In-depth Study of RNTI Management in Mobile Networks: Allocation Strategies and Implications on Data Trace Analysis

Abstract

The advance of mobile network technologies and components heavily relies on data-driven techniques. This is especially true for fifth generation (5G) and the upcoming sixth generation (6G) networks, as the optimization of network components and protocols is expected to be fueled by artificial intelligence (AI) based solutions. When using real-world radio access measurement traces, the identity of individual users is not directly accessible because at runtime operation Base Stations (BSs) assign Radio Network Temporary Identifiers (RNTIs) to users. 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 are mapped to the same RNTI. In fact, the allocation of RNTIs to users is implemented in diverse and proprietary ways by operators and equipment vendors. Distinguishing individual users within the RNTI space is a non-trivial task and key to analyze traffic traces properly. In this paper, we make the following contributions: i) we propose and validate two complementary methodologies to identify the RNTI inactivity threshold, and we characterize ii) the RNTI allocation process of network operators, and iii) the user traffic patterns given the specific RNTI allocation process. Our study is based on a large dataset we collected from production BSs of several mobile network operators across five different countries. We find that there exist heterogeneous strategies for RNTI allocation that BSs dynamically use depending on the traffic load and daytime. We further observe that the RNTI expiration threshold is in the order of minutes, and demonstrate how using thresholds around 10 seconds, as 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 solid foundations to research relying on traffic traces for data-driven analysis.