arXiv subjects

Hirley Alves

Publications and source records attributed to Hirley Alves.

At least 19 recordsLinked to original sources

arXiv2026

Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks

The integration of generative artificial intelligence with wireless communication and signal processing systems has opened new avenues for intelligent, data-driven decision-making in future 6G networks. This work proposes a diffusion soft actor-critic (Diffusion-SAC) approach that leverages offline reinforcement learning (RL) enhanced by denoising diffusion probabilistic models (DDPMs) to optimize trajectory and scheduling control in unmanned aerial vehicle (UAV) networks. While offline RL methods, such as conservative Q-learning (CQL), can learn from static datasets, they often struggle to generalize in low-data or dynamic conditions. To address this, we combine the robustness of CQL with the generative power of diffusion models, enabling expressive and signal-aware policy learning that generalizes beyond behavior policies. Applied to a UAV-assisted wireless network, the proposed framework minimizes transmission energy and improves fairness among devices. Simulations show that Diffusion-SAC outperforms standard offline RL baselines, achieving more stable convergence and higher rewards even with limited datasets. The method enhances data efficiency, reduces energy consumption, and increases throughput by more than 35 % compared to existing algorithms, demonstrating its potential for robust policy learning in next-generation wireless control systems.

cs.LG↗

arXiv2026

Meta-Offline and Distributional Multi-Agent RL for Risk-Aware Decision-Making

Mission critical applications, such as UAV-assisted IoT networks require risk-aware decision-making under dynamic topologies and uncertain channels. We propose meta-conservative quantile regression (M-CQR), a meta-offline distributional MARL algorithm that integrates conservative Q-learning (CQL) for safe offline learning, quantile regression DQN (QR-DQN) for risk-sensitive value estimation, and model-agnostic meta-learning (MAML) for rapid adaptation. Two variants are developed: meta-independent CQR (M-I-CQR) and meta-CTDE-CQR. In a UAV-based communication scenario, M-CTDE-CQR achieves up to 50% faster convergence and outperforms baseline MARL methods, offering improved scalability, robustness, and adaptability for risk-sensitive decision-making. Code is available at https://github.com/Eslam211/MA_Meta_ODRL

cs.MA↗

arXiv2026

Offline Multi-Agent Reinforcement Learning for 6G Communications: Fundamentals, Applications and Future Directions

The next-generation wireless technologies, including beyond 5G and 6G networks, are paving the way for transformative applications such as vehicle platooning, smart cities, and remote surgery. These innovations are driven by a vast array of interconnected wireless entities, including IoT devices, access points, UAVs, and CAVs, which increase network complexity and demand more advanced decision-making algorithms. Artificial intelligence (AI) and machine learning (ML), especially reinforcement learning (RL), are key enablers for such networks, providing solutions to high-dimensional and complex challenges. However, as networks expand to multi-agent environments, traditional online RL approaches face cost, safety, and scalability limitations. Offline multi-agent reinforcement learning (MARL) offers a promising solution by utilizing pre-collected data, reducing the need for real-time interaction. This article introduces a novel offline MARL algorithm based on conservative Q-learning (CQL), ensuring safe and efficient training. We extend this with meta-learning to address dynamic environments and validate the approach through use cases in radio resource management and UAV networks. Our work highlights offline MARL's advantages, limitations, and future directions in wireless applications.

cs.MA↗

arXiv2025

Extreme Value Theory-enhanced Radio Maps for Handovers in Ultra-reliable Communications

Efficient handover (HO) strategies are essential for maintaining the stringent performance requirements of ultra-reliable communication (URC) systems. This work introduces a novel HO framework designed from a physical-layer perspective, where the decision-making process focuses on determining the optimal time and location for performing HOs. Leveraging extreme value theory (EVT) and statistical radio maps, the proposed method predicts signal behaviour and enables efficient resource allocation. The framework ensures seamless HOs and improved system performance by facilitating effective resource transitions and coordination across spatial locations while incorporating mechanisms to mitigate the ping-pong effect. Comparative evaluations demonstrate that this strategy provides superior service availability and energy efficiency than traditional HO mechanisms, highlighting its effectiveness in URC environments.

cs.NI↗

arXiv2025

6G Resilience -- White Paper

6G must be designed to withstand, adapt to, and evolve amid prolonged, complex disruptions. Mobile networks' shift from efficiency-first to sustainability-aware has motivated this white paper to assert that resilience is a primary design goal, alongside sustainability and efficiency, encompassing technology, architecture, and economics. We promote resilience by analysing dependencies between mobile networks and other critical systems, such as energy, transport, and emergency services, and illustrate how cascading failures spread through infrastructures. We formalise resilience using the 3R framework: reliability, robustness, resilience. Subsequently, we translate this into measurable capabilities: graceful degradation, situational awareness, rapid reconfiguration, and learning-driven improvement and recovery. Architecturally, we promote edge-native and locality-aware designs, open interfaces, and programmability to enable islanded operations, fallback modes, and multi-layer diversity (radio, compute, energy, timing). Key enablers include AI-native control loops with verifiable behaviour, zero-trust security rooted in hardware and supply-chain integrity, and networking techniques that prioritise critical traffic, time-sensitive flows, and inter-domain coordination. Resilience also has a techno-economic aspect: open platforms and high-quality complementors generate ecosystem externalities that enhance resilience while opening new markets. We identify nine business-model groups and several patterns aligned with the 3R objectives, and we outline governance and standardisation. This white paper serves as an initial step and catalyst for 6G resilience. It aims to inspire researchers, professionals, government officials, and the public, providing them with the essential components to understand and shape the development of 6G resilience.

eess.SP↗

arXiv2025

Wireless Energy Transfer Beamforming Optimization for Intelligent Transmitting Surface

Radio frequency (RF) wireless energy transfer (WET) is a promising technology for powering the growing ecosystem of Internet of Things (IoT) using power beacons (PBs). Recent research focuses on efficient PB architectures that can support numerous antennas. In this context, PBs equipped with intelligent surfaces present a promising approach, enabling physically large, reconfigurable arrays. Motivated by these advantages, this work aims to minimize the power consumption of a PB equipped with a passive intelligent transmitting surface (ITS) and a collocated digital beamforming-based feeder to charge multiple single-antenna devices. To model the PB's power consumption accurately, we consider power amplifiers nonlinearities, ITS control power, and feeder-to-ITS air interface losses. The resulting optimization problem is highly nonlinear and nonconvex due to the high-power amplifier (HPA), the received power constraints at the devices, and the unit-modulus constraint imposed by the phase shifter configuration of the ITS. To tackle this issue, we apply successive convex approximation (SCA) to iteratively solve convex subproblems that jointly optimize the digital precoder and phase configuration. Given SCA's sensitivity to initialization, we propose an algorithm that ensures initialization feasibility while balancing convergence speed and solution quality. We compare the proposed ITS-equipped PB's power consumption against benchmark architectures featuring digital and hybrid analog-digital beamforming. Results demonstrate that the proposed architecture efficiently scales with the number of RF chains and ITS elements. We also show that nonuniform ITS power distribution influences beamforming and can shift a device between near- and far-field regions, even with a constant aperture.

eess.SP↗

arXiv2025

Dependability Theory-based Statistical QoS Provisioning of Fluid Antenna Systems

Fluid antenna systems (FAS) have recently emerged as a promising technology for next-generation wireless networks, offering real-time spatial reconfiguration to enhance reliability, throughput, and energy efficiency. Nevertheless, existing studies often overlook the temporal dynamics of channel fading and their implications for mission-critical operations. In this paper, we propose a dependability-theoretic framework for statistical quality-of-service (QoS) provisioning of FAS under finite blocklength (FBL) constraints. Specifically, we derive new closed-form expressions for the level-crossing rate (LCR) and average fade duration (AFD) of an $N$-port FAS over Nakagami-$m$ fading channels. Leveraging these second-order statistics, we define two key dependability metrics such as mission reliability and mean time-to-first-failure (MTTFF), to quantify the probability of uninterrupted operation over a defined mission duration. We further extend the classical effective capacity (EC) concept to incorporate mission reliability in the FBL regime, yielding a mission EC (mEC). To capture energy efficiency under bursty traffic and latency constraints, we also develop the mission effective energy efficiency (mEEE) metric and formulate its maximization as a non-convex fractional optimization problem. This problem is then solved via a modified Dinkelbach's method with an embedded line search. Extensive simulations uncover critical trade-offs among port count, QoS exponent, signal-to-noise ratio, and mission duration, offering insights for the design of ultra-reliable, low-latency, and energy-efficient industrial internet-of-things (IIoT) systems.

eess.SP↗

arXiv2025

Movable Antennas-aided Wireless Energy Transfer for the Internet of Things

Recent advancements in movable antennas (MAs) technology create new opportunities for 6G and beyond wireless systems. MAs are promising for radio frequency wireless energy transfer because they can dynamically adjust antenna positions, improving energy efficiency and scalability. This work aims to minimize the power consumed by an analog beamforming power beacon equipped with independently-controlled MAs (IMAs) for charging multiple single-antenna devices. To this end, we enforce a minimum separation among antennas and a minimum received power at the devices. The resulting optimization problem is nonlinear and nonconvex due to interdependencies among the variables. To tackle this, we propose a semidefinite program guided particle swarm optimization (SgPSO) algorithm where each particle represents an antenna configuration, and the fitness function optimizes the corresponding power allocation. SgPSO is utilized for configuring the MAs largely outperforming fixed array implementations, particularly with more antennas or devices. We also present an alternative implementation using uniformly-spaced MAs, whose performance closely approaches that of the IMAs, with the gap widening only as the number of devices grows. We also examine how increasing the number of antennas promotes near-field conditions, which decrease as devices become more widely distributed.

eess.SP↗

arXiv2025

Dimensioning and Optimization of Reliability Coverage in Local 6G Networks

Enabling vertical use cases for the sixth generation (6G) wireless networks, such as automated manufacturing, immersive extended reality (XR), and self-driving fleets, will require network designs that meet reliability and latency targets in well-defined service areas. In order to establish a quantifiable design objective, we introduce the novel concept of reliability coverage, defined as the percentage area covered by communication services operating under well-defined reliability and performance targets. Reliability coverage allows us to unify the different network design tasks occurring at different time scales, namely resource orchestration and allocation, resulting in a single framework for dimensioning and optimization in local 6G networks. The two time scales, when considered together, yield remarkably consistent results and allow us to observe how stringent reliability/latency requirements translate into the increased wireless network resource demands.

cs.NI↗

arXiv2025

Offline and Distributional Reinforcement Learning for Wireless Communications

The rapid growth of heterogeneous and massive wireless connectivity in 6G networks demands intelligent solutions to ensure scalability, reliability, privacy, ultra-low latency, and effective control. Although artificial intelligence (AI) and machine learning (ML) have demonstrated their potential in this domain, traditional online reinforcement learning (RL) and deep RL methods face limitations in real-time wireless networks. For instance, these methods rely on online interaction with the environment, which might be unfeasible, costly, or unsafe. In addition, they cannot handle the inherent uncertainties in real-time wireless applications. We focus on offline and distributional RL, two advanced RL techniques that can overcome these challenges by training on static datasets and accounting for network uncertainties. We introduce a novel framework that combines offline and distributional RL for wireless communication applications. Through case studies on unmanned aerial vehicle (UAV) trajectory optimization and radio resource management (RRM), we demonstrate that our proposed Conservative Quantile Regression (CQR) algorithm outperforms conventional RL approaches regarding convergence speed and risk management. Finally, we discuss open challenges and potential future directions for applying these techniques in 6G networks, paving the way for safer and more efficient real-time wireless systems.

cs.LG↗

arXiv2025

Activity Detection for Massive Random Access using Covariance-based Matching Pursuit

The Internet of Things paradigm heavily relies on a network of a massive number of machine-type devices (MTDs) that monitor various phenomena. Consequently, MTDs are randomly activated at different times whenever a change occurs. In general, fewer MTDs are simultaneously activated across the network, resembling targeted sampling in compressed sensing. Therefore, signal recovery in machine-type communications is addressed through joint user activity detection and channel estimation algorithms built using compressed sensing theory. However, most of these algorithms follow a two-stage procedure in which a channel is first estimated and later mapped to find active users. This approach is inefficient because the estimated channel information is subsequently discarded. To overcome this limitation, we introduce a novel covariance-learning matching pursuit (CL-MP) algorithm that bypasses explicit channel estimation. Instead, it focuses on estimating the indices of the active users greedily. Simulation results presented in terms of probability of miss detection, exact recovery rate, and computational complexity validate the proposed technique's superior performance and efficiency.

eess.SP↗

arXiv2025

Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning

Reinforcement learning (RL) has been a promising essence in future 5G-beyond and 6G systems. Its main advantage lies in its robust model-free decision-making in complex and large-dimension wireless environments. However, most existing RL frameworks rely on online interaction with the environment, which might not be feasible due to safety and cost concerns. Another problem with online RL is the lack of scalability of the designed algorithm with dynamic or new environments. This work proposes a novel, resilient, few-shot meta-offline RL algorithm combining offline RL using conservative Q-learning (CQL) and meta-learning using model-agnostic meta-learning (MAML). The proposed algorithm can train RL models using static offline datasets without any online interaction with the environments. In addition, with the aid of MAML, the proposed model can be scaled up to new unseen environments. We showcase the proposed algorithm for optimizing an unmanned aerial vehicle (UAV) 's trajectory and scheduling policy to minimize the age-of-information (AoI) and transmission power of limited-power devices. Numerical results show that the proposed few-shot meta-offline RL algorithm converges faster than baseline schemes, such as deep Q-networks and CQL. In addition, it is the only algorithm that can achieve optimal joint AoI and transmission power using an offline dataset with few shots of data points and is resilient to network failures due to unprecedented environmental changes.

cs.RO↗

arXiv2025

Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks

Age-of-information (AoI) and transmission power are crucial performance metrics in low energy wireless networks, where information freshness is of paramount importance. This study examines a power-limited internet of things (IoT) network supported by a flying unmanned aerial vehicle(UAV) that collects data. Our aim is to optimize the UAV flight trajectory and scheduling policy to minimize a varying AoI and transmission power combination. To tackle this variation, this paper proposes a meta-deep reinforcement learning (RL) approach that integrates deep Q-networks (DQNs) with model-agnostic meta-learning (MAML). DQNs determine optimal UAV decisions, while MAML enables scalability across varying objective functions. Numerical results indicate that the proposed algorithm converges faster and adapts to new objectives more effectively than traditional deep RL methods, achieving minimal AoI and transmission power overall.

cs.LG↗

arXiv2025

Offline and Distributional Reinforcement Learning for Radio Resource Management

Reinforcement learning (RL) has proved to have a promising role in future intelligent wireless networks. Online RL has been adopted for radio resource management (RRM), taking over traditional schemes. However, due to its reliance on online interaction with the environment, its role becomes limited in practical, real-world problems where online interaction is not feasible. In addition, traditional RL stands short in front of the uncertainties and risks in real-world stochastic environments. In this manner, we propose an offline and distributional RL scheme for the RRM problem, enabling offline training using a static dataset without any interaction with the environment and considering the sources of uncertainties using the distributions of the return. Simulation results demonstrate that the proposed scheme outperforms conventional resource management models. In addition, it is the only scheme that surpasses online RL with a 10 % gain over online RL.

cs.LG↗

arXiv2025

An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management

Offline multi-agent reinforcement learning (MARL) addresses key limitations of online MARL, such as safety concerns, expensive data collection, extended training intervals, and high signaling overhead caused by online interactions with the environment. In this work, we propose an offline MARL algorithm for radio resource management (RRM), focusing on optimizing scheduling policies for multiple access points (APs) to jointly maximize the sum and tail rates of user equipment (UEs). We evaluate three training paradigms: centralized, independent, and centralized training with decentralized execution (CTDE). Our simulation results demonstrate that the proposed offline MARL framework outperforms conventional baseline approaches, achieving over a 15\% improvement in a weighted combination of sum and tail rates. Additionally, the CTDE framework strikes an effective balance, reducing the computational complexity of centralized methods while addressing the inefficiencies of independent training. These results underscore the potential of offline MARL to deliver scalable, robust, and efficient solutions for resource management in dynamic wireless networks.

cs.MA↗

arXiv2024

On the Spectral Efficiency of Indoor Wireless Networks with a Rotary Uniform Linear Array

Contemporary wireless communication systems rely on Multi-User Multiple-Input Multiple-Output (MU-MIMO) techniques. In such systems, each Access Point (AP) is equipped with multiple antenna elements and serves multiple devices simultaneously. Notably, traditional systems utilize fixed antennas, i.e., antennas without any movement capabilities, while the idea of movable antennas has recently gained traction among the research community. By moving in a confined region, movable antennas are able to exploit the wireless channel variation in the continuous domain. This additional degree of freedom may enhance the quality of the wireless links, and consequently the communication performance. However, movable antennas for MU-MIMO proposed in the literature are complex, bulky, expensive and present a high power consumption. In this paper, we propose an alternative to such systems that has lower complexity and lower cost. More specifically, we propose the incorporation of rotation capabilities to APs equipped with Uniform Linear Arrays (ULAs) of antennas. We consider the uplink of an indoor scenario where the AP serves multiple devices simultaneously. The optimal rotation of the ULA is computed based on estimates of the positions of the active devices and aiming at maximizing the per-user mean achievable Spectral Efficiency (SE). Adopting a spatially correlated Rician channel model, our numerical results show that the rotation capabilities of the AP can bring substantial improvements in the SE in scenarios where the line-of-sight component of the channel vectors is strong. Moreover, our proposed system is robust against imperfect positioning estimates.

cs.IT↗

arXiv2024

MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion

Accurate indoor localization remains challenging due to variations in wireless signal environments and limited data availability. This paper introduces MetaGraphLoc, a novel system leveraging sensor fusion, graph neural networks (GNNs), and meta-learning to overcome these limitations. MetaGraphLoc integrates received signal strength indicator measurements with inertial measurement unit data to enhance localization accuracy. Our proposed GNN architecture, featuring dynamic edge construction (DEC), captures the spatial relationships between access points and underlying data patterns. MetaGraphLoc employs a meta-learning framework to adapt the GNN model to new environments with minimal data collection, significantly reducing calibration efforts. Extensive evaluations demonstrate the effectiveness of MetaGraphLoc. Data fusion reduces localization error by 15.92%, underscoring its importance. The GNN with DEC outperforms traditional deep neural networks by up to 30.89%, considering accuracy. Furthermore, the meta-learning approach enables efficient adaptation to new environments, minimizing data collection requirements. These advancements position MetaGraphLoc as a promising solution for indoor localization, paving the way for improved navigation and location-based services in the ever-evolving Internet of Things networks.

eess.SP↗

arXiv2024

Neural Network-Based Bandit: A Medium Access Control for the IIoT Alarm Scenario

Efficient Random Access (RA) is critical for enabling reliable communication in Industrial Internet of Things (IIoT) networks. Herein, we propose a deep reinforcement learning based distributed RA scheme, entitled Neural Network-Based Bandit (NNBB), for the IIoT alarm scenario. In such a scenario, the devices may detect a common critical event, and the goal is to ensure the alarm information is delivered successfully from at least one device. The proposed NNBB scheme is implemented at each device, where it trains itself online and establishes implicit inter-device coordination to achieve the common goal. Devices can transmit simultaneously on multiple orthogonal channels and each possible transmission pattern constitutes a possible action for the NNBB, which uses a deep neural network to determine the action. Our simulation results show that as the number of devices in the network increases, so does the performance gain of the NNBB compared to the Multi-Armed Bandit (MAB) RA benchmark. For instance, NNBB experiences a 7% success rate drop when there are four channels and the number of devices increases from 10 to 60, while MAB faces a 25% drop.

math.OC↗