Pub Date : 2018-06-01DOI: 10.23919/TMA.2018.8506531
D. Leng, Mattias Tiger, Mathias Almquist, Viktor Almquist, Niklas Carlsson
With Twitter and other microblogging services, users can easily express their opinion and ideas in short text messages. A recent trend is that users use the real-time property of these services to share their opinions and thoughts as events unfold on TV or in the real world. In the context of TV broadcasts, Twitter (over a mobile device, for example) is referred to as a second screen. This paper presents the first characterization of the second screen usage over the playoffs of a major sports league. We present both temporal and spatial analysis of the Twitter usage during the end of the National Hockey League (NHL) regular season and the 2015 Stanley Cup playoffs. Our analysis provides insights into the usage patterns over the full 72-day period and with regards to in-game events such as goals, but also with regards to geographic biases. Quantifying these biases and the significance of specific events, we then discuss and provide insights into how the playoff dynamics may impact advertisers and third-party developers that try to provide increased personalization.
{"title":"A Second Screen Journey to the Cup: Twitter Dynamics During the Stanley Cup Playoffs","authors":"D. Leng, Mattias Tiger, Mathias Almquist, Viktor Almquist, Niklas Carlsson","doi":"10.23919/TMA.2018.8506531","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506531","url":null,"abstract":"With Twitter and other microblogging services, users can easily express their opinion and ideas in short text messages. A recent trend is that users use the real-time property of these services to share their opinions and thoughts as events unfold on TV or in the real world. In the context of TV broadcasts, Twitter (over a mobile device, for example) is referred to as a second screen. This paper presents the first characterization of the second screen usage over the playoffs of a major sports league. We present both temporal and spatial analysis of the Twitter usage during the end of the National Hockey League (NHL) regular season and the 2015 Stanley Cup playoffs. Our analysis provides insights into the usage patterns over the full 72-day period and with regards to in-game events such as goals, but also with regards to geographic biases. Quantifying these biases and the significance of specific events, we then discuss and provide insights into how the playoff dynamics may impact advertisers and third-party developers that try to provide increased personalization.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"45 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76184609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.23919/TMA.2018.8506509
Diego Madariaga, Martín Panza, Javier Bustos-Jiménez
Global increase in the use of mobile Internet service generates interest in mobile network studies to determine and forecast the QoS provided by mobile operators. This study proposes different methods to forecast signal strength, one of the most important mobile Internet QoS indicator, based on time series analysis and considering external information about weather conditions as temperature, humidity and precipitations due to the effect they cause on mobile Internet QoS. This work shows the feasibility of forecasting mobile signal strength using crowd data corresponding to mobile devices in Santiago, Chile and that the inclusion of weather information generates more accurate forecast models for a given geographic area, obtaining good performance by all models used at comparing their forecast error values for weekly predictions. To the best of the authors' knowledge this is the first attempt of using weather information together with real data gathered from user devices in order to forecast mobile signal strength.
{"title":"I'm Only Unhappy when it Rains: Forecasting Mobile QoS with Weather Conditions","authors":"Diego Madariaga, Martín Panza, Javier Bustos-Jiménez","doi":"10.23919/TMA.2018.8506509","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506509","url":null,"abstract":"Global increase in the use of mobile Internet service generates interest in mobile network studies to determine and forecast the QoS provided by mobile operators. This study proposes different methods to forecast signal strength, one of the most important mobile Internet QoS indicator, based on time series analysis and considering external information about weather conditions as temperature, humidity and precipitations due to the effect they cause on mobile Internet QoS. This work shows the feasibility of forecasting mobile signal strength using crowd data corresponding to mobile devices in Santiago, Chile and that the inclusion of weather information generates more accurate forecast models for a given geographic area, obtaining good performance by all models used at comparing their forecast error values for weekly predictions. To the best of the authors' knowledge this is the first attempt of using weather information together with real data gathered from user devices in order to forecast mobile signal strength.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"50 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86749953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.23919/TMA.2018.8506570
Sarah Wassermann, John P. Rula, F. Bustamante, P. Casas
The appeal and clear operational and economic benefits of anycast to service providers have motivated a number of recent experimental studies on its potential performance impact for end users. For CDNs on mobile networks, in particular, anycast provides a simpler alternative to existing routing systems challenged by a growing, complex, and commonly opaque cellular infrastructure. This paper presents the first analysis of anycast performance for mobile users. In particular, our evaluation focuses on two distinct anycast services, both providing part of the DNS Root zone and together covering all major geographical regions. Our results show that mobile clients tend to be routed to suboptimal replicas in terms of geographical distance, more frequently while on a cellular connection than on WiFi, with a significant impact on latency. We find that this is not simply an issue of lacking better alternatives, and that the problem is not specific to particular geographic areas or autonomous systems. We close with a first analysis of the root causes of this phenomenon and describe some of the major classes of anycast anomalies revealed during our study, additionally including a systematic approach to automatically detect such anomalies without any sort of training or annotated measurements. We release our datasets to the networking community.
{"title":"Anycaston the Move: A Look at Mobile Anycast Performance","authors":"Sarah Wassermann, John P. Rula, F. Bustamante, P. Casas","doi":"10.23919/TMA.2018.8506570","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506570","url":null,"abstract":"The appeal and clear operational and economic benefits of anycast to service providers have motivated a number of recent experimental studies on its potential performance impact for end users. For CDNs on mobile networks, in particular, anycast provides a simpler alternative to existing routing systems challenged by a growing, complex, and commonly opaque cellular infrastructure. This paper presents the first analysis of anycast performance for mobile users. In particular, our evaluation focuses on two distinct anycast services, both providing part of the DNS Root zone and together covering all major geographical regions. Our results show that mobile clients tend to be routed to suboptimal replicas in terms of geographical distance, more frequently while on a cellular connection than on WiFi, with a significant impact on latency. We find that this is not simply an issue of lacking better alternatives, and that the problem is not specific to particular geographic areas or autonomous systems. We close with a first analysis of the root causes of this phenomenon and describe some of the major classes of anycast anomalies revealed during our study, additionally including a systematic approach to automatically detect such anomalies without any sort of training or annotated measurements. We release our datasets to the networking community.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"116 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75006912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.23919/TMA.2018.8506551
F. Abdesslem, H. Abrahamsson, B. Ahlgren
Cooperative Intelligent Transport Systems (C-ITS) make road traffic safer and more efficient, but require the mobile networks to handle time-critical applications. While some applications may need new dedicated communications technologies such as IEEE 802.11p or 5G, other applications can use current cellular networks. This study evaluates the performance that connected vehicles can expect from existing networks, and estimates the potential gain of multi-access by simultaneously transmitting over several operators. We upload time-critical warning messages from buses in Sweden, and characterise transaction times and network availability. We conduct the experiments with different protocols: UDP, TCP, and HTTPS. Our results show that when using UDP, the median transaction time for sending a typical warning message is 135 ms. We also show that multi-access can bring this value down to 73 ms. For time-critical applications requiring transaction times under 200 ms, multi-access can increase availability of the network from to 57.4% to 92.0%.
{"title":"Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications","authors":"F. Abdesslem, H. Abrahamsson, B. Ahlgren","doi":"10.23919/TMA.2018.8506551","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506551","url":null,"abstract":"Cooperative Intelligent Transport Systems (C-ITS) make road traffic safer and more efficient, but require the mobile networks to handle time-critical applications. While some applications may need new dedicated communications technologies such as IEEE 802.11p or 5G, other applications can use current cellular networks. This study evaluates the performance that connected vehicles can expect from existing networks, and estimates the potential gain of multi-access by simultaneously transmitting over several operators. We upload time-critical warning messages from buses in Sweden, and characterise transaction times and network availability. We conduct the experiments with different protocols: UDP, TCP, and HTTPS. Our results show that when using UDP, the median transaction time for sending a typical warning message is 135 ms. We also show that multi-access can bring this value down to 73 ms. For time-critical applications requiring transaction times under 200 ms, multi-access can increase availability of the network from to 57.4% to 92.0%.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"24 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85260748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.23919/TMA.2018.8506487
C. Jarvis, Cise Midoglu, Andra Lutu, Özgü Alay
Ensuring pervasive coverage of mobile networks and good quality of service are common goals for both regulators and operators. Currently, however, the evaluation of coverage is mostly limited to maps provided by Mobile Network Operators (MNOs). In this paper, we use the Measuring Mobile Broadband Networks in Europe (MONROE) platform to characterize mobile coverage along transport routes, reliably and in an objective manner. We leverage access to MONROE nodes onboard public transport vehicles: our unique geo-referenced dataset comes from nodes active on board 15 Norwegian inter-city trains that travel 13 different routes. The data from hundreds of train trips between 2017 and 2018 on each of the routes shows the mobile coverage status as travellers experience it. We propose an algorithm to segment the measurement routes to enable efficient grouping of data samples for analysis and visualization. We present our analysis and visualization of coverage along the railway routes. The proposed approach is generic so that other type of performance maps, including latency or throughput maps, can also be generated.
{"title":"Visualizing Mobile Coverage from Repetitive Measurements on Defined Trajectories","authors":"C. Jarvis, Cise Midoglu, Andra Lutu, Özgü Alay","doi":"10.23919/TMA.2018.8506487","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506487","url":null,"abstract":"Ensuring pervasive coverage of mobile networks and good quality of service are common goals for both regulators and operators. Currently, however, the evaluation of coverage is mostly limited to maps provided by Mobile Network Operators (MNOs). In this paper, we use the Measuring Mobile Broadband Networks in Europe (MONROE) platform to characterize mobile coverage along transport routes, reliably and in an objective manner. We leverage access to MONROE nodes onboard public transport vehicles: our unique geo-referenced dataset comes from nodes active on board 15 Norwegian inter-city trains that travel 13 different routes. The data from hundreds of train trips between 2017 and 2018 on each of the routes shows the mobile coverage status as travellers experience it. We propose an algorithm to segment the measurement routes to enable efficient grouping of data samples for analysis and visualization. We present our analysis and visualization of coverage along the railway routes. The proposed approach is generic so that other type of performance maps, including latency or throughput maps, can also be generated.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"30 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89583970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.23919/TMA.2018.8506545
Rapahel Bronfman-Nadas, A. N. Zincir-Heywood, John T. Jacobs
On the Internet today, mobile malware is one of the most common attack methods. These attacks are usually established via malicious mobile apps. To combat this threat, one technique used is the deployment of mobile malware detectors. As the mobile threats evolve, designing and developing mobile malware detectors remains a challenging task. In this paper, we aim to explore whether creating an artificial arms race between mobile malware and detectors could improve the ability of the detector to adapt to the evolving threats. To better model this interaction, we present a co-evolution of both sides of the arms race using genetic algorithms. The experimental evaluations on publicly available malicious and non-malicious mobile apps and their variants generated by the artificial arms race show that this approach improves the detectors understanding of the problem.
{"title":"An Artificial Arms Race: Could it Improve Mobile Malware Detectors?","authors":"Rapahel Bronfman-Nadas, A. N. Zincir-Heywood, John T. Jacobs","doi":"10.23919/TMA.2018.8506545","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506545","url":null,"abstract":"On the Internet today, mobile malware is one of the most common attack methods. These attacks are usually established via malicious mobile apps. To combat this threat, one technique used is the deployment of mobile malware detectors. As the mobile threats evolve, designing and developing mobile malware detectors remains a challenging task. In this paper, we aim to explore whether creating an artificial arms race between mobile malware and detectors could improve the ability of the detector to adapt to the evolving threats. To better model this interaction, we present a co-evolution of both sides of the arms race using genetic algorithms. The experimental evaluations on publicly available malicious and non-malicious mobile apps and their variants generated by the artificial arms race show that this approach improves the detectors understanding of the problem.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"14 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88680693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.23919/TMA.2018.8506519
Tarun Mangla, Emir Halepovic, M. Ammar, E. Zegura
Understanding the user-perceived Quality of Experience (QoE) of HTTP-based video has become critical for content providers, distributors, and network operators. For network operators, monitoring QoE is challenging due to lack of access to video streaming applications, user devices, or servers. Thus, network operators need to rely on the network traffic to infer key metrics that influence video QoE. Furthermore, with content providers increasingly encrypting the network traffic, the task of QoE inference from passive measurements has become even more challenging. In this paper, we present a methodology called eMIMIC that uses passive network measurements to estimate key video QoE metrics for encrypted HTTP-based Adaptive Streaming (HAS) sessions. eMIMIC uses packet headers from network traffic to model a HAS session and estimate video QoE metrics such as average bitrate and re-buffering ratio. We evaluate our methodology using network traces from a variety of realistic conditions and ground truth of two popular video streaming services collected using a lab testbed. eMIMIC estimates re-buffering ratio within 1 percentage point of ground truth for up to 70% sessions and average bitrate with error under 100 kbps for up to 80% sessions. We also compare eMIMIC with recently proposed machine learning-based QoE estimation methodology. We show that eMIMIC can predict average bitrate with 2.8%-3.2% higher accuracy and re-buffering ratio with 9.8%-24.8% higher accuracy without requiring any training on ground truth QoE metrics.
{"title":"eMIMIC: Estimating HTTP-Based Video QoE Metrics from Encrypted Network Traffic","authors":"Tarun Mangla, Emir Halepovic, M. Ammar, E. Zegura","doi":"10.23919/TMA.2018.8506519","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506519","url":null,"abstract":"Understanding the user-perceived Quality of Experience (QoE) of HTTP-based video has become critical for content providers, distributors, and network operators. For network operators, monitoring QoE is challenging due to lack of access to video streaming applications, user devices, or servers. Thus, network operators need to rely on the network traffic to infer key metrics that influence video QoE. Furthermore, with content providers increasingly encrypting the network traffic, the task of QoE inference from passive measurements has become even more challenging. In this paper, we present a methodology called eMIMIC that uses passive network measurements to estimate key video QoE metrics for encrypted HTTP-based Adaptive Streaming (HAS) sessions. eMIMIC uses packet headers from network traffic to model a HAS session and estimate video QoE metrics such as average bitrate and re-buffering ratio. We evaluate our methodology using network traces from a variety of realistic conditions and ground truth of two popular video streaming services collected using a lab testbed. eMIMIC estimates re-buffering ratio within 1 percentage point of ground truth for up to 70% sessions and average bitrate with error under 100 kbps for up to 80% sessions. We also compare eMIMIC with recently proposed machine learning-based QoE estimation methodology. We show that eMIMIC can predict average bitrate with 2.8%-3.2% higher accuracy and re-buffering ratio with 9.8%-24.8% higher accuracy without requiring any training on ground truth QoE metrics.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"82 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77652125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.23919/TMA.2018.8506528
Florian Wamser, Nikolas Wehner, Michael Seufert, P. Casas, P. Tran-Gia
In recent years, Quality of Experience (QoE) measurements have become an important means of determining customer satisfaction for a service on the Internet. With the tool YoMoApp exists an application that can determine the QoE for the most important video portal, namely YouTube. In this paper, we demonstrate and introduce the YoMoApp Dashboard, a web-based data interface for researchers. The dashboard allows the user to view and download his data as well as to display simple statistics. Researchers can independently conduct reliable QoE measurements and use and interpret the results in their own way.
{"title":"You Tube QoE Monitoring with YoMoApp: A Web-Based Data Interface for Researchers","authors":"Florian Wamser, Nikolas Wehner, Michael Seufert, P. Casas, P. Tran-Gia","doi":"10.23919/TMA.2018.8506528","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506528","url":null,"abstract":"In recent years, Quality of Experience (QoE) measurements have become an important means of determining customer satisfaction for a service on the Internet. With the tool YoMoApp exists an application that can determine the QoE for the most important video portal, namely YouTube. In this paper, we demonstrate and introduce the YoMoApp Dashboard, a web-based data interface for researchers. The dashboard allows the user to view and download his data as well as to display simple statistics. Researchers can independently conduct reliable QoE measurements and use and interpret the results in their own way.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"77 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78055811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.23919/TMA.2018.8506559
H. Abrahamsson, F. Abdesslem, B. Ahlgren, A. Brunström, I. Marsh, M. Björkman
Connected vehicles can make roads traffic safer and more efficient, but require the mobile networks to handle time-critical applications. Using the MONROE mobile broadband measurement testbed we conduct a multi-access measurement study on buses. The objective is to understand what network performance connected vehicles can expect in today's mobile networks, in terms of transaction times and availability. The goal is also to understand to what extent access to several operators in parallel can improve communication performance. In our measurement experiments we repeatedly transfer warning messages from moving buses to a stationary server. We triplicate the messages and always perform three transactions in parallel over three different cellular operators. This creates a dataset with which we can compare the operators in an objective way and with which we can study the potential for multi-access. In this paper we use the triple-access dataset to evaluate single-access selection strategies, where one operator is chosen for each transaction. We show that if we have access to three operators and for each transaction choose the operator with best access technology and best signal quality then we can significantly improve availability and transaction times compared to the individual operators. The median transaction time improves with 6% compared to the best single operator and with 61% compared to the worst single operator. The 90-percentile transaction time improves with 23% compared to the best single operator and with 65% compared to the worst single operator.
{"title":"Connected Vehicles in Cellular Networks: Multi-Access Versus Single-Access Performance","authors":"H. Abrahamsson, F. Abdesslem, B. Ahlgren, A. Brunström, I. Marsh, M. Björkman","doi":"10.23919/TMA.2018.8506559","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506559","url":null,"abstract":"Connected vehicles can make roads traffic safer and more efficient, but require the mobile networks to handle time-critical applications. Using the MONROE mobile broadband measurement testbed we conduct a multi-access measurement study on buses. The objective is to understand what network performance connected vehicles can expect in today's mobile networks, in terms of transaction times and availability. The goal is also to understand to what extent access to several operators in parallel can improve communication performance. In our measurement experiments we repeatedly transfer warning messages from moving buses to a stationary server. We triplicate the messages and always perform three transactions in parallel over three different cellular operators. This creates a dataset with which we can compare the operators in an objective way and with which we can study the potential for multi-access. In this paper we use the triple-access dataset to evaluate single-access selection strategies, where one operator is chosen for each transaction. We show that if we have access to three operators and for each transaction choose the operator with best access technology and best signal quality then we can significantly improve availability and transaction times compared to the individual operators. The median transaction time improves with 6% compared to the best single operator and with 61% compared to the worst single operator. The 90-percentile transaction time improves with 23% compared to the best single operator and with 65% compared to the worst single operator.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"5 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88601399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.23919/TMA.2018.8506494
Vaclav Raida, M. Lerch, P. Svoboda, M. Rupp
The performance of wireless systems is often interference-limited. In LTE, the parameter RSRQ is connected to the system interference. A solid and sound measurement of this parameter allows for an estimation of the current level of cell load as well as interference in the current cell, enabling us to use crowdsourced performance data for network benchmarking. However, RSRQ is not straightforward to interpret. We point out that RSRQ can be used to estimate the cell load caused by other users if it is measured at zero downlink throughput of the measuring device. In such a case we expect a positive correlation between RSRQ and achievable throughput which we confirm by measurements in a live LTE network. Conversely, we show that if the measuring device is downloading data, a wide range of different RSRQ values can be generated. As an extreme case we present measurements with strong negative correlation between RSRQ and throughput. The source codes of the network monitoring applications are often proprietary, we thus do not know if RSRQ samples are a) collected at zero downlink throughputs, b) during a downlink throughput test or c) a combination of both. In case a) RSRQ provides us precious additional knowledge about the cell load. In cases b) and c) it is merely useless if we cannot filter out the samples corresponding to nonzero downlink throughput.
{"title":"Deriving Cell Load from RSRQ Measurements","authors":"Vaclav Raida, M. Lerch, P. Svoboda, M. Rupp","doi":"10.23919/TMA.2018.8506494","DOIUrl":"https://doi.org/10.23919/TMA.2018.8506494","url":null,"abstract":"The performance of wireless systems is often interference-limited. In LTE, the parameter RSRQ is connected to the system interference. A solid and sound measurement of this parameter allows for an estimation of the current level of cell load as well as interference in the current cell, enabling us to use crowdsourced performance data for network benchmarking. However, RSRQ is not straightforward to interpret. We point out that RSRQ can be used to estimate the cell load caused by other users if it is measured at zero downlink throughput of the measuring device. In such a case we expect a positive correlation between RSRQ and achievable throughput which we confirm by measurements in a live LTE network. Conversely, we show that if the measuring device is downloading data, a wide range of different RSRQ values can be generated. As an extreme case we present measurements with strong negative correlation between RSRQ and throughput. The source codes of the network monitoring applications are often proprietary, we thus do not know if RSRQ samples are a) collected at zero downlink throughputs, b) during a downlink throughput test or c) a combination of both. In case a) RSRQ provides us precious additional knowledge about the cell load. In cases b) and c) it is merely useless if we cannot filter out the samples corresponding to nonzero downlink throughput.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":"50 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85288977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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