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The deployment of the next generation of cellular networks (5G) is expanding to enable new services and improve the quality of existing ones. Despite the benefits of 5G networks, they also present new challenges for the performance of classical and recently-proposed congestion control algorithms (CCAs), e.g., Bottleneck bandwidth and round-trip propagation time (BBR) and Copa. Despite their successful adoption in the Internet, BBR has been shown to overestimate the bottleneck bandwidth in cellular networks, and Copa has not been independently tested on a similar scale and detail as BBR in cellular networks. In this work, we compare the performance of these fairly recent CCAs as well as the widely deployed CUBIC CCA, and a modification to BBR for cellular networks (RBBR) at 5G rates. The evaluation is performed using the emerging QUIC protocol and uses both emulations and live experiments. Our results show that in 5G networks, CUBIC, BBR, and Copa suffer from significant bufferbloat, longer packet delays, and lower throughput, respectively. We also observe that in cases where the bottleneck is largely in the 5G link, RBBR can offer a significant delay reduction compared to BBR and CUBIC.

Edge computing is proposed as a technical enabler for meeting emerging network technologies (such as 5G and Industrial Internet of Things), stringent application requirements and key performance indicators (KPIs). It aims to alleviate the problems associated with centralized cloud computing systems by placing computational resources to the network’s edge, closer to the users. However, the complexity of distributed edge infrastructures grows when hosting containerized workloads as microservices, resulting in hard to detect and troubleshoot outages on critical use cases such as industrial automation processes. Observability aims to support operators in managing and operating complex distributed infrastructures and microservices architectures by instrumenting end-to-end runtime performance. To the best of our knowledge, no survey article has been recently proposed for distributed edge and containerized microservices observability. Thus, this article surveys and classifies state-of-the-art solutions from various communities. Besides surveying state-of-the-art, this article also discusses the observability concept, requirements, and design considerations. Finally, we discuss open research issues as well as future research directions that will inspire additional research in this area.

The Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy efficiency in mind. This paper conducts a comprehensive survey on energy-saving solutions in 3GPP-based CIoT networks. In comparison to current studies, the contribution of this paper is the classification and an extensive analysis of existing energy-saving solutions for CIoT, e.g., the configuration of particular parameter values and software modifications of transport- or radio-layer protocols, while also stressing key parameters impacting the energy consumption such as the frequency of data reporting, discontinuous reception cycles (DRX), and Radio Resource Control (RRC) timers. In addition, we discuss shortcomings, limitations, and possible opportunities which can be investigated in the future to reduce the energy consumption of CIoT devices.

The successful rollout of fifth-generation (5G) networks requires a full understanding of the behavior of the propagation channel, taking into account the signal formats and the frequencies standardized by the Third Generation Partnership Project (3GPP). In the past, channel characterization for 5G has been addressed mainly based on the measurements performed on dedicated links in experimental setups. This paper presents a state-of-the-art contribution to the characterization of the outdoor-to-indoor radio channel in the 3.5 GHz band, based on experimental data for commercial, deployed 5G networks, collected during a large scale measurement campaign carried out in the city of Rome, Italy. The analysis presented in this work focuses on downlink, outdoor-to-indoor propagation for two operators adopting two different beamforming strategies, single wide-beam and multiple synchronization signal blocks (SSB) based beamforming; it is indeed the first contribution studying the impact of beamforming strategy in real 5G networks. The time and power-related channel characteristics, i.e., mean excess delay and Root Mean Square (RMS) delay spread, path loss, and K-factor are studied for the two operators in multiple measurement locations. The analysis of time and power-related parameters is supported and extended by a correlation analysis between each pair of parameters. The results show that beamforming strategy has a marked impact on propagation. A single wide-beam transmission leads, in fact, to lower RMS delay spread and lower mean excess delay compared to a multiple SSB-based transmission strategy. In addition, the single wide-beam transmission system is characterized by a smaller path loss and a higher K-factor, suggesting that the adoption of a multiple SSB-based transmission strategy may have a negative impact on downlink performance.

Understanding radio propagation characteristics and developing channel models is fundamental to building and operating wireless communication systems. Among others uses, channel characterization and modeling can be used for coverage and performance analysis and prediction. Within this context, this paper describes a comprehensive dataset of channel measurements performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. Previous efforts to analyze outdoor-to-indoor propagation characteristics in this band were made by using measurements collected on dedicated, mostly single-link setups. Hence, measurements performed on deployed and operational 5G networks still lack in the literature. To fill this gap, this paper presents a dataset of measurements performed over commercial 5G networks. In particular, the dataset includes measurements of channel power delay profiles from two 5G networks in Band n78, i.e., 3.3–3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy by using the Rohde & Schwarz (R&S) TSMA6 network scanner during several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics toward the definition and refinement of empirical channel propagation models.

We examine the connection reliability of LTE cellular infrastructure for supporting train signaling systems. In particular, the impact of simultaneous use of multiple networks on reliability is considered, along with failure correlation effects. We present a tailored reliability model, and report on data collected from many train-mounted cellular routers. Connection reliability reaches 99.994% when aggregation is used, compared to 99.953% for the best single link. Both modeling and measurement results show greatly improved reliability when aggregating over multiple links, thus indicating that commercial cellular networks may be useful for providing connectivity to future train signaling systems.

Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry. In this paper, we present the first large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two Mobile Network Operators (MNOs) in Rome, Italy, to first, study network deployment and radio coverage aspects, and second, explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). Results show that 5G NSA can provide higher downlink throughput and slightly lower latency compared to Fourth Generation (4G) systems. However, performance is influenced by several factors, including propagation conditions, system configurations, and handover events, ultimately highlighting the need for further system optimization.

Networked systems have recently aimed to use multiple access networks in parallel to increase resiliency, availability and capacity. However, different paths may have different latency characteristics, which may lead to out-of-order packet delivery. This may severely impact both the end-to-end application performance and the capacity utilisation of multiaccess systems. In this paper, we show that in-network support for packet reordering for multiaccess systems that are based on multiple transport layer tunnels is beneficial for several application types. Our findings are applicable to TCP and QUIC traffic in the 3GPP ATSSS context, where we use the MP-DCCP tunneling framework with a buffer-based packet reordering approach that uses a dynamic timing threshold to cope with variation of path delays over time. We demonstrate achievable performance gains for a wide range of path latency differences and end-to-end round trip times when using different in-network reordering algorithms.

Fifth Generation (5G) cellular networks are gaining popularity and are soon expected to become the norm in the global telecommunications industry. In this paper, we follow an experimental approach to study key aspects of Handover (HO) events in commercial 5G Non Standalone (NSA) deployments. We collect our dataset by conducting a large-scale measurement campaign in the city of Rome, Italy. With our analysis, we aim to first, empirically evaluate the current HO strategies followed in 5G NSA, and second, to show the impact of HOs on end-user application performance. Our results show an increased number of intra-RAT HOs, i.e., HOs between cells with the same Radio Access Technology (RAT), in situation where signal quality conditions between multiple candidate cells is on par. In addition, we show that inter-RAT HOs, i.e., HOs between cells with different RATs, can be detrimental to the application performance.

Tactile Internet defines applications for remotely controlling and manipulating critical devices that require perceived real-time operation with additional demanding requirements like reliability. These use cases with stringent requirements demand adequate transport protocols to take advantage of the underlying possibilities. Traditional transport-layer solutions like TCP and UDP are no longer sufficient, hence novel protocols are being developed to support these applications. In this paper, we present an implementation and evaluation of the Multi-connection Tactile Internet Protocol (MTIP), a transport layer proposal to support these communications. MTIP uses application and network status information to perform an intelligent selection of paths in order to improve reliability and latency. In our evaluations, we study how the different configurations of the MTIP algorithm affect this selection and we see a direct trade-off where, with more restrictive thresholds, MTIP can increase the packets received correctly at the cost of sending extra duplicate packets.

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, thanks 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 are evaluated on a large-scale dataset that includes experimental data from two NB-IoT operators. Results show 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 finger-printing, with a minimum average positioning error of about 20 meters, consistent across different network scenarios, vs. about 70 meters for current state-of-the-art.