Abstract

Abstract
Telecommunications and computer vision solutions have evolved significantly in recent years, allowing a huge advance in the functionalities and applications offered. However, these two fields have been making their way as separate areas, not exploring the potential benefits of merging the innovations brought from each of them. In challenging environments, for example, combining radio sensing and computer vision can strongly contribute to solving problems such as those introduced by obstructions or limited lighting. Machine learning algorithms, able to fuse heterogeneous and multi-modal data, are also a key element for understanding and inferring additional knowledge from raw and low-level data, able to create a new abstracting level that can significantly enhance many applications. This paper introduces the CONVERGE vision-radio concept, a new paradigm that explores the benefits of integrating two fields of knowledge towards the vision of View-to-Communicate, Communicate-to-View. The main concepts behind this vision, including supporting use cases and the proposed architecture, are presented. CONVERGE introduces a set of tools integrating wireless communications and computer vision to create a novel experimental infrastructure that will provide open datasets to the scientific community of both experimental and simulated data, enabling new research addressing various 6 G verticals, including telecommunications, automotive, manufacturing, media, and health.

Abstract
This position paper presents A4FN, an Agentic Artificial Intelligence (AI) architecture for intent-driven automation in Flying Networks (FNs) using Unmanned Aerial Vehicles (UAVs) as access nodes. A4FN leverages Generative AI and Large Language Models (LLMs) to enable real-time, context-aware network control via a distributed agentic system. It comprises two components: the Perception Agent (PA), which semantically interprets multimodal input - including imagery, audio, and telemetry data - from UAV-mounted sensors to derive Service Level Specifications (SLSs); and the Decision-and-Action Agent (DAA), which reconfigures the network based on inferred intents. A4FN embodies key properties of Agentic AI, including autonomy, goal-driven reasoning, and continuous perception-action cycles. Designed for mission-critical, infrastructure-limited scenarios such as disaster response, it supports adaptive reconfiguration, dynamic resource management, and interoperability with emerging wireless technologies. The paper details the A4FN architecture, its core innovations, and open research challenges in multi-agent coordination and Agentic AI integration in next-generation FNs. © 2025 IEEE.

Abstract
This paper proposes an optimized Reconfigurable Internet of Things (RIoT) framework that integrates optical and radio wireless technologies with a focus on energy efficiency, scalability, and adaptability. To address the inherent complexity of hybrid optical–radio environments, a high-fidelity Digital Twin (DT) is developed within the Network Simulator 3 (NS-3) platform. The DT models deploy subsystems of the RIoT architecture, including Radio Frequency (RF) communication, Optical Wireless Communication (OWC), and energy harvesting and consumption mechanisms that enable autonomous operation. Real-time energy and power measurements from target hardware platforms are also incorporated to ensure accurate representation of physical behavior and enable runtime analysis and optimization. Building on this foundation, a proactive cross-layer optimization strategy is devised to balance energy efficiency and quality of service (QoS). The strategy dynamically reconfigures RIoT nodes by adapting transmission rates, wake/sleep scheduling, and access technology selection. Results demonstrate that the proposed framework, combining digital twin technology, hybrid optical–radio integration, and data-driven energy modeling, substantially enhances the performance, resilience, and sustainability of 6G IoT networks. © 2020 IEEE.

Abstract
The increasing complexity of wireless technologies, such as Wi-Fi, presents significant challenges for Rate Adaptation (RA) due to the large configuration space of transmission parameters. While extensive research has been conducted on RA for low-mobility networks, existing solutions fail to adapt in Flying Networks (FNs), where high mobility and dynamic wireless conditions introduce additional uncertainty. We propose Linear Upper Confidence Bound for RA (LinRA), a novel Contextual Bandit-based approach that leverages real-time link context to optimize transmission rates in predictable FNs, where future trajectories are known. Simulation results demonstrate that LinRA converges $\mathbf {5.2\times }$ faster than benchmarks and improves throughput by 80% in Non Line-of-Sight conditions, matching the performance of ideal algorithms.