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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 promises of multipath transport are to aggregate bandwidth, improve resource utilisation and enhance reliability. In this article, we demonstrate that the way multipath coupled congestion control is defined today leads to a suboptimal resource utilisation when network paths are disjoint, i.e., they do not share a bottleneck link. With growing interest in standardising Multipath QUIC (MPQUIC), we have implemented the practical Shared Bottleneck Detection (SBD) algorithm from RFC8382 in MPQUIC (MPQUIC-SBD). Through extensive experiments, we evaluate MPQUIC-SBD in the context of video streaming with various Adaptive Bitrate (ABR) algorithms, addressing both ABR classes of rule- and learning-based solutions. We demonstrate that MPQUIC-SBD accurately detects shared bottlenecks over 90% of the time, depending on the ABR algorithm, as the size of the video segments increases. In non-shared bottleneck scenarios, when MPQUIC-SBD detects that its QUIC subflows do not share the same network resources, it decouples their congestion windows accordingly, enabling video throughput gains of up to 37% compared to MPQUIC. These gains translate directly into improved video quality metrics, including higher bitrate, better resolution and reduced buffering, resulting in an enhanced quality of experience for users.

The mobile telecommunication industry is undergoing continuous evolution to cope with ever increasing service requirements and expectations of end users. This has recently led to the rise of Mobile Network Aggregators (MNAs), a new type of global virtual operators that deliver mobile communication services by utilizing multiple Mobile Network Operators (MNOs), dynamically connecting to the one that best meets their customers’ needs based on location and time. MNAs can then offer optimized global coverage by connecting to local MNOs that have limited (e.g., national) geographic service. In this paper, we provide a first in-depth analysis of the operations of three major MNAs: Google Fi, Twilio, and Truphone. We conduct performance measurements across these MNAs for critical applications spanning DNS, web browsing, and video streaming, and compare their performance against that of a traditional MNO from two very diverse geographical locations, US and Spain. We find that MNAs may introduce some delay compared to local MNOs in the region where the user is roaming, yet they offer significant performance improvements over the traditional MNOs roaming model, such as home-routed roaming. To fully assess the potential benefits of the MNA model, we also carry out emulation studies assessing the potential performance gains that MNAs could achieve by deploying both control and user plane functions of open-source 5G implementations across different Amazon Web Services locations.

Mobile Augmented Reality (MAR) applications pose unique challenges due to computation intensity, constrained device resources, and high interactive rendering requirements. The emergence of 5G and edge computing offers opportunities to offload computation to the edge and cloud, indirectly enhancing the computing capability and usage duration of MAR devices. However, existing general task offloading and multipath transmission techniques do not address the challenges in offloading path selection with multiple edges, dynamic resource competition awareness, and spatial computation with strong task dependencies. This paper contributes FPSelector, a flexible path selector for MAR offloading. We present a two-tier MAR-specific offloading scheme with multiple edge nodes. In offloading decisions, we design a reinforcement learning model to generate the selection policy for each packet of an AR data stream. This model incorporates an action masking mechanism, a comprehensive reward function, and state features complemented by a resource prediction module, making FPSelector aware of dynamic heterogeneous environments. Moreover, we propose an online learning strategy to facilitate real-time selection. To validate its efficacy, we compare FPSelector’s performance against leading schedulers under various scenarios, demonstrating a notable reduction of 9.9% and 9.6% in overall completion time for 4 K and 8 K video-based MAR applications compared to its closest competitor.

Existing proposals for positioning in 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 Neighbours (W $k$ NN), recently tested on experimental data. This work investigates five ML strategies for range-free positioning in NB-IoT networks, based on W $k$ NN 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 (KPIs) 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 W $k$ NN and ANNs are at the two extremes in terms of a performance/complexity trade-off; intermediate trade-offs can be achieved by combining W $k$ NN with preprocessing techniques and classification 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.