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

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.

Abstract
Unmanned Aerial Vehicles (UAVs) are suited as cost-effective and adaptable platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations (BSs). Implementing aerial networks in disaster management scenarios and crowded areas can effectively enhance Quality of Service (QoS). Maintaining Line-of-Sight (LoS) in such environments, especially at higher frequencies, is crucial for ensuring reliable communication networks with high capacity, particularly in environments with obstacles. The main contribution of this paper is a traffic- and obstacle-aware UAV positioning algorithm named Reinforcement Learning-based Traffic and Obstacle-aware Positioning Algorithm (RLTOPA), for such environments. RLTOPA determines the optimal position of the UAV by considering the positions of ground users, the coordinates of obstacles, and the traffic demands of users. This positioning aims to maximize QoS in terms of throughput by ensuring optimal LoS between ground users and the UAV. The network performance of the proposed solution, characterized in terms of mean delay and throughput, was evaluated using the ns-3 simulator. The results show up to 95% improvement in aggregate throughput and 71% in delay without compromising fairness.

Abstract
The Integrated Access and Backhaul (IAB) 5G network architecture, introduced in 3GPP Release 16, leverages a shared 5G spectrum for both access and backhaul networks. Due to the novelty of IAB, there is a lack of suitable implementations and performance evaluations. This paper addresses this gap by proposing EMU-IAB, a mobility emulator for private standalone 5G IAB networks. The proposed emulation environment comprises a 5G Core Network, an IAB-enabled Radio Access Network (RAN), leveraging the Open-RAN (O-RAN) architecture. The RAN includes a fixed IAB Donor, a mobile IAB Node, and multiple User Equipments (UEs). The mobility of the IAB Node is managed through EMU-IAB, which allows defining the path loss of emulated wireless channels. The validation of EMU-IAB was conducted under a realistic IAB node mobility scenario, addressing different traffic demand from the UEs.

Abstract
Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient's travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient's body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research.