Özgü Alay’s research while affiliated with University of Oslo and other places

After first being standardized by the 3rd Generation Partnership Project (3GPP) in Release 15, 5th Generation (5G) mobile systems have been rapidly deployed worldwide [...]

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments.

Millimeter-wave (mmWave) radio is a key building block in 5G and beyond cellular networks. However, mmWave channels are very sensitive to environmental conditions and depend on Line-of-Sight connections to provide very high data rates. Achieving reliable, consistent communication — i.e., a steady link rate together with low delay — over mmWave links is therefore a challenging problem. The goal of this work is to explore the use of predictive control to manage and simultaneously use multiple available mmWave paths to achieve reliable consistent communication by means of a multipath proxy. We investigate transient solutions of Markov Modulated Fluid Queues (MMFQ) to model the short-term evolution of the proxy’s packet queue, consistent with the use of Markovian models to capture the behavior of mmWave channel blocking. We propose a combination of models that can be solved using newly proposed matrix-analytic techniques in a timely enough manner for use in real-time control. This gives us a prediction, over a short time horizon, of either proxy queue distributions or probabilities of reaching particular proxy buffer levels. Thus, it enables the proxy to make preemptive path decisions in order to maintain a desired Quality of Service. A proof-of-concept simulation study demonstrates the efficacy of our proposed MMFQ-based predictive approach over both static and purely reactive control approaches. Further, we explore the potential benefits of a hybrid approach to path management, combining both predictive and reactive control. This can allow the controller to cater for unforeseen events that cannot be forecast by the predictive controller, mitigating the resulting extra queuing and corresponding delay spikes. <https://davidahayes.owlstown.net/publications/35029-investigating-predictive-model-based-control-to-achieve-reliable-consistent-multipath-mmwave-communication>

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 s, 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.