@inproceedings {26519,
	title = {Estimating Downlink Throughput from End-User Measurements in Mobile Broadband Networks},
	journal = {IEEE World of Wireless, Mobile and Multimedia Networks (WoWMoM)},
	year = {2019},
	pages = {1-10},
	publisher = { IEEE},
	abstract = {In recent years, Downlink (DL) throughput estima- tion in Mobile Broadband (MBB) networks has gained immense popularity and it is expected to become a vital component of the upcoming fifth generation (5G) systems. Plentiful adaptive video streaming algorithms greatly rely on accurate DL throughput predictions to adapt their mechanisms and ensure high Quality of Service (QoS) to the end-users. Thus far, conventional DL throughput estimation approaches, also known as speed tests, require an extensive exchange of TCP traffic over the network for an allocated time duration. While such tools appear to deliver trustworthy results, they turn out to be inefficient when mobile subscriptions with limited data plans are engaged.In this paper, we propose a supervised Machine Learning (ML) solution for DL throughput estimation that aims at delivering highly accurate predictions while significantly limiting the over- the-air data consumption. We capture the network performance metrics by exploring both crowdsourced and controlled test- ing methodologies. We leverage RTR-NetTest, a platform of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), and MONROE-NetTest, its counterpart wrapper built as an Experi- ment as a Service (EaaS) on top of Measuring Mobile Broadband Networks in Europe (MONROE). Results reveal that our solution can achieve a 39.7\% reduction in terms of data consumption while delivering a Median Absolute Percentage Error (MdAPE) of 5.55\%. We further show that accuracy can be traded-off, for example, a significant data consumption reduction of 95.15\% can be achieved for a MdAPE of 20\%.},
	author = {Kousias, Konstantinos and Alay, {\"O}zg{\"u} and Argyriou, Antonios and Lutu, Andra and Riegler, Michael}
}
@inproceedings {26039,
	title = {HINDSIGHT: An R-Based Framework Towards Long Short Term Memory (LSTM) Optimization},
	journal = {Multimedia Systems Conference (MMSys)},
	year = {2018},
	publisher = {ACM},
	author = {Kousias, Konstantinos and Riegler, Michael and Alay, {\"O}zg{\"u} and Argyriou, Antonios}
}
@inproceedings {25621,
	title = {FLEX-MONROE: A Unified Platform for Experiments under Controlled and Operational LTE Settings},
	journal = {11th ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation \& Characterization (WiNTECH2017)},
	year = {2017},
	publisher = {ACM},
	address = {Utah, US},
	abstract = {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.},
	doi = {10.1145/3131473.3131477},
	author = {Fida, Mah-rukh and Kousias, Konstantinos and Lutu, Andra and Rajiullah, Mohammad and Alay, {\"O}zg{\"u} and Brunstr{\"o}m, Anna and Argyriou, Antonios},
	editor = {Kousias, Konstantinos}
}
@inproceedings {25620,
	title = {The Same, Only Different: Contrasting Mobile Operator Behavior from CrowdSourced Dataset},
	journal = {IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2017)},
	year = {2017},
	publisher = {IEEE},
	address = {Montreal, Canada},
	abstract = {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.},
	author = {Kousias, Konstantinos and Midoglu, Cise and Alay, {\"O}zg{\"u} and Lutu, Andra and Argyriou, Antonios and Riegler, Michael},
	editor = {Kousias, Konstantinos}
}
@inproceedings {24431,
	title = {Cooperative Live Video Multicast for Small Cell Base Stations with Overlapping Coverage},
	journal = {IEEE Wireless Communications and Networking Conference},
	year = {2016},
	month = {04/2016},
	author = {Alay, {\"O}zg{\"u} and Argyriou, Antonios}
}
@article {23705,
	title = {Distributed Estimation in Wireless Sensor Networks With an Interference Canceling Fusion Center},
	journal = {IEEE Transactions on Wireless Communication},
	volume = {15},
	year = {2016},
	month = {03/2016},
	pages = {2205-2214},
	publisher = {IEEE},
	author = {Argyriou, Antonios and Alay, {\"O}zg{\"u}}
}
@inproceedings {24432,
	title = {Lossy Transmission of Correlated Sources in a Multiple Access Quasi-Static Fading Channel},
	journal = {IEEE Wireless Communications and Networking Conference},
	year = {2016},
	month = {04/2016},
	author = {Argyriou, Antonios and Alay, {\"O}zg{\"u}}
}
@inproceedings {Simula.simula.2726,
	title = {Transmission of Correlated Gaussian Sources With Opportunistic Interference},
	journal = {Wireless Communications and Networking Conference (WCNC)},
	year = {2014},
	month = {April},
	publisher = {IEEE},
	type = {1},
	abstract = {Interfering transmissions of independent sources is shown to provide benefits at a cost of complex receiver design. In this paper, we study the optimum transmission of correlated Gaussian sources by allowing opportunistic interference in order to minimize the expected distortion. We assume a successive interference cancellation decoder at the destination and consider a Wyner-Ziv type setup to transmit the correlated sources along with interference. We show how interference can be utilized to improve the end-to-end distortion for correlated sources.},
	author = {Alay, {\"O}zg{\"u} and Argyriou, Antonios},
	editor = {IEEE, }
}
