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How Different Parameters Affect the Downlink Speed Linköping University | IDA Bachelor Thesis | Computer and Information Science Spring 2016| LIU-IDA/LITH-EX-G—16/071--SE How Different Parameters Affect the Downlink Speed Martin Claesson Lovisa Edholm Handledare/Tutor, Niklas Carlsson Examinator, Nahid Shahmehri Abstract Today many societies rely on fast mobile networks, and the future seem to place even larger demand on the networks performance. This thesis analyzes which parameters af- fects the downlink speed of mobile networks. Various statistical analyses are performed on a large dataset provided by Bredbandskollen. We find that parameters such as the in- ternet service provider, the type of phone, the time of day and the density of population affect the downlink speed. We also find that the downlink speeds are significantly higher in urban areas compared to more rural regions. Acknowledgments Thanks to Rickard Dahlstrand at .SE for sharing the Bredbandskollen dataset, without the dataset this study would not have been possible. Thanks to our supervisor Niklas Carlsson for all the great guidance in this project. We would also like to thank our Tim Lestander and Jakob Nilsson for proof reading our thesis as well as providing us with helpful feedback during the process. iii Contents Abstract ii Acknowledgments iii Contents iv List of Figures v List of Tables vi 1 Introduction 2 1.1 Contributions . 3 1.2 Thesis outline . 4 2 Related Work 5 3 Method 6 3.1 The dataset . 6 3.2 Linear regression . 7 4 Results 9 4.1 Urban and rural areas . 9 4.2 Phones and tablets . 10 4.3 Time of day . 13 4.4 Distance from city center . 17 4.5 Density of measurements . 19 5 Discussion 21 5.1 Method . 21 5.2 Results . 22 5.3 Work in a wider context . 24 6 Conclusion 25 6.1 Possible future works . 25 Bibliography 27 iv List of Figures 1.1 Grids sized 1600x1600, 800x800, 400x400 and 200x200 meters. 3 1.2 Doughnut-shaped rings with a width of 100 meters and a radius between 100 and 2500 meters. Placed with the city center in the center of the circles . 4 4.1 Average downlink speed for different ISPs in the data set. 9 4.2 Average downlink speed for different ISPs in Stockholm, Göteborg, Malmö, Upp- sala and Linköping . 10 4.3 Average downlink speed for different mobile units for Telia .............. 11 4.4 Average downlink speed for different mobile units for Tele2 . 11 4.5 Average downlink speed for different mobile units for Telenor . 11 4.6 Total number of measurements per time of day . 13 4.7 Average downlink speed per time of day for Telia ................... 14 4.8 Average downlink speed per time of day for Tele2 ................... 14 4.9 Average downlink speed per time of day for Telenor . 14 4.10 Linear regression for Telia, with average downlink speed on the y-axis and number of measurements (interval for each hour of the day) on the x-axis.The equation for the linear regression is y = 8 10 5x + 14.394 and the value for the coefficient of ¨ ´ determination(R2) is 0.20671. 15 4.11 Linear regression for Tele2, with average downlink speed on the y-axis and num- ber of measurements (interval for each hour of the day) on the x-axis. The equation for the linear regression is y = 0.0005x + 25.089 and the value for the coefficient ´ of determination(R2) is 0.86754 . 16 4.12 Linear regression for Telenor, with average downlink speed in the y-axis and num- ber of measurements (interval for each hour of the day) in the x-axis. The equation for the linear regression is y = 0.0004x + 21.475 and the value for the coefficient ´ of determination(R2) is 0.89060 . 16 4.13 Grids sized 1600x1600, 800x800, 400x400 and 200x200 meters. 19 4.14 Linear regression with average downlink on the y-axis and number of measure- ments per region in each square on the x-axis. 20 v List of Tables 4.1 Difference in performance between devices. 12 4.2 Linear regression in grids of size 1600x1600, with average downlink speed (Mbps) in the y-axis and distance to city center (km) in the x-axis . 17 4.3 Linear regression of 25 circles of varying distance from city center, with average downlink speed (Mbps) in the y-axis and distance to city center (km) in the x-axis 18 4.4 Linear regression with average downlink speed (Mbps) on the y-axis and density of measurements per region on the x-axis. 20 vi List of Tables Students in the 5 year Information Technology program complete a semester-long soft- ware development project during their sixth semester (third year). The project is completed in mid-sized groups, and the students implement a mobile application intended to be used in a multi-actor setting, currently a search and rescue scenario. In parallel they study several topics relevant to the technical and ethical considerations in the project. The project culmi- nates by demonstrating a working product and a written report documenting the results of the practical development process including requirements elicitation. During the final stage of the semester, students form small groups and specialise in one topic, resulting in a bache- lor thesis. The current report represents the results obtained during this specialization work. Hence, the thesis should be viewed as part of a larger body of work required to pass the semester, including the conditions and requirements for a bachelor thesis. 1 1 Introduction In a disaster scenario, the need for fast and reliable network is critical. Today, almost every- one owns a battery powered device such as a laptop, a smartphone or a tablet. Since the transfer of data is very energy consuming, it is important to reduce the time spent download- ing data in order to maximize the device’s uptime. This is especially important in a disaster scenario when the access to charging points may be very limited. Smartphone usage is very diverse, and a study made by Falaki et al. [4] has shown that the average interactions made by users per day vary from from 10-200 interactions. The same study shows that the amount of data received by each user varies between 1-1000 MB. Network performance varies depending on various conditions. To understand the cur- rent network conditions, models such as performance maps are valuable. In this thesis, we analyze network capabilities by using crowd-sourced network measurements and network performance maps summarizing these measurements. We use this data to create a multi- variate model for mobile download speeds. This is done by using a large crowd sourced dataset provided by Bredbandskollen, Swe- dens primary internet test provider. By January 2016 it had been used to perform over 187 million measurements, and today about 100,000 measurements are performed using Bredbandskollen every day. This thesis focuses on the 16 million mobile non-WiFi measure- ments done between January 2014 and February 2015, using simultaneously collected meta information such as operator and geographic location we identify factors that impact the downlink speed. The locations in Sweden have been split up into different regions. In order to be able to use the techniques and softwares required for the analysis, we use aggregate measurements as well as down sample the dataset. 2 1.1. Contributions 1.1 Contributions This thesis makes two primary contributions. The first one is to identify factors of poten- tial interest that may impact internet speed. For this purpose, we characterize the mobile speed test usage of Bredbandskollen by creating a performance map that fits our needs. The measurements are diurnal (with a peak-to-valley ratio of 16) and highly concentrated to regions where most people live, with a small amount of the geographical locations being responsible for most measurements. To allow for efficient analysis we therefore focus our analysis towards the more frequent locations. The second contribution is to look at how well each of the candidate factors satisfies the assumptions, and if needed, transform variables. This is done by studying each factor in different contexts, and determine whether they satisfy the assumptions or not. Cluster Analysis To cluster the data we use three methods. First, similar to prior works analyzing this dataset [7], we divide the data into square buckets with sizes between 200x200 and 1600x1600 meters, as seen in Figure 1.1. Second, when considering geographic locality of the measurements proximity to a city center we analyze the data within doughnut-shaped rings with a width of 100 meters and a radius between 100 and 2500 meters (Figure 1.2). The third and last method is to divide the dataset within the 1600x1600 bucket (the largest bucket used in Figure 1.1) into nine equally sized squares. Since the dataset is sparse, we aggregate many measurements from a geographical area with similar characteristics, and perform analysis on this aggregated data [11]. For the purpose of our discussion, we call all measurements from such a location a group of clustered measurements. Figure 1.1: Grids sized 1600x1600, 800x800, 400x400 and 200x200 meters. 3 1.2. Thesis outline Figure 1.2: Doughnut-shaped rings with a width of 100 meters and a radius between 100 and 2500 meters. Placed with the city center in the center of the circles 1.2 Thesis outline This thesis is structured as follows. Section 2 presents related works and the theoretical back- ground for the thesis. In Section 3 the methodology used to find the results is presented, together with some explanations of the dataset that is used for the study.
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