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 deployment of Cellular Internet of Things (CIoT)is expected to reach over six billion devices by 2030. Many ofthese devices will be located in remote areas where replacingor recharging their batteries would be difficult and expensive.Therefore, it is crucial to configure these devices for efficientenergy use to avoid frequent battery replacements or recharging.However, optimizing the energy consumption of CIoT devices, con-sidering their applications and operating environmental conditions,presents a complex challenge. In response to this challenge, wepropose the Gradient-Boosted Learning Optimization for BatteryEfficiency (GLOBE) framework for dynamic configuration ofNarrowband Internet of Things (NB-IoT) devices. GLOBE adjuststhe radio layer of NB-IoT devices based on data transmissionpatterns and network conditions, enabling swift and automatedreconfiguration. Our results demonstrate that GLOBE reducesenergy consumption by 30% to 75% compared to baselineconfigurations, offering significant benefits for both networkoperators and end devices by improving energy efficiency.

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.

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. 

Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry, and Mobile Network Operators (MNOs) are currently deploying 5G alongside their existing Fourth Generation (4G) networks. In this paper, we present results and insights from our large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two MNOs in Rome, Italy, to study network deployment and radio coverage aspects, and explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). We further leverage a machine learning (ML)-based approach to model the Dual Connectivity (DC) feature enabled by 5G NSA. Our data-driven analysis shows that 5G NSA can provide higher downlink throughput and slightly lower latency compared to 4G. However, performance is influenced by several factors, including propagation conditions, system configurations, and handovers, ultimately highlighting the need for further system optimization. Moreover, by casting the DC modeling problem into a classification problem, we compare four supervised ML algorithms and show that a high model accuracy (up to 99%) can be achieved, in particular, when several radio coverage indicators from both access networks are used as input. Finally, we conduct analyses towards aiding the explainability of the ML models. 

As the adoption of Fifth Generation (5G) systems increases, efforts towards Sixth Generation (6G) systems have already started across research, standardization, and stakeholder fora. 6G is expected to support applications with immersive capabilities, with specific use case requirements from different verticals playing a critical role in solution development. Unlike current solutions in the education vertical that uses immersive technologies such as Augmented/Virtual/eXtended Reality (AR/VR/XR), which rely on pre-recorded content and  lack engagement, 6G can enhance remote education by enabling real-time, AR/VR/XR-enriched interactions among students and instructors. This paper presents ongoing activities within the 6G-PATH EU project, towards the design, implementation, and testing of a 6G use case for healthcare personnel remote education/training, which aims to facilitate real-time, AR/VR/XR-enhanced interactions among healthcare trainees and instructors.

Deploying Cellular Internet of Things (CIoT) devices in urban and remote areas faces significant energy efficiency challenges. This is especially true for Narrowband IoT (NB-IoT) devices, which are expected to function on a single charge for up to 10 years while transmitting small amounts of data daily. The 3rd Generation Partnership Project (3GPP) has introduced energy-saving mechanisms in Releases 13 to 16, including Early Data Transmission (EDT) and Preconfigured Uplink Resources (PURs). These mechanisms extend battery life and reduce latency by enabling data transmission without an active Radio Resource Control (RRC) connection or Random Access Procedure (RAP). This paper examines these mechanisms using the LENA-NB simulator in the ns-3 environment, which is a comprehensive framework for studying various aspects of NB-IoT. The LENA-NB has been extended with PURs, and our analysis shows that PURs significantly enhance battery life and latency efficiency, particularly in high-density environments. Compared to the default RAP method, PURs reduce energy consumption by more than 2.5 times and increases battery life by 1.6 times. Additionally, PURs achieve latency reductions of 2.5 - 3.5 times. The improvements with PURs are most notable for packets up to 125 bytes. Our findings highlight PURs' potential to enable more efficient and effective CIoT deployments across various scenarios. This study represents a detailed analysis of latency and energy consumption in a simulated environment, advancing the understanding of PURs' benefits.

Starlink satellite internet has emerged as a viable solution for extending internet connectivity, particularly in remote or underserved areas, complementing terrestrial networks. With the increasing use of Starlink for latency-critical realtime multimedia communication such as live streaming, video conferencing, virtual reality, and online gaming, understanding and predicting Starlink performance becomes increasingly relevant. However, the dynamic nature of satellite constellations, atmospheric conditions, bandwidth availability, and network congestion present hurdles to maintaining steady network performance, which is crucial for real-time multimedia applications. This study investigates the predictability of Starlink downlink throughput, which can aid in proactive scheduling decisions and intelligent traffic management across multiple paths, thereby optimizing Starlink for real-time multimedia applications. The research develops a comprehensive approach for throughput prediction by leveraging historical time series data and a diverse array of machine learning and deep learning models over time slot sizes of 1 s, 100 ms, 50 ms, 20 ms, 10 ms, and 5 ms. Furthermore, the work includes three baseline models to assess the performance enhancement that the machine learning and deep learning models provide. The study proposes a prediction scheme that allows a set of predictors to perform single-step predictions over these prediction intervals. Model evaluation results show that predicting downlink throughput at short time slot sizes of 5 ms and 10 ms for different history sizes offers substantial benefits relative to the baseline models.

Fifth Generation (5G) systems have been commercially available worldwide for at least a couple of years, with mid-band Non-Standalone (NSA) being the deployment mode preferred by Mobile Network Operators (MNOs). Empirical analyses have provided so far key insights on 5G NSA performance from different perspectives, but most of these works consider short time periods to drive conclusions. In this paper, we investigate the evolution of 5G NSA considering deployment, performance, and services, including positioning. We perform a large-scale measurement campaign in two phases (2021 and 2023), covering six MNOs in two European countries, Italy and Sweden. Our results show significant differences in network deployment and performance, with increasing network density and frequencies but, at times, decreasing downlink throughput performance. For the latter, we identify worse radio coverage and connectivity issues as root causes. By using a standardized methodology, we also evaluate the performance of new services such as real-time gaming and augmented/virtual reality, and reveal that stable 5G connectivity is key to meet their requirements. Similarly, we highlight the negative effects of roaming on performance. Finally, we evaluate 5G fingerprinting positioning systems and show that a higher accuracy is achievable in denser 5G deployments.