Despite 5G’s promise of enhanced capacity and lower latency, recent measurement studies have shown that users experience significant cellular performance degradation under vehicular mobility. In this paper, we go beyond prior work that merely characterizes 5G performance, by uncovering the underlying mechanisms responsible for throughput degradation episodes during vehicular mobility. Through extensive measurements across 3,687 km of driving routes in the U.S., we collect granular performance and network KPI data from three major carriers across diverse radio access technologies. We introduce a novel KPI-driven clustering methodology that not only quantifies the frequency and duration of performance degradations, but also critically identifies their root causes by analyzing KPIs and their interactions. Our analysis reveals previously unidentified patterns: persistent higher degradation rates in uplink versus downlink, technology-specific vulnerability signatures in LTE and 5G deployments, and the predominance of compound degradation mechanisms where multiple factors interact to create severe throughput reductions. These findings provide essential information for developing mobility-aware resource management strategies to address the unique challenges of vehicular connectivity.

We demonstrate a novel agentic control architecture for dynamic edge network reconfiguration, featuring a dual—Model Context Protocol (MCP) deployment: one instance at the gNodeB and another at the User Equipment (UE), coordinated via a shared client interface. A large language model (LLM) receives high-level user intent (e.g., "improve video call stability") and constructs a semantic action plan conditioned on real-time context and validated operational constraints. The system enables collaborative reasoning across the UE and RAN components, with human-in-the-loop authorization governing execution. Unlike conventional APIs or policy engines, MCP supports reversibility, semantic validation, and learning from feedback. This demonstration highlights the potential for intent-driven, context-aware, verifiable autonomy in edge wireless systems.

Accurate localization in dense urban areas remains a significant challenge due to the limitations of Global Navigation Satellite Systems (GNSS) in environments with obstacles and reflections, such as urban canyons. While the most recent 3GPP standards offer sophisticated network-centric positioning techniques, their widespread deployment will take time and is hindered by high infrastructure costs and complexity. In this work, we present mm-NOLOC, a UE-centric localization system, designed as a practical fallback when GNSS fails to deliver high accuracy, that leverages the growing deployment of 5G mmWave infrastructure in dense urban areas. Unlike traditional approaches, mm-NOLOC operates independently of 3GPP location support and utilizes only standardized control-plane information collected solely on the UE side – Synchronization Signal Block (SSB) Indices that are mapped to 5G mmWave beam directions – to obtain robust position estimations. To address the uncertainty introduced by urban multipath, mm-NOLOC models the SSB-to-angle relationship as a discrete and multimodal distribution, based on empirical measurements in operational 5G mmWave networks, and uses a particle filter to refine position estimates by integrating probabilistic observations with UE-side motion dynamics. We validate mm-NOLOC through experiments over commercial 5G mmWave deployments, as well as trace-based simulations. Our results show that mm-NOLOC achieves a median localization error below 3 m and a 95th percentile error below 10 m, offering a practical fallback localization solution in urban canyon scenarios for 5G networks without network location support.

As Fifth generation (5G) cellular systems transition to softwarized, programmable, and intelligent networks, it becomes fundamental to enable public and private 5G deployments that are (i) primarily based on software components while (ii) maintaining or exceeding the performance of traditional monolithic systems and (iii) enabling programmability through bespoke configurations and optimized deployments. This requires hardware acceleration to scale the Physical (PHY) layer performance, programmable elements in the Radio Access Network (RAN) and intelligent controllers at the edge, careful planning of the Radio Frequency (RF) environment, as well as end-to-end integration and testing. In this paper, we describe how we developed the programmable X5G testbed, addressing these challenges through the deployment of the first 8-node network based on the integration of NVIDIA Aerial RAN CoLab Over-the-Air (ARC-OTA), OpenAirInterface (OAI), and a near-real-time RAN Intelligent Controller (RIC). The Aerial Software Development Kit (SDK) provides the PHY layer, accelerated on Graphics Processing Unit (GPU), with the higher layers from the OAI open-source project interfaced with the PHY through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). An E2 agent provides connectivity to the O-RAN Software Community (OSC) nearreal-time RIC. We discuss software integration, network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, as well as up to 25 emulated User Equipments (UEs), measuring a cell rate higher than 1.65 Gbps in downlink and 143 Mbps in uplink.

Open Radio Access Networks (O-RAN) are expected to revolutionize the telecommunications industry with benefits like cost reduction, vendor diversity, and improved network performance through AI optimization. Supporting the O-RAN ALLIANCE’s mission to achieve more intelligent, open, virtualized and fully interoperable mobile networks, O-RAN Open Testing and Integration Centers (OTICs) play a key role in accelerating the adoption of O-RAN specifications based on rigorous testing and validation. One theme in the recent O-RAN Global PlugFest Spring 2024 focused on demonstrating consistent and repeatable Open Fronthaul testing in multiple labs. To respond to this topic, in this paper, we present a detailed analysis of the testing methodologies and results for O-RAN Distributed Unit (O-DU) in O-RAN across two OTICs. We identify key differences in testing setups, share challenges encountered, and propose best practices for achieving repeatable and consistent testing results. Our findings highlight the impact of different deployment technologies and testing environments on performance and conformance testing outcomes, providing valuable insights for future O-RAN implementations.

Information and communications technologies are increasingly present in sports’ execution, being implemented in a huge variety of tools used to improve player performances and/or the decision-making of referees. However, the position and movement of the players, being an essential information variable, is still not widely measured by current systems, especially at indoors and outside the high-competition. To address this, the present work focuses on the application of positioning techniques for indoor sports. In this way, a system employing Ultra-Wideband based radio-positioning is designed and implemented to locate players in real time. The performance of such system is then evaluated in a real environment, considering several trajectories with direction changes and followed in different conditions.

The generalization in the use of virtualization in the upcoming generation of cellular networks involves new paradigms and approaches for their management. The correct sharing of the underlying resources between multiple virtualized network functions as well as any other processes sharing the same computational platform implies a complex architecture that makes virtualization challenging. As a result, numerous variables (e.g., computational capacity) were mostly ignored by previous management systems, but that can now lead to relevant impacts on the service performance. In this virtualized scenario with multiple coexistent processes over the same hardware, a “Noisy Neighbour” (NN) is identified as an entity that uses most of the underlying resources while other virtual units suffer a lack of them. While difficult to identify, such situations can affect the network service. In this context, the present work analyzes and assesses the NN problem for 5G Core scenarios. A complete emulated 5G network and analysis framework is defined and developed to evaluate the impact of a noisy entity. In this way, the degradation that Key Performance Indicators suffer in the network and by the end-users when a NN appears is assessed. Thus, the present work proposes a baseline for handling NN through a novel lifecycle management flow. For this purpose, it is evaluated the effectiveness of multiple Machine Learning (ML) models for the identification of NN, based on the metrics gathered from the proposed framework, achieving 99% of accuracy. Moreover, ML is applied to develop a method for network performance inference, along with a prediction model to forecast the number of CPU resources the network may demand at any given time supporting the proposed management flow.

Extended Reality (XR) arises as one of the current cutting-edge educational and entertainment emergent technologies. This service differs from traditional video streaming approaches due to its immersive experience, which allows the user to enjoy omnidirectional multimedia. However, the service experience must be guaranteed to avoid side effects such as cybersickness or disorientation. This work presents a framework to assess 360-video service streaming performance over mobile networks through Key Quality Indicators (KQIs) using VR (Virtual Reality) HMD (Head Mounted Device). The testbed is composed of a 360-video client for DASH (Dynamic Adaptive Streaming over HTTP), which playbacks multimedia content from a video server located in the cloud while KQI measuring tasks are performed in the user as on the network sides. Various metrics are collected such as resolution, frame rate, initial playback time, throughput, stall events, and round trip time (RTT), among various others. Finally, a performance comparison between LTE and 5G technologies is provided. Results from the KQI measurement highlight the potential of the new generation of mobile networks in the provision of service with high-quality levels of experience.