Since their deployment and commercialization, 5th Generation (5G) mobile systems have been extensively analyzed to quantify the Quality of Service and Experience (QoS/QoE) achievable by heterogeneous services. Real-time interactive services, i.e., applications within the scope of Ultra-Reliable Low Latency Communication (URLLC) and at the intersection of URLLC and enhanced Mobile Broadband (eMBB), are, however, often tested using simplistic methodologies that do not provide accurate assessments. In this paper, we extend our previous work on the empirical characterization of mobile networks by presenting a comprehensive analysis of a methodology standardized by the International Telecommunication Union Telecommunication Standardization Sector (ITU-T). This methodology is designed for systematic and reproducible QoS/QoE evaluations of real-time interactive services. We validate it through dedicated measurements (for which we open-source the corresponding dataset along with this paper) in the Karlstad University testbed, i.e., a private network supporting 5G connectivity modes and features beyond those available in current public networks in Sweden. Our results, spanning across services, mobility scenarios, connectivity modes, and servers, provide key insights into the intricate dependencies between QoS/QoE, environmental conditions, and system configurations, ultimately serving as a foundation for designing high-performing beyond-5G mobile systems.

The Narrowband Internet of Things (NB-IoT) technology has an important role in the mobile cellular ecosystem, enabling massive Machine Type Communication (mMTC) services. NB-IoT propagation was preliminarily analyzed via a measurement campaign carried out in 2020 in the city of Oslo, Norway. This investigation resulted in Oslo-2020, the first NB-IoT-specific Alpha-Beta-Gamma (ABG) path loss (PL) model, which showed higher prediction accuracy compared to models developed for different technologies but often used for NB-IoT. In this paper, to further investigate NB-IoT PL in urban scenarios, we analyze new measurement campaigns performed in 2020-2021 and 2023 in the city of Rome, Italy. First, we use the 2020-2021 measurements to derive Rome-2021, a new NB-IoT-specific ABG PL model. We show that Rome-2021 preserves the statistical properties of Oslo-2020 (e.g., the Gaussianity of the PL exponent distribution across base stations), although the moments of the distributions are different due to city-specific environmental characteristics. We also use new data on signal losses due to outdoor-to-indoor propagation to refine the analysis of this scenario. Finally, we propose a methodology to combine Oslo-2020 and Rome-2021 into a more general model. Our methodology uses so-called Mixture Distributions (MDs), thus leveraging the shared statistical properties between Oslo-2020 and Rome-2021. By using the 2023 measurements, we show that our MD-based approach estimates PL model parameters with higher accuracy compared to Oslo-2020 and Rome-2021 models used separately, thus providing an effective solution for predicting NB-IoT urban PL in lack of site-specific measurements and information. 

Multi-Access Edge Computing (MEC) is a promising solution for providing the computational resources and low latency required by vehicular services, such as autonomous driving. It enables cars to offload computationally intensive tasks to nearby servers. Effective offloading involves determining when to offload tasks, selecting the appropriate MEC site, and efficiently allocating resources to ensure optimal performance. While car mobility poses significant challenges to guaranteeing reliable task completion, today we lack energy-efficient solutions to solve this problem, especially when considering real-world car mobility traces. In this paper, we begin by examining the mobility patterns of cars using data obtained from a leading mobile network operator in Europe. Based on the insights from this analysis, we design an optimization problem for task computation/offloading, considering both static and mobility scenarios. Our objective is to minimize the total energy consumption - both at the cars and the MEC nodes - while satisfying the latency requirements of various tasks. We evaluate our solution, based on multi-agent reinforcement learning, both in simulations as well as in a realistic setup that relies on datasets from the operator. Our solution shows a significant reduction of user dissatisfaction and task interruptions in both static and mobile scenarios, while achieving energy savings of 47% (static) and 14% (mobile) compared to state-of-the-art schemes. 

Background

High professional competence is crucial for ambulance personnel, as expected by patients, relatives, and organizations. Prehospital advanced trauma and medical care demand exceptional competence. Specialized training programs i.e., AMLS, ATLS, and PHTLS are widely adopted internationally. Integrating “beyond-5G and 6G technologies” can significantly enhance realism, increase the number of simulated patient cases, and improve prehospital nursing education by providing real-time data and advanced communication capabilities. This integration supports the development of critical thinking and decision-making skills and ensures that ambulance personnel are well-prepared to handle a wide range of emergencies, ultimately improving patient outcomes and overall service efficiency. The aim of this study is to evaluate the impact of integrating high-fidelity simulation with “beyond-5G and 6G technologies” in prehospital nurse education.

Methods

Students will practically carry out multiple high fidelity simulation cases in a road ambulance. The cases are communicated and distributed from the learning site to the ambulance using “beyond-5G and 6G technologies”. Data are gathered using the Paramedic Global Rating Scale and System Usability Scale. Students’ experiences of reality and learning will also be explored in individual (n=15) interviews.

Results

The study is expected to demonstrate that integrating realistic high-fidelity simulation with “beyond-5G and 6G technologies” can improve clinical and decision-making skills in prehospital nursing students. The study is also expected to be able to relate effective simulation methods to different simulated scenarios and contribute to more effective prehospital nurse education.

Conclusions

High-fidelity simulation with “beyond-5G and 6G technologies” could be a valuable addition to future prehospital nurse education.

Cellular Internet of Things (IoT) offers extensive connectivity today and is poised for further growth in the 5G era, especially after the upcoming sunsetting of 2G and 3G networks. It facilitates crucial IoT applications, such as smart metering to reduce energy consumption, smart logistics to enhance distribution efficiency, and smart environmental monitoring to address urban pollution. To support this expansion, leading mobile operators, global vendors, and developers are deploying NB-IoT networks as part of their long-term 5G IoT strategies. A key goal of NB-IoT is to optimize the battery life of IoT devices. While NB-IoT includes several power-saving features, the cell selection and re-selection processes result in significant energy consumption. We conducted a measurement campaign across three locations in two countries, Norway and Sweden, to investigate this issue based on an NB-IoT commercial network. Our findings reveal that cell re-selection frequently occurs even when the IoT device is stationary. Additionally, the reliance on Reference Signal Received Power (RSRP) for cell selection often leads to oscillations between the nearby cells or prolonged attach procedure. To address this challenge, we propose a cell reselection framework that considers RSRP while also considering historical information on transmission reliability. Evaluations of our proposed framework demonstrate energy savings of over 50% compared to legacy RSRP-based cell selection methods.

The design of positioning solutions for the Industrial Internet of Things (IIoT) faces several open challenges, particularly in complex indoor environments typical of industrial settings: first and foremost, the limited availability of experimental datasets from realistic scenarios, in particular in relation to private 5G networks.This paper investigates range-free positioning for IIoT in indoor environments using a combination of public and private 5G networks operating at both midband (3.7 GHz) and mmWave (26 GHz) frequencies. The study introduces one of the first publicly available datasets collected in a realistic industrial setting, utilizing synchronization signals from 5G networks. A Weighted k-Nearest Neighbors (WkNN) algorithm is used to perform fingerprinting-based positioning using the experimental data, comparing the achievable accuracy in public vs. private deployments. The results show that private mmWave deployments significantly outperform public networks in terms of positioning accuracy, achieving errors as low as 2.16 meters. Additionally, the study highlights the discriminative power of time-based features such as Time of Arrival (ToA) in enhancing range-free positioning accuracy.

Existing proposals for positioning in narrowband Internet of Things (NB-IoT) networks based on range estimation are characterized by either low accuracy or lack of compliance with 3GPP standards. While range-free approaches taking advantage of machine learning (ML) have been recently proposed as a potential way forward, their evaluation has been carried out only in simulated environments, with the exception of weighted k nearest neighbors (WkNN), recently tested on experimental data. This work investigates five ML strategies for range-free positioning in NB-IoT networks, based on WkNN and its combination with preprocessing and classification algorithms as well as on artificial neural networks (ANNs). The strategies are evaluated on experimental data and are compared based on a set of key performance indicators measuring both positioning performance and processing load. Two different datasets taken at different times and locations were adopted, enabling the validation of strategies optimized on one testbed on the other, as well as the study of the impact of dataset features on performance. Results show that range-free positioning using ML is a viable solution in commercial NB-IoT networks, and that WkNN and ANNs are at the two extremes in terms of a performance/complexity tradeoff; intermediate tradeoffs can be achieved by combining WkNN with preprocessing techniques and classification models.

Mobile networks are highly complex systems. Therefore, it is crucial to examine them from an empirical perspective to better understand how network features affect performance, so to suggest additional improvements. To this aim, this paper presents a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT), and 5G New Radio (NR) connectivity. We collected our dataset during seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization.

5th Generation (5G) mobile systems are being deployed to address the Quality of Service and Experience (QoS/QoE) requirements of several use cases, including enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). While eMBB performance testing inherits well-established methodologies and Key Performance Indicators (KPIs), beyond-eMBB services (i.e., URLLC and eMBBURLLC real-time applications) are often tested by adopting simplistic or in-house methodologies, which do not help towards accurate assessment and comparison. In this paper, we fill this gap by providing a detailed analysis of a methodology, recently standardized by the International Telecommunication Union Telecommunication Standardization Sector (ITU-T), that targets systematic performance evaluations of beyond-eMBB services. The methodology relies on the definition of a QoE KPI, i.e., the interactivity score (i-score), on top of three QoS KPIs measuring service latency, stability, and continuity. To this aim, we perform a multi-service measurement campaign on two 5G networks across two cities in Sweden, during which we run a large number of tests compliant with the ITU-T methodology, and analyze the collected data. Our results empirically validate the methodology, showcasing its ability of capturing heterogeneous service characteristics and requirements, as well as the interdependencies between i-score and QoS KPIs, and the impact of different factors on QoS/QoE performance, including user mobility, connection capability, and server location.