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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: 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: 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: Offline and Distributional Reinforcement Learning for Radio Resource Management
Offline and Distributional Reinforcement Learning for Radio Resource Management Open
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 onlin…
View article: An Analysis of Minimum Error Entropy Loss Functions in Wireless Communications
An Analysis of Minimum Error Entropy Loss Functions in Wireless Communications Open
This paper introduces the minimum error entropy (MEE) criterion as an advanced information-theoretic loss function tailored for deep learning applications in wireless communications. The MEE criterion leverages higher-order statistical pro…
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: A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles
A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles Open
Task-oriented semantic communication is an emerging technology that transmits only the relevant semantics of a message instead of the whole message to achieve a specific task. It reduces latency, compresses the data, and is more robust in …
View article: Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning
Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning Open
Reinforcement learning (RL) has been widely adopted for controlling and optimizing complex engineering systems such as next-generation wireless networks. An important challenge in adopting RL is the need for direct access to the physical e…
View article: A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles
A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles Open
Task-oriented semantic communication is an emerging technology that transmits only the relevant semantics of a message instead of the whole message to achieve a specific task. It reduces latency, compresses the data, and is more robust in …
View article: Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models
Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models Open
The age of information (AoI) is used to measure the freshness of the data. In IoT networks, the traditional resource management schemes rely on a message exchange between the devices and the base station (BS) before communication which cau…
View article: Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach Open
In many massive IoT communication scenarios, the IoT devices require coverage from dynamic units that can move close to the IoT devices and reduce the uplink energy consumption. A robust solution is to deploy a large number of UAVs (UAV sw…
View article: Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models
Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models Open
The age of information (AoI) is used to measure the freshness of the data. In IoT networks, the traditional resource management schemes rely on a message exchange between the devices and the base station (BS) before communication which cau…
View article: LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case
LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case Open
In brief, we present a novel framework that counts the number of people based on the readings of the sensors inside closed rooms. These sensors are connected using LoRa network. First, we identify the transmission failures that cause missi…
View article: LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case
LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case Open
In brief, we present a novel framework that counts the number of people based on the readings of the sensors inside closed rooms. These sensors are connected using LoRa network. First, we identify the transmission failures that cause missi…
View article: LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case
LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case Open
IoT has a significant role in the smart campus. This paper presents a detailed description of the Smart Campus dataset based on LoRaWAN. LoRaWAN is an emerging technology that enables serving hundreds of IoT devices. First, we describe the…
View article: Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models
Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models Open
The age-of-information (AoI) is used to measure the freshness of the data. In IoT networks, the traditional resource management schemes rely on message exchange between the devices and the base station (BS) prior to communication which cau…