Characterizing RNTI Allocation and Management in Mobile Networks
Giulia Attanasio, PhD Student at IMDEA Networks Institute, Madrid, Spain
The characterization of user behavior is key to performing traffic analysis, modeling, and optimization of network components and protocols. This is especially true for 5G and beyond networks that will heavily rely on machine learning for network optimization. In Base Station (BS) traffic traces, users are uniquely identified by a Radio Network Temporary Identifier (RNTI) assigned to them. RNTIs are not bound to a user but are reused upon expiration of an inactivity timer, whose duration is operator dependent. This implies that, over time, multiple users can be mapped to the same RNTI ID in diverse ways. Hence, when using real-world radio access measurement traces for traffic analysis, distinguishing individual users within the RNTI space is a non-trivial task.
This talk presents “Characterizing RNTI Allocation and Management in Mobile Networks”. In this paper, we provide the first in-depth study of the RNTI allocation process and shed light not only on the setting of the inactivity timer but also on the relationship of the RNTI allocation scheme and the user characteristics. For this, we collect a large dataset of mobile traffic from multiple BSs of several mobile network operators. The analysis of the decoded control messages of the BS unveils that the RNTI allocation process changes over time depending on the BSs observed load and time of day. We also observe that the RNTI expiration threshold is on the order of minutes, and demonstrate how using thresholds around 10 s that are reported in the vast majority of the literature can bias subsequent analyses. Overall, our work provides an important step towards dependable mobile network trace analysis, and lays more solid foundations to research relying on traffic traces for data-driven analysis and simulation.
About Giulia Attanasio
Giulia Attanasio is a PhD student in Telematics at the University of Carlos III. Her research interest lies between machine learning and mm-wave communications. Previously, she completed an MSc in Communications and Computer Networks Engineering and a BS in Telecommunications Engineering at the Polytechnic University of Turin.
This event will be conducted in English