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Umoh, V.; Ekpe, U.; Davidson, I.; Akpan, J. Existing Mobile Broadband Performance Measurements. Encyclopedia. Available online: https://encyclopedia.pub/entry/42854 (accessed on 27 July 2024).
Umoh V, Ekpe U, Davidson I, Akpan J. Existing Mobile Broadband Performance Measurements. Encyclopedia. Available at: https://encyclopedia.pub/entry/42854. Accessed July 27, 2024.
Umoh, Vincent, Unwana Ekpe, Innocent Davidson, Joseph Akpan. "Existing Mobile Broadband Performance Measurements" Encyclopedia, https://encyclopedia.pub/entry/42854 (accessed July 27, 2024).
Umoh, V., Ekpe, U., Davidson, I., & Akpan, J. (2023, April 06). Existing Mobile Broadband Performance Measurements. In Encyclopedia. https://encyclopedia.pub/entry/42854
Umoh, Vincent, et al. "Existing Mobile Broadband Performance Measurements." Encyclopedia. Web. 06 April, 2023.
Existing Mobile Broadband Performance Measurements
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This entry is adapted from the peer-reviewed paper 10.3390/electronics12071630

Globally, mobile broadband (MBB) penetration has increased due to the widespread use of smart devices, better mobile network coverage, and the ongoing quest for faster wireless and mobile communication technology. This has led to a tremendous rise in the number of internet subscribers, which is positively impacting the social and economic well-being of society at large. Terrestrial mobile network operators (MNOs) are responsible for providing MBB services to end users, but some of them do not offer the advertised speeds or theoretical speeds specified by 3GPP specifications. Therefore, periodic impartial and unbiased performance measurement studies of the quality of service (QoS) provided by the MNOs as perceived by the end users are required to help MNOs enhance the capabilities of their MBB networks and provide services at an acceptable quality.

measurement methodology mobile broadband

1. User-Centric Performance Evaluation Works

The research objective of most MBB studies is conducted to comparatively evaluate the MBB performance delivered by different MNOs. The research reported in [1][2] adopted a host and crowdsourced based approached using MBperf as the mobile application to measure the performance of 2G and 3G MBB networks, while [3] used a simplified Raspberry Pi testbed for measurement of the performance of 3G and 4G MBB networks over an extended period. The results of this research carried out in Nigeria reveal variations in MBB speeds delivered by four major MNOs in the country. Similarly, studies reported in [4][5][6] also used a panel-based crowdsourced approach for a comparative assessment of 3G and 4G MBB networks in Nepal, Pakistan and South Africa, respectively. They identified that the MBB speeds delivered to end users do not meet the values advertised by the MNOs. The behavior of these crowdsourced MBB measurement datasets can be analyzed using machine learning for more accurate estimations [7][8].
Apart from comparative analysis, other types of MBB performance evaluation have been carried out. For instance, [9] used a panel-based crowdsourced method for performance assessment of MBB services offered by different Internet service providers during defined peak periods and off-peak periods in major Canadian metropolitan areas. They defined peak periods as the time between 7 pm and 11 pm from Monday to Friday and off-peak periods as any hours or days exclusive of peak periods. Additionally, [10][11][12] used a dedicated testbed and drive tests approach to study the performance of different MBB networks under mobility, while [13][14] adopted the walk test methodology to perform coverage and capacity measurement and characterize the performance of MBB networks during peak periods and off-peak period.
Furthermore, when designing future technologies, MBB measurement can be valuable for benchmarking and planning network upgrades. The MBB performance measurement of the 4G networks reported in [12][15] are studies conducted to determine the baseline for 5G capabilities and assess the inefficiencies that should be addressed in the 5G network. Some of the points highlighted and the benchmarks estimated were considered in the 5G pilot MBB measurement reported in [16][17][18][19].

2. Testbed-Based Measurement Projects

The limitations posed by using the aforementioned methodologies have driven institutions and private researchers to develop more robust infrastructure for testbed-based experiments on MBB performance. Although some of these testbeds are expensive to build, they allow for a controlled and scalable measurement over a long period and thus, eliminate many limitations of the other methodologies. This section introduces some testbed-based MBB performance evaluation platforms and projects that already exist. It goes further to explain the network tools used for these testbeds.

2.1. The Nornet Edge (NNE) Platform

The Nornet Edge (NNE) platform is a testbed dedicated to the measurement and study of MBB networks and is presented in [20][21][22]. Figure 1 shows the overview of the testbed for MBB experiments. Renowned as one of the largest infrastructures in the world for MBB measurements, the NNE has over 400 fully programmable and multi-homed nodes shown in Figure 2, placed at different locations in Norway. The NNE measurement nodes comprise custom-made single-board computers running a standard Linux operating system that allow 2–5 MNOs to be connected to it using MBB modems. The node is equipped with a Samsung S5PV210 Cortex A8 1 GHz processor with 512 MB RAM, 512 MB NAND flash memory and a 16 GB SD card for storage. Sets of servers form a central backend system for collection and storing data on the NNE platform. There is also an algorithm designed to manage the nodes and run measurements for a long time on a national scale. The platform allows for the collection of status information from the modems on mobile broadband cell ID, connection mode and signal strength.
Figure 1. The overall system architecture of the NNE platform [20].
Figure 2. NNE node with 4 modems connected [20].
Since the platform is able to simultaneously connect to multiple networks, it is possible to directly compare QoS metrics across different MNOs. The NNE platform is built for future compatibility with new systems as its design makes it seamless to install new measurement applications to gather new or additional data. A website is also created for real-time viewing of the status of all NNE nodes, including the status of each MBB connection.
NNE is well suited for national scale measurements and experiments that require a large number of geographically distributed measurement nodes, simultaneous connections to multiple operators, information regarding the context in which measurements are taken, and continuous measurements that span long. One such experiment and research is reported in [10].

2.2. The MONROE Platform

The MONROE testbed and its operation presented in [23][24][25][26] is the first open access European transnational hardware-based platform for independent, multihomed, large-scale experimentation in MBB measurements. Figure 3 shows the overview of the MONROE MBB performance evaluation platform. MONROE has a set of 150 nodes, both mobile and stationary, which are multihomed to 5 different MNOs with the aid of commercial grade subscriptions across numerous European countries. The MONROE MBB measurement node shown in Figure 3 is based on Debian GNU/Linux “stretch” distribution integrating two small programmable computers. The computers are made of PC engines APU2 board interfacing with three 3G/4G MC7455 miniPCI express modems using LTE CAT6 and one WiFi modem. Each of the nodes gathers metadata such as carrier, technology, signal strength, GPS location and sensor data from the different modems. MONROE runs its MBB experiments using Docker containers (lightweight virtualized environment) to provide agile reconfiguration. Only users who are authenticated can access resources on the platform through a web portal, and also have access to the MONROE scheduler to deploy experiments. After each experiment on the MONROE platform, the results are periodically transferred from the nodes to a repository at a back-end server, while the MONROE scheduler also sets data quotas to ensure fairness among users. Some of the vast experiments run with the MONROE testbed have been reported in [27][28][29].
Figure 3. Overview of the MONROE platform.
Three vital features of MONROE make the platform unique. It allows measurements to be repeated and controlled for precise and scientifically verifiable results for both fixed and mobile scenarios, enables support for demanding applications such as web and video services and supports protocol and service innovation.

2.3. The Simplified Raspberry Pi Platform

A simplified testbed for MBB performance evaluation that follows the setup of the NNE albeit using easily sourced commercial-off-the-shelf (COTS) devices is presented in [3][30]. Figure 4 shows the overall system architecture with the Raspberry Pi forming the core of the remote MBB measurement node. The Raspberry Pi 4 with 64 quad-core Cortex-A72 processors and 2GB Low-Power Double Data Rate (LPDDRA) RAM on its board is used for the node. The testbed uses USB modems and retrofitted WiFi to connect up to 4 MNOs for 3G and 4G MBB networks, respectively. The Raspberry Pi nodes are configured with the 4-way 5V relay modules mounted and an executable script written in python to achieve multihoming for 3G and 4G MBB measurements. The node autonomously initiates the measurement at regular intervals and stores the information, which an authorized user can access remotely at the testbed core for evaluation. This simplified MBB testbed is not as sophisticated as NNE; however, it can measure the key MBB performance metrics over an extended period.
Figure 4. Overview of the Simplified Raspberry Pi platform [30].
The aforementioned testbeds have been dedicated mostly to 3G and 4G MBB experiments, albeit allowing compatibility with future mobile communication networks like the recently deployed 5G network. To the best of the knowledge, there is no dedicated testbed to assess the QoS delivered to end users on the 5G MBB network from a user-centric perspective. However, as part of the 5G Public Private Partnership (5G-PPP) initiative, the EU funded 5GENESIS [31] project has been developed as a flexible and open experimentation testbed for validating the end-to-end key performance indicators (KPIs) of 5G networks. The 5GENESIS architecture is designed to provide an integrated and open experimentation framework that facilitates interactions between the experimenters and the testing facilities. A detailed description of the experimentation suite is presented in [32], while pilot 5G experiments using the testbed have been reported in [33][34].
Furthermore, there are other testbed federations such as Fed4FIRE+ [35][36] and 5TONIC [37], developed to carry out experiments on numerous aspects of 4G and 5G. Fed4FIRE+ was the largest federation of internet testbeds in Europe consisting of 23 testbeds equipped with numerous user-friendly tools that enabled remote testing in different areas of interest. The Fed4FIRE+ project, which was a successor to the Fed4FIRE project, came to an end in June 2022 and its legacy will be taken by Scientific LargeScale Infrastructure for Computing/Communication Experimental Studies. (SLICES-RI) [35][36]. 5TONIC is an open research and innovation laboratory developed to create an open global environment for industry experts and members of academia to work together on specific projects that focus on 5G technologies [37]. Some studies that utilized the 5TONIC platform have been reported in [38][39].
Table 1 presents a summary of extensively reviewed user-centric MBB performance evaluation studies, highlighting the method adopted, the QoS metrics considered, the type of access network and a summary of each study.
Table 2 compares the different MBB performance measurement methods already discussed.
Table 1. Summary of existing mobile broadband performance evaluation works.
S/N Refs. Methodology Adopted QoS Metrics Access Networks Study Summary
1 [1][2] Crowdsourced Download and upload throughput, latency and DNS lookup 2G and 3G Developed a mobile phone application (MBPerf) and adopted a host and crowdsourced based approach to carry out a comparative analysis of the performance of four MNOs that offer MBB services in a developing country.
2 [3][30] Testbed Download and upload throughput and latency 3G and 4G Used Raspberry Pi to develop a simplified testbed and conducted a comparative analysis of the MBB performance offered by four MNOs.
3 [9] Crowdsourced Download and upload speed, latency, packet loss and web loading time 4G Conducted MBB performance assessment during defined peak periods for Internet service providers using a panel-based crowdsourced method.
4 [4][5] Crowdsourced Download and upload throughput, latency, packet loss, jitter and DNS resolution time 4G Provides a comparative investigation of MBB performance using a panel-based crowdsourced method.
5 [6] Crowdsourced Upload and download throughput and latency 4G Presented a comprehensive comparative study of user-centric MBB performance using a panel-based crowdsourced method.
6 [10] Testbed Latency, packet loss and connectivity 3G and 4G Studied the performance of MBB networks under mobility using a dedicated testbed for measurement
7 [40] Drive test Signal quality, downlink and uplink throughput, ping and handover 3G and 4G Developed and used a drive test method to evaluate and understand MBB performance in different locations
8 [41] Testbed Download speed 3G and 4G Presented a “speedtest like” measurement to estimate the download speed offered by MBB networks to users.
9 [42] Drive test Speed, coverage, satisfaction and latency 3G and 4G Conducted performance analysis of MBB networks to enable planning for 5G network upgrade in a rural area
10 [43] Testbed Web QoE, throughput, latency, and signal coverage 4G Examined the performance and response of nine mobile networks across Europe at different times during the COVID-19 pandemic
11 [44] Drive test Speed, coverage, satisfaction and latency 3G and 4G Conducted performance analysis of MBB networks to enable planning of 5G network upgrade urban area
12 [13] Walk test Received signal strength 3G and 4G Measured and characterized MBB performance through an indoor walk test
13 [45] Drive test Signal quality, Downlink and uplink throughput and Ping 3G and 4G Evaluated the MBB performance and coverage of existing MBB networks of different MNOs.
14 [46] Drive test Throughput and latency 3G and 4G Measured real characteristics and coverage of MBB networks using a custom in-house made software tool.
15 [47] Testbed Latency and signal quality 4G Adapted data from extensive MBB measurement campaigns to develop a model suitable for realistic performance evaluation of applications and services.
16 [48] Drive test Signal strength 2G, 3G and 4G Determined and compared the signal strength of MBB networks of two MNOs.
17 [49] Drive test Throughput 2G, 3G and 4G Measured the performance of MBB networks using a custom mobile phone application.
18 [11] Drive test Throughput and latency 3G and 4G Presented a comparative analysis of real MBB networks under mobility with expected theoretical expectations in order to identify the gaps between both.
19 [50] Crowdsourced Throughput rates 4G Examined user perceived data rate fluctuations in 4G networks during different periods as well as compared the performance of MNOs
20 [14] Drive test and walk test Throughput and RTT 3G and 4G Performed coverage and capacity measurement of MBB networks in pedestrian zones during both busy hour and non-busy hour
21 [51] Drive test Signal quality and downlink and uplink throughput 4G Conducted performance analysis of 4G MBB networks and observed the propagation measurement of key performance indicators using drive test
22 [7] Crowdsourced and testbed Throughput 4G Proposed a supervise machine learning solution for a more accurate throughput estimation.
23 [8] Crowdsourced Download and upload data rate, latency, signal strength 4G Developed a Machine Learning (ML) based framework used to define and determine different behavior of MNOs from crowdsourced datasets.
24 [52] Crowdsourced Throughput, latency and DNS lookup 4G Conducted a longitudinal and multidimensional analysis of the extensive MBB measurement data collected to diagnose the cause behind observed performance variations.
25 [53] Testbed Latency 3G Examined delay characteristics in 3G networks from long-term MBB measurement data.
26 [54] Crowdsourced Latency 2G to 4G Analyzed latency of MBB networks from measurement data obtained using the crowdsourced method.
27 [55] Crowdsourced Throughput and latency 3G and 4G Used crowdsourced measurement data to study the characteristics of MBB network of three MNOs.
28 [56] Testbed Video streaming 4G Investigated the influence of different factors on YouTube streaming performance with different network configurations in four countries.
29 [57] Testbed Video streaming 4G Presented the design and implementation of a large-scale measurement tool for QoE when live streaming with MBB networks.
30 [58] Crowdsourced Throughput and latency of mobile apps 4G Extensively addressed the problem of QoE provisioning in smartphones from a double perspective, combining the results obtained from subjective laboratory tests with end-device passive MBB measurements and QoE crowd-sourced feedback obtained.
31 [16] Drive test Upload and download throughput, latency and packet loss 5G Conducted a pilot MBB measurement on the 5G network to investigate the QoS parameters of two MNOs.
32 [15] Crowdsourced Upload and download throughput, latency, jitter and packet loss 4G Performed a comparative assessment of the QoS parameters obtained from 4G MBB network and used it to establish a baseline for 5G MBB evaluation.
33 [17] Drive test Upload and download speed 5G Performed both stationary and mobility field tests to study the efficiency and performance of 5G networks.
34 [18] Drive test Upload and download speeds and latency 5G Conducted stationary field test to assess the MBB QoS performance of the 5G network using three popular mobile phone speedtest applications.
35 [59] Crowdsourced Download speed 4G Conducted a comparative study of download speeds on the 4G networks.
36 [60] Testbed Latency, jitter, packet loss, and throughput 3G Conducted a study to obtain the end-to-end parameters of the QoS for internet usage from a user perspective.
37 [12] Testbed Latency, upload throughput, handover 4G Measured the key MBB performance metrics of 4G networks under mobility, highlighting inefficiencies that need to be considered when designing the mobility features in 5G networks.
38 [61] Testbed Throughput, latency, jitter 4G and 5G Presents initial MBB measurement results of the key performance indicators on the 5G network.
39 [62] Walk Test Throughput, latency 4G and 5G Used private LTE and 5G networks to measure MBB performance metrics.
40 [19] Crowdsourced Handover, Mobile app performance 5G Performed extensive field tests of 5G network performance in different urban areas.
41 [63] Crowdsourced Throughput, latency, handover 5G Used a custom measurement tool to conduct a comprehensive measurement of numerous key aspects of commercial end-to-end 5G network performance.
42 [64] Testbed Throughput, latency, coverage 5G Conducted a full-fledged, end-to-end measurement study of the first commercial 5G networks.
43 [65] Testbed and Drive test Throughput, RTT, loss rate, signal quality 4G and 5G Performed a comparative study of the key performance metrics of 5G in extreme mobility.
44 [66] Drive test Throughput, latency, video streaming 5G Conducted an in-depth measurement study of 5G network performance in transit.
45 [67] Crowdsourced Throughput, latency 4G and 5G Performed a comparative study of 4G and 5G network deployment in two cities using a panel-based approach.
46 [33] Testbed Throughput, latency 5G Developed a modular and flexible experimentation methodology for validating 5G network KPIs.
Table 2. Comparison of the different MBB measurement methods.
Features Drive Test Walk Test Crowdsourced Testbed
Complexity Moderate Simple Moderate Complex
Accuracy Accurate Accurate (for the given purpose) Accurate (relying on users) Accurate
Scalability Moderate Low High Very High
Reliability and sensitivity Moderate Low Moderate High
Per measurement cost High High Moderate Moderate
Set up cost Moderate Low High (relative to the coverage) Very High

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Subjects: Engineering, Electrical & Electronic
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Vincent Umoh
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Unwana Ekpe
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Innocent Davidson
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Joseph Akpan
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Update Date: 07 Apr 2023
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