Publications
PhD Thesis
Characterization and ML-based Modeling of Mobile Broadband Networks
In University of Oslo. Vol. Ph.D, 2021.Status: Published
Characterization and ML-based Modeling of Mobile Broadband Networks
Mobile Broadband (MBB) networks underpin several essential operations of today’s society by regulating a huge portion of the modern communications system in the world. The recent scientific advances in MBB technologies such as Fifth Generation (5G) and cellular Internet of Things (IoT) will further strengthen the MBB networks’ role, making them the norm in the global mobile telecommunications ecosystem. Given the increasing number of mobile devices and coupled with the high availability of data, it is therefore important to understand the underlying mechanisms that define the behavior of the MBB network performance in the wild. Such constructive feedback is crucial to dictate the network operators’ strategies on on-going or future deployment initiatives. In addition, businesses and application developers can benefit from the research activities by employing performance optimization actions on their services.
In this thesis, we focus on the empirical characterization and modeling of mobile systems. In particular, we are interested in capturing the interplay between numerous network performance metrics, such as bandwidth, latency, and signal strength for MBB networks in the wild. Moreover, we aim to explore the performance and coverage boundaries of the Narrowband IoT (NBIoT) radio technology standard. Toward this goal, we exploit experimental platforms to perform controlled, transparent, and replicable real-world measurements and collect a multitude of attributes and characteristics from operational mobile networks. We further complement our research activities by leveraging crowdsourced measurements, since they constitute a more realistic but rather noisier picture of the mobile network performance in the wild. For the analysis part, we design, implement, and propose supervised learning models using data- driven methods and Artificial Intelligence (AI) paradigms. The research is partitioned across nine papers, including four conference papers, four journal papers, and one demo paper. However, context-wise, it can be grouped under three main categories, i.e., (i) Characterization and data-driven modeling of MBB network performance, (ii) Advanced data analytics for bandwidth forecasting under mobility, and (iii) Dissection of Narrowband Internet of Things (NBIoT) performance in the wild.
First, we focus on the data-driven modeling aspect of Long-Term Evolution (LTE) networks. In particular, we study the impact of LTE parameters on the web application performance as well as speed test measurements. We further propose a proof of concept ML regression based framework to reduce the data volume consumption when running speed test measurements. We complement our research efforts by providing additional insights on the MBB performance leveraging a longitudinal campaign of mobile measurements spanning two countries over a two-year period.
Second, considering the increasing amount of attention on time series performance forecasting applications, such as network traffic management and application provisioning, we study the performance of Long Short Term Memory (LSTM) networks for bandwidth forecasting in MBB settings under diverse mobility. We design an open-source framework that allows experimentation with LSTM networks, including hyperparameter optimization, and we leverage it as a tool to carry out an extensive comparative analysis between 4G and 5G systems.
Last, we perform a large-scale measurement campaign to study the performance of the NBIoT technology in terms of network coverage and deployment. We further analyze the functionality and efficiency of the Random Access (RA) process and provide additional insights, including a Machine Learning (ML) based methodology to predict its outcome. To the best of our knowledge, this is the first publicly available dataset and empirical analysis of NBIoT on operational networks.
Afilliation | Communication Systems |
Project(s) | No Simula project |
Publication Type | PhD Thesis |
Year of Publication | 2021 |
Degree awarding institution | University of Oslo |
Degree | Ph.D. |
Number of Pages | 249 |
Date Published | 04/2021 |
Thesis Type | Collection of papers |
Journal Article
Large scale “speedtest” experimentation in Mobile Broadband Networks
Computer Networks 184, no. 31 (2021): 107629.Status: Published
Large scale “speedtest” experimentation in Mobile Broadband Networks
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics, MONROE: Measuring Mobile Broadband Networks in Europe |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Computer Networks |
Volume | 184 |
Issue | 31 |
Pagination | 107629 |
Date Published | Oct-17-2021 |
Publisher | Elsevier |
ISSN | 13891286 |
DOI | 10.1016/j.comnet.2020.107629 |
NB-IoT Random Access: Data-driven Analysis and ML-based Enhancements
IEEE Internet of Things Journal 8, no. 14 (2021): 11384-11399.Status: Published
NB-IoT Random Access: Data-driven Analysis and ML-based Enhancements
In the context of massive Machine Type Communications (mMTC), the Narrowband Internet of Things (NB-IoT) technology is envisioned to efficiently and reliably deal with massive device connectivity. Hence, it relies on a tailored Random Access (RA) procedure, for which theoretical and empirical analyses are needed for a better understanding and further improvements. This paper presents the first data-driven analysis of NB-IoT RA, exploiting a large scale measurement campaign. We show how the RA procedure and performance are affected by network deployment, radio coverage, and operators’ configurations, thus complementing simulation-based investigations, mostly focused on massive connectivity aspects. Comparison with the performance requirements reveals the need for procedure enhancements. Hence, we propose a Machine Learning (ML) approach, and show that RA outcomes are predictable with good accuracy by observing radio conditions. We embed the outcome prediction in a RA enhanced scheme, and show that optimized configurations enable a power consumption reduction of at least 50%. We also make our dataset available for further exploration, toward the discovery of new insights and research perspectives.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Mobile Systems and Analytics |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | IEEE Internet of Things Journal |
Volume | 8 |
Issue | 14 |
Pagination | 11384-11399 |
Date Published | 01/2021 |
Publisher | IEEE |
Keywords | Cellular Internet of Things, Empirical Analysis, massive Machine Type Communications, Narrowband Internet of Things, Random Access |
Notes | Supplementary Materials, Results, and Dataset available at https://mosaic-simulamet.com/nbiot-randomaccess/ |
URL | https://ieeexplore.ieee.org/document/9324758 |
DOI | 10.1109/JIOT.2021.3051755 |
Journal Article
Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild
IEEE Communications Magazine 58, no. 9 (2020): 39-45.Status: Published
Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild
Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising technology for massive Machine Type Communication (mMTC). Given that its deployment is rapidly progressing worldwide, measurement campaigns and performance analyses are needed to better understand the system and move toward its enhancement. With this aim, this paper presents a large scale measurement campaign and empirical analysis of NB-IoT on operational networks, and discloses valuable insights in terms of deployment strategies and radio coverage performance. The reported results also serve as examples showing the potential usage of the collected dataset, which we make open- source along with a lightweight data visualization platform.
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | IEEE Communications Magazine |
Volume | 58 |
Issue | 9 |
Pagination | 39-45 |
Date Published | 09/2020 |
Publisher | IEEE |
Notes | Preprint available here: https://arxiv.org/abs/2005.02341 |
URL | https://ieeexplore.ieee.org/document/9214385 |
DOI | 10.1109/MCOM.001.2000131 |
Proceedings, refereed
Empirical Analysis of LoRaWAN Adaptive Data Rate for Mobile Internet of Things Applications
In S3'19: Proceedings of the 2019 on Wireless of the Students, by the Students, and for the Students Workshop. ACM, 2019.Status: Published
Empirical Analysis of LoRaWAN Adaptive Data Rate for Mobile Internet of Things Applications
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | S3'19: Proceedings of the 2019 on Wireless of the Students, by the Students, and for the Students Workshop |
Pagination | 9-11 |
Date Published | 10/2019 |
Publisher | ACM |
ISBN Number | 978-1-4503-6929-9 |
URL | https://dl.acm.org/citation.cfm?id=3355727 |
DOI | 10.1145/3349621.3355727 |
Estimating Downlink Throughput from End-User Measurements in Mobile Broadband Networks
In IEEE World of Wireless, Mobile and Multimedia Networks (WoWMoM). IEEE, 2019.Status: Published
Estimating Downlink Throughput from End-User Measurements in Mobile Broadband Networks
Afilliation | Communication Systems |
Project(s) | MONROE: Measuring Mobile Broadband Networks in Europe |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | IEEE World of Wireless, Mobile and Multimedia Networks (WoWMoM) |
Publisher | IEEE |
Proceedings, refereed
HINDSIGHT: An R-Based Framework Towards Long Short Term Memory (LSTM) Optimization
In Multimedia Systems Conference (MMSys). ACM, 2018.Status: Published
HINDSIGHT: An R-Based Framework Towards Long Short Term Memory (LSTM) Optimization
Afilliation | Communication Systems |
Project(s) | MONROE: Measuring Mobile Broadband Networks in Europe |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | Multimedia Systems Conference (MMSys) |
Publisher | ACM |
Poster
Evaluation of DASH Rate Adaptation Algorithms in Operational Mobile Networks
Dublin, Ireland, 2017.Status: Published
Evaluation of DASH Rate Adaptation Algorithms in Operational Mobile Networks
In this study, we evaluate the performance of different Dynamic Adaptive Streaming over HTTP (DASH) video rate adaptation algorithms over mobile networks. More specifically, we compare and evaluate the performance of Basic, Segment Aware Rate Adaptation (SARA), and Buffer-Based Rate Adaptation (BBA) rate adaptation algorithms over the 4G-Long Term Evolution (LTE) networks of three Mobile Network Operators (MNOs): Telenor, Telia, and ICE. We use the Measuring Mobile Broadband Networks in Europe (MONROE) testbed for conducting measurements, and OpenVQ toolbox for evaluation.
Afilliation | Communication Systems |
Project(s) | MONROE: Measuring Mobile Broadband Networks in Europe |
Publication Type | Poster |
Year of Publication | 2017 |
Date Published | 06/2017 |
Place Published | Dublin, Ireland |
Proceedings, refereed
Experimentation in Controlled and Operational LTE Settings with FLEX-MONROE
In Proceedings of the 11th Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization. {ACM, 2017.Status: Published
Experimentation in Controlled and Operational LTE Settings with FLEX-MONROE
This demo paper presents FLEX-MONROE, a platform that facilitates better understanding of current LTE Mobile Broadband (MBB) networks and enables performance improvements by allowing experimentation with controllable LTE parameters. The platform enables investigating impact of low-level network parameter tweaks in LTE infrastructure on the application performance. We argue that FLEX-MONROE is crucial to provide guidelines on improving application performance both in the current and future MBB networks.
Afilliation | Communication Systems |
Project(s) | FLEX-MONROE: FIRE LTE Testbeds for Open Experimentation |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | Proceedings of the 11th Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization |
Pagination | 93–94 |
Publisher | {ACM |
FLEX-MONROE: A Unified Platform for Experiments under Controlled and Operational LTE Settings
In 11th ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization (WiNTECH2017). Utah, US: ACM, 2017.Status: Published
FLEX-MONROE: A Unified Platform for Experiments under Controlled and Operational LTE Settings
This paper presents FLEX-MONROE, a unique platform that facilitates achieving a thorough understanding of LTE networks, one that captures the status of current operational MBB networks and that also enables LTE performance improvements by allowing experimentation in an environment with controllable LTE parameters. Using this platform, we propose to investigate how variations in the LTE network parameters infuence the network characteristics, which, in turn, translate to application performance metrics that represent the end-user experience. We argue that the FLEX- MONROE platform is crucial to understand, validate and ultimately improve how current operational MBB networks perform, towards providing guidelines for designing future 5G architectures. Furthermore, understanding the effects of low-level tweaks in network parameters in the LTE infrastructure on the application performance is critical to provide guidelines on how to improve the application performance in the current but also future MBB networks.
Afilliation | Communication Systems |
Project(s) | FLEX-MONROE: FIRE LTE Testbeds for Open Experimentation , Department of Mobile Systems and Analytics |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | 11th ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization (WiNTECH2017) |
Publisher | ACM |
Place Published | Utah, US |
DOI | 10.1145/3131473.3131477 |
FLEX-MONROE: A Unified Platform for Experiments under Controlled and Operational LTE Settings
In 11th ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization (WiNTECH2017). Utah, US: ACM, 2017.Status: Published
FLEX-MONROE: A Unified Platform for Experiments under Controlled and Operational LTE Settings
This paper presents FLEX-MONROE, a unique platform that facilitates achieving a thorough understanding of LTE networks, one that captures the status of current operational MBB networks and that also enables LTE performance improvements by allowing experimentation in an environment with controllable LTE parameters. Using this platform, we propose to investigate how variations in the LTE network parameters infuence the network characteristics, which, in turn, translate to application performance metrics that represent the end-user experience. We argue that the FLEX- MONROE platform is crucial to understand, validate and ultimately improve how current operational MBB networks perform, towards providing guidelines for designing future 5G architectures. Furthermore, understanding the effects of low-level tweaks in network parameters in the LTE infrastructure on the application performance is critical to provide guidelines on how to improve the application performance in the current but also future MBB networks.
Afilliation | Communication Systems |
Project(s) | FLEX-MONROE: FIRE LTE Testbeds for Open Experimentation , Department of Mobile Systems and Analytics |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | 11th ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization (WiNTECH2017) |
Publisher | ACM |
Place Published | Utah, US |
DOI | 10.1145/3131473.3131477 |
The Same, Only Different: Contrasting Mobile Operator Behavior from CrowdSourced Dataset
In IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2017). Montreal, Canada: IEEE, 2017.Status: Published
The Same, Only Different: Contrasting Mobile Operator Behavior from CrowdSourced Dataset
Crowdsourcing mobile network performance evaluation is rapidly gaining popularity, with new applications aiming to deliver more accurate and reliable results every day. From the perspective of end-users, these utilities help them estimate the performance of their service provider in terms of throughput, latency and other key performance indicators of the network. In this paper, we build ORCA: Operator Classifier, a Machine Learning (ML) based framework to define and determine the behavior of Mobile Network Operators (MNOs) from crowdsourced datasets. We investigate whether one can differentiate MNOs by using crowdsourced end-to-end network measurements. We consider different performance metrics (e.g. Download (DL)/Upload (UL) data rate, latency, signal strength) and study the impact of them individually but also collectively on differentiating MNOs. We use RTR Open Data, an open dataset of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), to characterize the three major mobile native operators and two virtual operators in Austria. Our results show that ORCA can be used to identify patterns between various mobile systems and disclose their differences from the end-user perspective.
Afilliation | Communication Systems |
Project(s) | No Simula project |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2017) |
Publisher | IEEE |
Place Published | Montreal, Canada |
The Same, Only Different: Contrasting Mobile Operator Behavior from CrowdSourced Dataset
In IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2017). Montreal, Canada: IEEE, 2017.Status: Published
The Same, Only Different: Contrasting Mobile Operator Behavior from CrowdSourced Dataset
Crowdsourcing mobile network performance evaluation is rapidly gaining popularity, with new applications aiming to deliver more accurate and reliable results every day. From the perspective of end-users, these utilities help them estimate the performance of their service provider in terms of throughput, latency and other key performance indicators of the network. In this paper, we build ORCA: Operator Classifier, a Machine Learning (ML) based framework to define and determine the behavior of Mobile Network Operators (MNOs) from crowdsourced datasets. We investigate whether one can differentiate MNOs by using crowdsourced end-to-end network measurements. We consider different performance metrics (e.g. Download (DL)/Upload (UL) data rate, latency, signal strength) and study the impact of them individually but also collectively on differentiating MNOs. We use RTR Open Data, an open dataset of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), to characterize the three major mobile native operators and two virtual operators in Austria. Our results show that ORCA can be used to identify patterns between various mobile systems and disclose their differences from the end-user perspective.
Afilliation | Communication Systems |
Project(s) | No Simula project |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2017) |
Publisher | IEEE |
Place Published | Montreal, Canada |