Mobile broadband (MBB) networks underpin numerous vital operations of the society and are arguably becoming the most important piece of the communications infrastructure. The use of MBB networks has exploded over the last few years due to the popularity of mobile devices, combined with the availability of high-capacity 3G/4G mobile networks. More than half of this mobile traffic is generated by mobile video. Given the increasing importance of MBB networks, there is a strong need for a better understanding of the fundamental characteristics of MBB networks and their relationship with the performance of popular applications, such as mobile video. The overarching goal of the MEMBRANE project is to address this need by developing reliable models that can accurately capture the relationship between the performance and reliability parameters and the underlying network factors that affect them. These models will thus reveal the key factors that are required to accurately describe and predict the performance and reliability of MBB networks in general and mobile video in particular. To achieve this goal, we will follow a real-world measurement-driven modeling approach. As the first step, we will first run an extensive measurement campaign to collect data from operational MBB networks in order to understand their performance and reliability. Second, we will derive machine-learning based models using the collected data in order to capture the network characteristics of MBB networks. Finally, we will extend our models to consider the performance of mobile video, more specifically video streaming, experienced by the end users. These models will be instrumental for different stakeholders from operators to application developers in improving the quality of their services and products. Therefore, MEMBRANE will have a significant impact on different sectors of industry while helping to improve the performance of their products leading to a better user experience for the end users.
Project leader: Özgü Alay
Category: Øvrige forskningsinstitutter
Institution: SIMULA RESEARCH LABORATORY AS