, 2019.
40.
ETSI TS 102 250-3 V2.4.1 (2019-11); Speech and Multimedia Transmission Quality (STQ); QoS Aspects for Popular Services in
Mobile Networks; Part 3: Typical Procedures for Quality of Service Measurement Equipment. European Telecommunications
Standards Institute (ETSI): Sophia Antipolis, France, 2019.
41.
ETSI TS 102 250-4 V2.3.1 (2019-11); Speech and Multimedia Transmission Quality (STQ); QoS Aspects for Popular Services
in Mobile Networks; Part 4: Requirements for Quality of Service Measurement Equipment. European Telecommunications
Standards Institute (ETSI): Sophia Antipolis, France, 2019.
42.
ETSI TS 102 250-5 V2.5.1 (2019-11); Speech and Multimedia Transmission Quality (STQ); QoS Aspects for Popular Services in
Mobile Networks; Part 5: Definition of Typical Measurement Profiles. European Telecommunications Standards Institute (ETSI):
Sophia Antipolis, France, 2019.
43.
Data Performance—Capacity and iPerf. In Test Description. Release 22.2; Rohde & Schwarz SwissQual AG: Zuchwil, Switzerland,
2022.
44.
Data Performance—Interactivity and Latency. In Test Description Release 22.2; Rohde & Schwarz SwissQual AG: Zuchwil,
Switzerland, 2022.
45. Wesley, E. Transmission Control Protocol (TCP); Internet Engineering Task Force: Fremont, CA, USA, 2022.
46. User Datagram Protocol (UDP); Internet Engineering Task Force: Fremont, CA, USA, 1980.
47.
Afroz, F.; Subramanian, R.; Heidary, R.; Sandrasegaran, K.; Ahmed, S. SINR, RSRP, RSSI and RSRQ Measurements in Long Term
Evolution Networks. Int. J. Wirel. Mob. Netw. 2015,7, 113–123. [CrossRef]
48.
ETSI TS 136 214 V16.1.0 (2020-07); LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Layer; Measurements
(3GPP TS 36.214 Version 16.1.0 Release 16). European Telecommunications Standards Institute (ETSI): Sophia Antipolis, France,
2020.
49.
ETSI TS 136 133 V17.9.0 (2023-05); LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Requirements for Support of Radio
Resource Management (3GPP TS 36.133 Version 17.9.0 Release 17). European Telecommunications Standards Institute (ETSI):
Sophia Antipolis, France, 2023.
50.
ETSI TS 138 215 V16.2.0 (2020-07); 5G; NR; Physical Layer Measurements (3GPP TS 38.215 Version 16.2.0 Release 16). European
Telecommunications Standards Institute (ETSI): Sophia Antipolis, France, 2020.
51.
ETSI TS 138 214 V16.5.0 (2021-04); 5G; NR; Physical Layer Procedures for Data (3GPP TS 38.214 Version 16.5.0 Release 16).
European Telecommunications Standards Institute (ETSI): Sophia Antipolis, France, 2021.
52.
5G NR RSRQ Measurements and Its Mapping. Techplayon. 2021. Available online: https://www.techplayon.com/5g-nr-rsrq-
measurements-and-its-mapping/ (accessed on 3 January 2024).
53.
Synchronization Signal Reference Signal Received Power. Available online: http://comtech.vsb.cz/qualmob/rsrp_5g.html
(accessed on 2 December 2023).
54. Campbell, M.J. Statistics at Square One, 12th ed.; Wiley-Blackwell: Hoboken, NJ, USA, 2021; ISBN 978-1-119-40130-8.
55.
Information about Spectrum Occupancy in Bands 420 MHz, 450 MHz, 800 MHz, 900 MHz, 1800 MHz, 2100 MHz, 2600 MHz,
3600 MHz (In Polish). Available online: https://bip.uke.gov.pl/dostepnosc-czestotliwosci/informacja-o-zajetosci-widma-tresci/
informacja-o-zajetosci-widma-w-pasmach-420-mhz-450-mhz-800-mhz-900-mhz-1800-mhz-2100-mhz-2600-mhz-3600-mhz,
13,0.html (accessed on 23 January 2024).
56.
Poland 5G—NR, 4G—LTE Frequency Spectrum Bands, 3G—WCDMA, 2G—GSM, FDD, TDD, FR1, FR2 mmWave Mobile
Networks. Available online: https://www.spectrum-tracker.com/Poland (accessed on 23 January 2024).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.