Abstract
Integrated Access and Backhaul (IAB) technology in cellular networks operating in the 3.x GHz band combines access and backhaul functionalities within a wireless framework, reducing dependence on fiber-based solutions and enabling cost-efficient, flexible network expansion. Deploying a mobile IAB (MIAB) in obstruction-prone environments, such as seaports, offers on-demand capacity and resilience but poses unique challenges due to severe shadowing from dense physical obstacles. This paper presents a three-dimensional, obstacle-aware model for optimal MIAB placement and scheduler selection in networks comprising user equipments (UEs) and fixed IABs (FIABs). We evaluate user and backhaul association patterns under different scheduling strategies, including Round-Robin (RR) and Weighted Round-Robin (WRR), ensuring that both MIABs and FIABs meet UE application-layer capacity demands without exceeding backhaul limits. A genetic algorithm (GA)-based optimizer is employed to explore deployment configurations under varying FIAB densities, number of UEs, and obstacles. Results show that MIAB assistance yields the greatest benefits in sparse FIAB networks and low-UE scenarios, with capacity gains reaching up to 350%. MIAB delivers the greatest added value in the presence of obstacles. In contrast, dense FIAB deployments exhibit diminishing returns from MIAB integration. Across most of the evaluated conditions, WRR outperforms RR by enabling fairer and more adaptive resource blocks (RBs) allocation. These findings provide practical guidance for targeted MIAB deployment strategies that balance infrastructure investment, environmental constraints, and scheduling policies.

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This paper proposes FLUC, a modular framework that integrates open-source Large Language Models (LLMs) with Unmanned Aerial Vehicle (UAV) autopilot systems to enable autonomous control in Flying Networks (FNs). FLUC translates high-level natural language commands into executable UAV mission code, bridging the gap between operator intent and UAV behaviour. FLUC is evaluated using three open-source LLMs - Qwen 2.5, Gemma 2, and LLaMA 3.2 - across scenarios involving code generation and mission planning. Results show that Qwen 2.5 excels in multi-step reasoning, Gemma 2 balances accuracy and latency, and LLaMA 3.2 offers faster responses with lower logical coherence. A case study on energy-aware UAV positioning confirms FLUC's ability to interpret structured prompts and autonomously execute domain-specific logic, showing its effectiveness in real-time, mission-driven control.

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.