Diagnosing performance issues in 5G and future disaggregated networks remains challenging due to the complexity of the protocol stack and the multitude of interdependent metrics. This is especially true in experimental environments using open-source 5 G software, where logs are often verbose, fragmented, and difficult to interpret. Real-time visual observability, especially at the Radio Access Network (RAN) level, is therefore essential for effective troubleshooting. However, tools like 5GC-Observer and Monarch provide only partial support, focusing mainly on the core network and lacking visibility into the RAN, where many performance bottlenecks originate. To tackle this, we present a Prometheus-compatible telemetry pipeline for the OpenAirInterface (OAI) gNB that extracts RAN metrics, adjusts acquisition intervals via a custom FlexRIC model, and visualizes the data in real time with Grafana. The pipeline is also compatible with SRSRAN through a lightweight Telegraf-Prometheus integration, extending its use to multiple open-source stacks. We integrate our solution into the Monarch monitoring architecture for cloud-native 5G, enabling end-to-end observability. An Ansible-based automation simplifies testbed setup and ensures reproducible experimentation. Validation on a realistic testbed shows real-time RAN metrics exposure with negligible overhead.

Diagnosing performance degradations in 5G networks remains challenging due to the limited visibility of internal RAN behavior in open-source deployments, where radio impairments, such as interference, directly affect performance and are difficult to observe. This demo presents a real-time RAN observability framework for open-source 5 G systems and demonstrates its effectiveness under controlled radio interference. The solution extends Monarch's existing cloud-native monitoring stack with a non-intrusive, Prometheus-compatible telemetry pipeline for the OAI gNB, exposing fine-grained, per-UE metrics from the physical, MAC, and RRC layers and visualizing them live in Grafana. The system is deployed automatically using an Ansible-based framework and validated on a controlled testbed with real radio hardware, where interference-induced effects can be observed and correlated in real time, enabling rapid and practical diagnosis of RAN performance degradation.

Short-form video platforms dominate online content consumption through powerful recommendation systems, yet most research relies on simulated or limited datasets that do not reflect real-world behavior. This demo introduces SWiPE X, a browser extension designed to anonymously collect and analyze user interactions with short-form videos (e.g., YouTube Shorts). The system captures fine-grained behavioral signals alongside Quality of Experience (QoE) metrics to enable the study of video traffic efficiency, recommendation performance, and user engagement. Attendees will be able to interact directly with the system and explore real-time analytics, illustrating how everyday viewing behavior can be transformed into large-scale, privacy-preserving research insights.

Short videos have become one of the most popular forms of content consumption on mobile devices. Unlike traditional video streaming, users often watch many short videos within a single session and frequently swipe away videos after only a brief viewing period. This interaction pattern can lead to significant data waste, where video content is downloaded but never viewed. In this paper, we systematically investigate data waste in short video streaming and propose an experimental methodology to quantify its magnitude and underlying causes. Using YouTube Shorts as a case study, we design multiple controlled viewing scenarios to measure the fraction of downloaded video data that is not watched. We further analyze how video properties, content categories, and the platform’s buffering and prefetching strategies contribute to this waste. Our results reveal substantial inefficiencies in short video delivery, highlighting opportunities for improving data efficiency in short-form video streaming systems.

5G networks have the potential for ultra-low latency communication, supporting a broad array of critical and delay-sensitive applications. However, end-to-end performance prediction in 5G scenarios is still a challenging problem, given the complexity of the system architecture and the interconnection of a multitude of interrelated parameters that contribute to delay. In this work, we report an experiment-driven investigation on the basis of an actual standalone 5G deployment to better understand how interdependent configuration parameters, such as TDD configuration and scheduling request (SR) settings, contribute to round-trip time (RTT). For instance, in a 2 ms TDD configuration, RTT was reduced by 30% after aligning SR periodicity with the frame structure, highlighting the decisive impact of scheduling configuration. Through the integration of fine-grained measurement and delay component analysis, we reveal the major contributors to latency and highlight the crucial role of empirical assessment and careful parameter tuning in realizing dependable low-latency operation in 5G networks.

The design and development of new network protocols, architectures, and technologies requires an evaluation phase where the researcher must provide empirical evidence for the performance of their contributions, potentially in comparison to existing solutions. In this context, network emulation has proven to be an attractive approach as it offers more flexibility compared to traditional testing platforms, and more realism compared to simulation. Network emulators provide contained, customisable, and scalable testing environments both for researchers to evaluate their contributions and for the community to reproduce their results. However, two limitations to network emulation have been identified and well documented in the literature: its scalability limits and its accuracy issues. This paper1 documents our attempts to address these concerns. Our findings are distilled into Hifinet: a lightweight scalable and fidelity-aware distributed network emulator. We particularly show how Hifinet outperforms its state-of-the-art counterparts in terms of scalability and efficiency by working around the flaws of their design principles and the technological limitations of the tools they rely on. Hifinet is also fidelity-enhanced, in that it implements a well-theorised fidelity monitoring framework, which passively monitors emulated packet delays to evaluate realism of network emulation and accuracy of results. Another asset of Hifinet is its ability to infer underlying causes in case of erroneous emulation and guide the user through fixing them. This is achieved by using delay tomography algorithms and heuristics.

Distributed network emulators allow users to perform network evaluation by running large-scale virtual networks over a cluster of fewer machines. While they offer accessible testing environments for researchers to evaluate their contributions and for the community to reproduce its results, their use of limited physical network and compute resources can silently and negatively impact the emulation results. In this paper, we present a methodology that uses linear optimization to extract information about the physical infrastructure from emulation-level packet delay measurements, in order to pinpoint the root causes of emulation inaccuracy with minimal hypotheses. We evaluate the precision of our methodology using numerical simulations and then show how its implementation performs in a real network scenario.

Accurate estimation of the signal power received at a given location can be obtained through Ray Tracing (RT) propagation model. Nevertheless, processes involved in RT are extremely complex and not adapted for Radio Frequency (RF) mapping purposes. Particularly, the reception test process, responsible for validating received rays at given locations, contributes to over 98% of the overall RT complexity in large-scale scenarios. In this paper, we introduce a novel RT algorithm able to divide by a factor of 50 the RF map rendering time while limiting the difference in bitrate estimation to less than 1Mbps on average as compared to existing approaches. Our algorithm integrates hardware acceleration techniques for RT, proposes a new modeling of the terrain in the form of a triangular mesh, and introduces a new reception test able to efficiently calculate the signal power received per each triangle of the mesh. Our simulations show how these processes together can reduce the complexity of RF map generation, hence allowing the algorithm to scale to large-scale and precise scenarios.

Ray Tracing is an electromagnetic wave propagation modeling approach used for accurate generation of Quality of Service (QoS) maps in mobile networks. Due to its complexity, current implementation of Ray Tracing fails to generate such maps in wide areas. In this paper, we propose an optimization to Ray Tracing able to accurately generate QoS maps in a reasonable time. Using a site-specific ray launching technique and an alternative to the reception test process, we divide by almost 1200 the execution time of Ray Tracing with less than 2% of memory usage as compared to baseline solutions.

Ray Tracing is a propagation modeling approach that accurately estimates the signal power received by end users while considering the details of the environment in their vicinity. The accuracy of this estimation is at the cost of high computational load and high memory consumption due to the heavy computation performed by processes such as the Ray Generation. In this paper, we introduce a site-specific ray generation technique able to generate up to 1 million rays within 5 seconds and a root mean square error for bandwidth estimation within 2 Mbps. Depending on the location of the antenna, the coverage area, the type of the terrain and the computational resources available, our technique gives the minimum possible number of rays required to accurately estimate end-users' signal power received and their download bitrate.

Distributed Network emulators (e.g., Mininet Cluster Edition) have proven to be an attractive solution to perform extreme-scale network and systems evaluation on smaller-size testbeds and experiment platforms. They can provide contained, customisable, and scalable testing environments for researchers to evaluate their contributions and reproduce their results. The major drawback of this approach in network experimentation is the use of virtual components (hosts, network switches, etc.) that do not behave with perfect similarity to the physical components they emulate, mainly due to the concurrency in using the underlay network and computing resources. We thus present in this paper a methodology to monitor emulation fidelity by measuring the network delays of emulated packets, which relies on statistical metrics to evaluate their inaccuracy. We further dig into the possible sources of emulation inaccuracy and show how our system can detect them to avoid biased experiment results. We particularly show through a common experiment scenario how undetected network emulation errors can lead to biased results.

Connected autonomous vehicles technology is expected to be an important component of Intelligent Transportation Systems (ITS). With the help of artificial intelligence, cognitive methods, software-hardware and sensor platforms, several relevant industry standards are being developed to enable connected vehicles. One significant challenge raised by connected vehicles is how the underlying communication infrastructure will be able to efficiently support them. In this article, we propose the Dynamic, Decentralized, Distributed, Delegation-based (D4) network control plane architecture, which aims at providing adequate communication support for connected vehicles. To our knowledge, D4 is the first framework to support on-demand network control decentralization. At its core, D4’s flexible network control delegation framework allows network control to be dynamically distributed on demand to address quality-of-service requirements of different connected vehicle services and applications. We demonstrate the benefits of D4 through a proof-of-concept prototype running on a fully reproducible emulation platform. Experimental results using a variety of real-world scenarios demonstrate that D4 delivers lower latency with minimal additional overhead. We also showcase the benefits of D4 when compared to traditional vehicular ad hoc network (VANET) approaches.

5G enhanced Mobile broadband (eMBB) aims to provide users with a peak data rate of 20 Gbps in the Radio Access Network (RAN). However, since most Congestion Control Algorithms (CCAs) rely on startup and probe phases to discover the bottleneck bandwidth, they cannot quickly utilize the available RAN bandwidth and adapt to fast capacity changes without introducing large delay increase, especially when multiple flows are sharing the same Radio Link Control (RLC) buffer. To tackle this issue, we propose RAPID, a RAN-aware proxy-based flow control mechanism that prevents CCAs from overshooting more than the available RAN capacity while allowing near optimal link utilization. Based on analysis of up-to-date radio information using Multi-access Edge Computing (MEC) services and packet arrival rates, RAPID is able to differentiate slow interactive flows from fast download flows and allocate the available bandwidth accordingly. Our simulation and experimentation results with concurrent Cubic and BBR flows show that RAPID can reduce delay increase by a factor of 10 to 50 in both Line-of-Sight (LOS) and Non-LOS (NLOS) conditions while preserving high throughput in both 4G and 5G environments.

Emulation has become a popular approach for the validation and evaluation of network research. It provides researchers with a contained, customizable, and scalable testing environment, which can be easily packaged and published for potential readers to reproduce their results. However, as the network components are only virtual, emulation lacks the inherent realism of physical testbeds. In light of this, monitoring specific metrics of the emulated network has been proposed as a solution to mitigate to some degree inaccuracies caused by emulation. While this is not difficult to implement in a single-machine setting (e.g. with M ininet), monitoring is limited by the lack of time synchronization in scenarios where the emulation is distributed over multiple physical machines (e.g., D istrinet). In this paper we tackle the case of packet delay monitoring, to which we propose a methodology for passively measuring one-way delays with underlying assumptions about time synchronization, and round-trip delays otherwise. For an efficient implementation of our methodology, we propose an e BPF-based packet measurement tool that performs better than current packet sniffers under emulation-specific assumptions. We implement and evaluate our system in an open testbed and show that it can reach results within few microseconds of perfect accuracy and precision.

New cellular technologies combine complex software stacks and heterogeneous hardware. As a result, even if models and simulators are still essential to understand them, validation must go to the next level and rely on real software and hardware testbeds. Unfortunately, building comprehensive testbeds is expensive and this is why we propose the Sophia-node, a cloud-native cellular network testbed accessible to the community. In this demonstration, we show how to run arbitrary *G experiments in this testbed by following DevOps techniques.

The design and development of new network protocols, architectures, and technologies require an evaluation phase where the researcher must provide empirical evidence for the performance of their contributions. In this context, network emulation has proven to be an attractive approach as it offers more flexibility compared to traditional testing platforms, and more realism compared to simulation software. However, as emulation requires running multiple concurrent virtual network components on a limited amount of resources, it faces well-documented limitations in its scalability and accuracy. Our work is an attempt to address these issues by theorising the concept of emulation fidelity and developing a framework for its monitoring through passive measurement of emulated packet delays. The preliminary evaluation shows promising results and demonstrates the high potential of our approach.

Ray Tracing is a propagation modelling approach that accurately estimates the signal power received by end users while taking into account the details of the environment in their vicinity. This accuracy is at the cost of high computational load and high memory consumption due to the heavy computation performed by processes such as Ray Generation. In this paper, we introduce a site-specific ray generation technique able to generate up to 1 million rays within 5 seconds and a root mean square error for bandwidth estimation within 2 Mbps. Depending on the location of the antenna and the coverage area, our technique gives the minimum possible number of rays required in order to estimate end-users' signal power received and their download bitrate.

Intelligent transportation systems, or ITS for short, includes a variety of services and applications such as road traffic management, traveler information systems, public transit system management, and autonomous vehicles, to name a few. ITS are expected to be an integral part of urban planning and future smart cities, contributing to improved road and traffic safety, transportation and transit efficiency, as well as to increased energy efficiency and reduced environmental pollution. On the other hand, ITS pose a variety of challenges due to its scalability and diverse quality-of-service needs, as well as the massive amounts of data it will generate. In this survey, we explore the use of machine learning (ML), which has recently gained significant traction, to enable ITS. We provide a thorough survey of the current state-of-the-art of how ML technology has been applied to a broad range of ITS applications and services, such as cooperative driving and road hazard warning, and identify future directions for how ITS can further use and benefit from ML technology.

5G enhanced Mobile broadband (eMBB) aims to provide users with a peak data rate of 20 Gbps in the Radio Access Network (RAN). However, since most Congestion Control Algorithms (CCAs) rely on startup and probe phases to discover the bottleneck bandwidth, they cannot quickly utilize the available RAN bandwidth and adapt to fast capacity changes without introducing large delay increase, especially when multiple flows are sharing the same Radio Link Control (RLC) buffer. To tackle this issue, we propose RAPID, a RAN-aware proxy-based flow control mechanism that prevents CCAs from overshooting more than the available RAN capacity while allowing near optimal link utilization. Based on analysis of up-to-date radio information using Multi-access Edge Computing (MEC) services and packet arrival rates, RAPID is able to differentiate slow interactive flows from fast download flows and allocate the available bandwidth accordingly. Our experiments with concurrent Cubic and BBR flows show that RAPID can reduce delay increase by a factor of 10 to 50 in both Line-of-Sight (LOS) and Non-LOS (NLOS) conditions while preserving high throughput.

Traditional loss-based Congestion Control Algorithms (CCAs) suffer from performance issues over wireless networks mostly due to their inability to distinguish wireless random losses from congestion losses. Different loss discrimination algorithms have been proposed to tackle this issue but they are not efficient for 4G networks since they do not consider the impact of various link layer mechanisms such as adaptive modulation and coding and retransmission techniques on congestion in LTE Radio Access Networks (RANs). We propose MELD (MEC-based Edge Loss Discrimination), a novel server-side loss discrimination mechanism that leverages recent advancements in Multi-access Edge Computing (MEC) services to discriminate packet losses based on real-time RAN statistics. Our approach collects the relevant radio information via MEC's Radio Network Information Service and uses it to correctly distinguish random losses from congestion losses. Our experimental study made with the QUIC transport protocol shows over 80% higher goodput when MELD is used with NewReno and 8% higher goodput when used with Cubic.