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View article: 6G Resilience -- White Paper
6G Resilience -- White Paper Open
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 g…
View article: Wireless Energy Transfer Beamforming Optimization for Intelligent Transmitting Surface
Wireless Energy Transfer Beamforming Optimization for Intelligent Transmitting Surface Open
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 …
View article: Movable Antennas-aided Wireless Energy Transfer for the Internet of Things
Movable Antennas-aided Wireless Energy Transfer for the Internet of Things Open
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, imp…
View article: Dimensioning and Optimization of Reliability Coverage in Local 6G Networks
Dimensioning and Optimization of Reliability Coverage in Local 6G Networks Open
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 …
View article: Offline and Distributional Reinforcement Learning for Wireless Communications
Offline and Distributional Reinforcement Learning for Wireless Communications Open
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) a…
View article: Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning Open
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 f…
View article: Multi-Agent Meta-Offline Reinforcement Learning for Timely UAV Path Planning and Data Collection
Multi-Agent Meta-Offline Reinforcement Learning for Timely UAV Path Planning and Data Collection Open
Multi-agent reinforcement learning (MARL) has been widely adopted in high-performance computing and complex data-driven decision-making in the wireless domain. However, conventional MARL schemes face many obstacles in real-world scenarios.…
View article: Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks Open
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 s…
View article: An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management
An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management Open
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…
View article: Severe disseminated paracoccidioidomycosis
Severe disseminated paracoccidioidomycosis Open
Paracoccidioidomycosis is a systemic fungal disease with a highly variable distribution, endemic to Central and South America with the highest prevalence in Brazil, Argentina, and Colombia. The chronic presentation of the disease is common…
View article: On the Spectral Efficiency of Movable and Rotary Antenna Arrays Under Rician Fading
On the Spectral Efficiency of Movable and Rotary Antenna Arrays Under Rician Fading Open
Most works evaluating the performance of Multi-User Multiple-Input Multiple-Output (MU-MIMO) systems consider Access Points (APs) with fixed antennas, that is, without any movement capability. Recently, the idea of APs with antenna arrays …
View article: Adaptive Handover Optimization in LEO Satellite Networks Using Energy-Aware Q-Learning
Adaptive Handover Optimization in LEO Satellite Networks Using Energy-Aware Q-Learning Open
Low Earth orbit (LEO) satellite networks are rapidly becoming a foundational infrastructure for global connectivity, especially in remote and underserved regions. However, the high mobility of LEO satellites results in frequent handovers, …
View article: Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification
Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification Open
Semantic and goal-oriented (SGO) communication is an emerging technology that only transmits significant information for a given task. Semantic communication encounters many challenges, such as computational complexity at end users, availa…
View article: Extending the LoRa Direct-to-Satellite Limits: Doppler Shift Pre-Compensation
Extending the LoRa Direct-to-Satellite Limits: Doppler Shift Pre-Compensation Open
Earlier studies and field tests have extensively investigated Long Range (LoRa) direct-to-satellite (DtS) communications, confirming the feasibility of integration with Low Earth Orbit (LEO) satellites. These works identify the Doppler eff…
View article: Detection and Classification of Anomalies in WSN-Enabled Cyber-Physical Systems
Detection and Classification of Anomalies in WSN-Enabled Cyber-Physical Systems Open
Detection and classification of anomalies in industrial applications has long been a focus of interest in the research community. The integration of computational and physical systems has increased the complexity of interactions between pr…
View article: MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion
MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion Open
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), an…
View article: Improvement of the Low-Energy Adaptive Clustering Hierarchy Protocol in Wireless Sensor Networks Using Mean Field Games
Improvement of the Low-Energy Adaptive Clustering Hierarchy Protocol in Wireless Sensor Networks Using Mean Field Games Open
The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is a widely used method for managing energy consumption in Wireless Sensor Networks (WSNs). However, it has limitations that affect network longevity and performance. This paper…