Abstract
The rapid growth of data traffic and evolving service demands are driving a shift from traditional network architectures to advanced solutions. While 5G networks provide reduced latency and higher availability, they still face limitations due to reliance on integrated hardware, leading to configuration and interoperability challenges. The emerging Open Radio Access Network (O-RAN) paradigm addresses these issues by enabling remote configuration and management of virtualized components through open interfaces, promoting cost-effective, multi-vendor interoperability. Network slicing, a key 5G enabler, allows for tailored network configurations to meet heterogeneous performance requirements. The main contribution of this paper is a private Standalone 5G network based on O-RAN, featuring a dynamic Data Radio Bearer Management xApp (xDRBM) for real-time metric collection and traffic prioritization. xDRBM optimizes resource usage and ensures performance guarantees for specific applications. Validation was conducted in an emulated environment representative of real-world scenarios. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

Abstract
Over the years, mobile networks were deployed using monolithic hardware based on proprietary solutions. Recently, the concept of open Radio Access Networks (RANs), including the standards and specifications from O-RAN Alliance, has emerged. It aims at enabling open, interoperable networks based on independent virtualized components connected through open interfaces. This paves the way to collect metrics and to control the RAN components by means of software applications such as the O-RAN-specified xApps. We propose a private standalone network leveraged by a mobile RAN employing the O-RAN architecture. The mobile RAN consists of a radio node (gNB) carried by a Mobile Robotic Platform autonomously positioned to provide on-demand wireless connectivity. The proposed solution employs a novel Mobility Management xApp to collect and process metrics from the RAN, while using an original algorithm to define the placement of the mobile RAN. This allows for the improvement of the connectivity offered to the User Equipments.

Abstract
Telecommunications and computer vision have evolved separately so far. Yet, with the shift to sub-terahertz (sub-THz) and terahertz (THz) radio communications, there is an opportunity to explore computer vision technologies together with radio communications, considering the dependency of both technologies on Line of Sight. The combination of radio sensing and computer vision can address challenges such as obstructions and poor lighting. Also, machine learning algorithms, capable of processing multimodal data, play a crucial role in deriving insights from raw and low-level sensing data, offering a new level of abstraction that can enhance various applications and use cases such as beamforming and terminal handovers. This paper introduces CONVERGE, a pioneering vision-radio paradigm that bridges this gap by leveraging Integrated Sensing and Communication (ISAC) to facilitate a dual View-to-Communicate, Communicate-to-View approach. CONVERGE offers tools that merge wireless communications and computer vision, establishing a novel Research Infrastructure (RI) that will be open to the scientific community and capable of providing open datasets. This new infrastructure will support future research in 6G and beyond concerning multiple verticals, such as telecommunications, automotive, manufacturing, media, and health.

Abstract
Unmanned Aerial Vehicles (UAVs) are versatile platforms for carrying communications nodes such as Wi-Fi Access Points and cellular Base Stations. Flying Networks (FNs) offer on-demand wireless connectivity where terrestrial networks are impractical or unsustainable. However, managing communications resources in FNs presents challenges, particularly in optimizing UAV placement to maximize Quality of Service (QoS) for Ground Users (GUs) while minimizing energy consumption, given the UAVs' limited battery life. Existing multi-UAV placement solutions primarily focus on maximizing coverage areas, assuming static UAV positions and uniform GU distribution, overlooking energy efficiency and heterogeneous QoS requirements. We propose the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which defines and optimizes UAV trajectories to reduce energy consumption while ensuring QoS based on Signal-to-Noise Ratio (SNR) in the links with GUs. Additionally, we introduce the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate energy consumption. Using both MUAVE and ns-3 simulators, we evaluate SUPPLY in typical and random networking scenarios, focusing on energy consumption and network performance. Results show that SUPPLY reduces energy consumption by up to 25% with minimal impact on throughput and delay.

Abstract
In wireless communications, the need to cover operation areas, such as seaports, is at the forefront of discussion, especially regarding network capacity provisioning. Radio network planning typically involves determining the number of fixed cells, considering link budgets and deploying them geometrically centered across targeted areas. This paper proposes a solution to determine the optimal position for a mobile cell, considering 3GPP pathloss models. The obtained position for the mobile cell maximises the aggregate network capacity offered to a set of User Equipments (UEs), with gains up to 187% compared to the positioning of the mobile cell at the UEs’ geometrical center. The proposed solution can be used by network planners and integrated into network optimisation tools. This has the potential to reduce costs associated with the radio access network planning by enhancing flexibility for on-demand deployments. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.