arXiv subjects

Rui Campos

Publications and source records attributed to Rui Campos.

At least 19 recordsLinked to original sources

arXiv2026

Voice-Driven Semantic Perception for UAV-Assisted Emergency Networks

Unmanned Aerial Vehicle (UAV)-assisted networks are increasingly foreseen as a promising approach for emergency response, providing rapid, flexible, and resilient communications in environments where terrestrial infrastructure is degraded or unavailable. In such scenarios, voice radio communications remain essential for first responders due to their robustness; however, their unstructured nature prevents direct integration with automated UAV-assisted network management. This paper proposes SIREN, an AI-driven framework that enables voice-driven perception for UAV-assisted networks. By integrating Automatic Speech Recognition (ASR) with Large Language Model (LLM)-based semantic extraction and Natural Language Processing (NLP) validation, SIREN converts emergency voice traffic into structured, machine-readable information, including responding units, location references, emergency severity, and Quality-of-Service (QoS) requirements. SIREN is evaluated using synthetic emergency scenarios with controlled variations in language, speaker count, background noise, and message complexity. The results demonstrate robust transcription and reliable semantic extraction across diverse operating conditions, while highlighting speaker diarization and geographic ambiguity as the main limiting factors. These findings establish the feasibility of voice-driven situational awareness for UAV-assisted networks and show a practical foundation for human-in-the-loop decision support and adaptive network management in emergency response operations.

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arXiv2026

Hybrid Deep Learning Framework for CSI-Based Activity Recognition in Bandwidth-Constrained Wi-Fi Sensing

This paper presents a novel hybrid deep learning framework designed to enhance the robustness of CSI-based Human Activity Recognition (HAR) within bandwidth-constrained Wi-Fi sensing environments. The core of our proposed methodology is a preliminary Doppler trace extraction stage, implemented to amplify salient motion-related signal features before classification. Subsequently, these enhanced inputs are processed by a hybrid neural architecture, which integrates Inception networks responsible for hierarchical spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks that capture temporal dependencies. A Support Vector Machine (SVM) is then utilized as the final classification layer to optimize decision boundaries. The framework's efficacy was systematically validated using a public dataset across 20, 40, and 80 MHz bandwidth configurations. The model yielded accuracies of 89.27% (20 MHz), 94.13% (40 MHz), and 95.30% (80 MHz), respectively. These results confirm a marked superiority over standalone deep learning baselines, especially in the most constrained low-bandwidth scenarios. This study underscores the utility of combining Doppler-based feature engineering with a hybrid learning architecture for reliable HAR in bandwidth-limited wireless sensing applications.

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arXiv2025

Design and Evaluation of a Multi-Agent Perception System for Autonomous Flying Networks

Autonomous Flying Networks (FNs) are emerging as a key enabler of on-demand connectivity in dynamic and infrastructure-limited environments. However, current approaches mainly focus on UAV placement, routing, and resource management, neglecting the autonomous perception of users and their service demands - a critical capability for zero-touch network operation. This paper presents the Multi-Agent Perception System (MAPS), a modular and scalable system that leverages multi-modal large language models (MM-LLMs) and agentic Artificial Intelligence (AI) to interpret visual and audio data collected by UAVs and generate Service Level Specifications (SLSs) describing user count, spatial distribution, and traffic demand. MAPS is evaluated using a synthetic multimodal emergency dataset, achieving user detection accuracies above 70% and SLS generation under 130 seconds in 90% of cases. Results demonstrate that combining audio and visual modalities enhances user detection and show that MAPS provides the perception layer required for autonomous, zero-touch FNs.

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arXiv2025

Edge-Enabled UAV Swarm Deployment for Rapid Post-Disaster Search and Rescue

This paper presents an optimized Joint Radar-Communication (JRC) system utilizing multiple Unmanned Aerial Vehicles (UAVs) to simultaneously achieve sensing and communication objectives. By leveraging UAVs equipped with dual radar and communication capabilities, the proposed framework aims to maximize radar sensing performance across all UAVs in challenging environments. The proposed approach focuses on formulating and solving a UAV positioning and power allocation problem to optimize multi-UAV sensing and communications performance over multiple targets within designated zones. Due to the NP-hard and combinatorial nature of the problem, we propose a Distributed JRC-based (DJRC) solution. This solution employs an efficient reward for potential actions and consistently selects the best action that maximizes the reward while ensuring both communications and sensing performance. Simulation results demonstrate significant performance improvements of the proposed solution over state-of-the-art radar- or communication-centric trajectory planning methods, with polynomial complexity dependent on the number of UAVs and linear dependence on the iteration count.

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arXiv2025

A4FN: an Agentic AI Architecture for Autonomous Flying Networks

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.

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arXiv2025

Modular Design and Experimental Evaluation of 5G Mobile Cell Architectures Based on Overlay and Integrated Models

This paper presents the concept, architectural design, and performance evaluation of a 5G Mobile Cell (MC) used to provide 5G wireless connectivity to User Equipment (UE) in areas with limited fixed 5G infrastructures or subject to adverse radio conditions. We consider two main approaches to MC design: an overlay model, where the MC obtains backhaul connectivity from a 5G overlay network, and an Integrated Access and Backhaul (IAB)-based model, discussing their protocol stacks and architectural implications. In order to validate the MC's performance, we employ an emulation-based testbed using the OpenAirInterface (OAI) implementation, considering different MC positions. The results validate the MC concept and demonstrate that MC positioning significantly influences network performance. This paper has the potential to aid network operators and service providers in selecting and deploying MC architectures for temporary coverage extension and capacity reinforcement in different environments, including seaports, industrial scenarios, and public safety.

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arXiv2025

On the Resilience of Underwater Semantic Wireless Communications

Underwater wireless communications face significant challenges due to propagation constraints, limiting the effectiveness of traditional radio and optical technologies. Long-range acoustic communications support distances up to a few kilometers, but suffer from low bandwidth, high error ratios, and multipath interference. Semantic communications, which focus on transmitting extracted semantic features rather than raw data, present a promising solution by significantly reducing the volume of data transmitted over the wireless link. This paper evaluates the resilience of SAGE, a semantic-oriented communications framework that combines semantic processing with Generative Artificial Intelligence (GenAI) to compress and transmit image data as textual descriptions over acoustic links. To assess robustness, we use a custom-tailored simulator that introduces character errors observed in underwater acoustic channels. Evaluation results show that SAGE can successfully reconstruct meaningful image content even under varying error conditions, highlighting its potential for robust and efficient underwater wireless communication in harsh environments.

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arXiv2025

Autonomous Vision-Aided UAV Positioning for Obstacle-Aware Wireless Connectivity

Unmanned Aerial Vehicles (UAVs) offer a promising solution for enhancing wireless connectivity and Quality of Service (QoS) in urban environments, acting as aerial Wi-Fi access points or cellular base stations. Their flexibility and rapid deployment capabilities make them suitable for addressing infrastructure gaps and traffic surges. However, optimizing UAV positions to maintain Line of Sight (LoS) links with ground User Equipment (UEs) remains challenging in obstacle-dense urban scenarios. This paper proposes VTOPA, a Vision-Aided Traffic- and Obstacle-Aware Positioning Algorithm that autonomously extracts environmental information -- such as obstacles and UE locations -- via computer vision and optimizes UAV positioning accordingly. The algorithm prioritizes LoS connectivity and dynamically adapts to user traffic demands in real time. Evaluated through simulations in ns-3, VTOPA achieves up to a 50% increase in aggregate throughput and a 50% reduction in delay, without compromising fairness, outperforming benchmark approaches in obstacle-rich environments.

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arXiv2025

Joint Optimization of Multi-UAV Deployment and 3D Positioning in Traffic-Aware Aerial Networks

Unmanned Aerial Vehicles (UAVs) have emerged as a key enabler for next-generation wireless networks due to their on-demand deployment, high mobility, and ability to provide Line-of-Sight (LoS) connectivity. These features make UAVs particularly well-suited for dynamic and mission-critical applications such as intelligent transportation systems and emergency communications. However, effectively positioning multiple UAVs in real-time to meet non-uniform, time-varying traffic demands remains a significant challenge, especially when aiming to optimize network throughput and resource utilization. In this paper, we propose an Efficient Multi-UAV Traffic-Aware Deployment (EMTAD) Algorithm, a scalable and adaptive framework that dynamically adjusts UAV placements based on real-time user locations and spatial traffic distribution. In contrast to existing methods, EMTAD jointly optimizes UAV positioning and minimizes the number of deployed UAVs, ensuring efficient UE-UAV association while satisfying the traffic demand of users. Simulation results demonstrate that EMTAD significantly improves network performance while reducing deployment overhead by minimizing the number of UAVs required in dynamic and traffic-aware environments.

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arXiv2025

A Framework Leveraging Large Language Models for Autonomous UAV Control in Flying Networks

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.

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arXiv2025

Context-aware Rate Adaptation for Predictive Flying Networks using Contextual Bandits

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, 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. Designed for predictive flying networks, where future trajectories are known, LinRA proactively adapts to obstacles affecting channel quality. Simulation results demonstrate that LinRA converges $\mathbf{5.2\times}$ faster than state-of-the-art benchmarks and improves throughput by 80\% in Non Line-of-Sight (NLoS) conditions, matching the performance of ideal algorithms. With low time complexity, LinRA is a scalable and efficient RA solution for predictive flying networks.

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arXiv2025

Joint Channel Bandwidth Assignment and Relay Positioning for Predictive Flying Networks

Flying Networks (FNs) have emerged as a promising solution to provide on-demand wireless connectivity when network coverage is insufficient or the communications infrastructure is compromised, such as in disaster management scenarios. Despite extensive research on Unmanned Aerial Vehicle (UAV) positioning and radio resource allocation, the challenge of ensuring reliable traffic relay through backhaul links in predictive FNs remains unexplored. This work proposes Simulated Annealing for predictive FNs (SAFnet), an innovative algorithm that optimizes network performance under positioning constraints, limited bandwidth and minimum rate requirements. Our algorithm uniquely leverages prior knowledge of the first-tier node trajectories to assign bandwidth and dynamically adjust the position of the second-tier flying relay. Building upon Simulated Annealing, our approach enhances this well-known AI algorithm with penalty functions, achieving performance levels comparable to exhaustive search while significantly reducing computational complexity.

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arXiv2025

A Framework to Develop and Validate RL-Based Obstacle-Aware UAV Positioning Algorithms

Unmanned Aerial Vehicles (UAVs) increasingly enhance the Quality of Service (QoS) in wireless networks due to their flexibility and cost-effectiveness. However, optimizing UAV placement in dynamic, obstacle-prone environments remains a significant research challenge due to their complexity. Reinforcement Learning (RL) offers adaptability and robustness in such environments, proving effective for UAV optimization. This paper introduces RLpos-3, a novel framework that integrates standard RL techniques and simulation libraries with Network Simulator 3 (ns-3) to facilitate the development and evaluation of UAV positioning algorithms. RLpos-3 serves as a supplementary tool for researchers, enabling the implementation, analysis, and benchmarking of UAV positioning strategies across diverse environmental conditions while meeting user traffic demands. To validate its effectiveness, we present use cases demonstrating RLpos-3's performance in optimizing UAV placement under realistic conditions, such as urban and obstacle-rich environments.

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arXiv2024

On-demand 5G Private Networks using a Mobile Cell

This paper proposes the Mobile Cell (MC) concept for on-demand 5G private networks. The MC is designed to extend, restore, and reinforce 5G wireless coverage and network capacity on-demand, especially in areas with temporary communications needs or where it is costly or not possible to deploy a permanent fixed infrastructure. The design of the MC as well as the development, integration, and deployment in 5G private networks are discussed. The Mobile Cell concept can be applied in multiple real-world environments, including seaports and application scenarios. Similarly to critical hubs in the global supply chain, seaports require reliable, high-performance wireless communications to increase efficiency and manage dynamic operations in real-time. Current communications solutions in seaports typically rely on Wi-Fi and wired-based technologies. Wired-based technologies lack the necessary flexibility for dynamic environments. Wi-Fi is susceptible to interference from other systems operating in the same frequency bands. An MC operating in a licensed, interference-free spectrum is a promising solution to overcome these limitations and provide improved Quality of Service when using the 5G technology.

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arXiv2024

Traffic and Obstacle-aware UAV Positioning in Urban Environments Using Reinforcement Learning

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). In such environments, maintaining Line-of-Sight (LoS), 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.

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arXiv2024

On the Energy Consumption of Rotary Wing and Fixed Wing UAVs in Flying Networks

Unmanned Aerial Vehicles (UAVs) are increasingly used to enable wireless communications. Due to their characteristics, such as the ability to hover and carry cargo, UAVs can serve as communications nodes, including Wi-Fi Access Points and Cellular Base Stations. In previous work, we proposed the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of multiple UAVs acting as Flying Access Points (FAPs). Additionally, we developed the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate the UAV energy consumption, specifically when using the SUPPLY algorithm. However, MUAVE was initially designed to compute the energy consumption for rotary-wing UAVs only. In this paper, we propose eMUAVE, an enhanced version of the MUAVE simulator that allows the evaluation of the energy consumption for both rotary-wing and fixed-wing UAVs. Our energy consumption evaluation using eMUAVE considers reference and random networking scenarios. The results show that fixed-wing UAVs can be employed in the majority of networking scenarios. However, rotary-wing UAVs are typically more energy-efficient than fixed-wing UAVs when following the trajectories defined by SUPPLY.

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arXiv2024

Semantic Communications: the New Paradigm Behind Beyond 5G Technologies

Each generation of cellular networks is characterized by its distinct capabilities and innovations, which reflect the significant milestones reached with each new release. 5G has made substantial progress through the deployment of advanced encoding and modulation techniques, nearly reaching the Shannon physical capacity limit. In light of the requirements of Beyond-5G technologies, there is the need of a paradigm shift in the development of communication systems. Recent developments in the realm of Artificial Intelligence (AI) have enabled the deployment of tools with high abstraction capabilities, relevant for feature extraction processes and End-to-End system optimization tasks. In this context, Semantic Communications has emerged as a novel information transmission system, with AI as one of the core components in its implementation. This communication paradigm relies on the extraction and transmission of the "semantic meaning" of the source information using AI techniques, diverging from the conventional systems that primarily focus on ensuring the successful reception of the transmitted bits. The purpose of this survey is to provide a comprehensive overview of the fundamental concepts underlying Semantic Communications, including Shannon's Information Theory, classical and modern theories of semantic information, and an examination of the framework and system design of Semantic Communications. Additionally, recent implementations are reviwed, including the analysis of Semantic Communications systems according to the information object transmitted and the objective of the information transmission. Moreover, an in-depth study of prototypes and demonstrations are presented, supporting the viability of the Semantic Communications systems. Finally, some of the most relevant open challenges are detailed, highlighting open research questions to be pursued in Semantic Communications.

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arXiv2024

SUPPLY: Sustainable multi-UAV Performance-aware Placement Algorithm for Flying Networks

Unmanned Aerial Vehicles (UAVs) are used for a wide range of applications. Due to characteristics such as the ability to hover and carry cargo on-board, rotary-wing UAVs have been considered suitable platforms for carrying communications nodes, including Wi-Fi Access Points and cellular Base Stations. This gave rise to the concept of Flying Networks (FNs), now making part of the so-called Non-Terrestrial Networks (NTNs) defined in 3GPP. In scenarios where the deployment of terrestrial networks is not feasible, the use of FNs has emerged as a solution to provide wireless connectivity. However, the management of the communications resources in FNs imposes significant challenges, especially regarding the positioning of the UAVs so that the Quality of Service (QoS) offered to the Ground Users (GUs) and devices is maximized. Moreover, unlike terrestrial networks that are directly connected to the power grid, UAVs typically rely on on-board batteries that need to be recharged. In order to maximize the UAVs' flying time, the energy consumed by the UAVs needs to be minimized. When it comes to multi-UAV placement, most state-of-the-art solutions focus on maximizing the coverage area and assume that the UAVs keep hovering in a fixed position while serving GUs. Also, they do not address the energy-aware multi-UAV placement problem in networking scenarios where the GUs may have different QoS requirements and may not be uniformly distributed across the area of interest. In this work, we propose the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm. SUPPLY defines the energy and performance-aware positioning of multiple UAVs in an FN. To accomplish this, SUPPLY defines trajectories that minimize UAVs' energy consumption, while ensuring the targeted QoS levels. The obtained results show up to 25% energy consumption reduction with minimal impact on throughput and delay.

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