PreM-FedIoV: A Novel Federated Reinforcement Learning Framework for Predictive Maintenance in IoV
The Internet of Vehicles (IoV) enhances data availability by equipping a plethora of sensors,
driving the automotive industry towards data-driven Predictive Maintenance (PreM) models …
driving the automotive industry towards data-driven Predictive Maintenance (PreM) models …
Multi-Agent Reinforcement Learning based Uplink OFDMA for IEEE 802.11 ax Networks
In the IEEE 802.11 ax Wireless Local Area Networks (WLANs), Orthogonal Frequency
Division Multiple Access (OFDMA) has been applied to enable the high-throughput WLAN …
Division Multiple Access (OFDMA) has been applied to enable the high-throughput WLAN …
High priority space protection (HPP): countdown space-based MAC protocol with enhanced lockdown effect
The countdown mechanism is a powerful concept for creating distributed wireless contention
protocols and is the key component of the standard IEEE 802.11 Distributed Coordination …
protocols and is the key component of the standard IEEE 802.11 Distributed Coordination …
[PDF][PDF] Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach
Optimizing the deployment of large language models (LLMs) in edge computing
environments is critical for enhancing privacy and computational efficiency. Toward efficient …
environments is critical for enhancing privacy and computational efficiency. Toward efficient …
OSCAR: A Contention Window Optimization Approach Using Deep Reinforcement Learning
C Grasso, R Raftopoulos… - ICC 2023-IEEE …, 2023 - ieeexplore.ieee.org
The contention window (CW) has a significant impact on the efficiency of Wi-Fi networks.
Unfortunately, the basic access method employed by 802.11 networks does not scale well …
Unfortunately, the basic access method employed by 802.11 networks does not scale well …
Multi-Armed Bandit for Contention Window Optimization
R Raftopoulos, G Schembra - European Wireless 2023; 28th …, 2023 - ieeexplore.ieee.org
Future networks will require to support a tremendous number of communicating devices.
Unfortunately, the basic access method employed by IEEE 802.11 networks does not scale …
Unfortunately, the basic access method employed by IEEE 802.11 networks does not scale …
Deep reinforcement learning-based contention window optimization for IEEE 802.11 networks
This study focuses on optimizing the contention window (CW) in IEEE 802.11 networks
using deep reinforcement learning (DRL) to enhance the effectiveness of the contention …
using deep reinforcement learning (DRL) to enhance the effectiveness of the contention …
[PDF][PDF] Integrating Deep Reinforcement Learning in 6G Edge Environments: Towards Intelligent Network Optimization
R Raftopoulos - iris.unict.it
The rapid evolution of wireless communication technologies has led to the emergence of 6G
networks, which promise unprecedented levels of connectivity, capacity, and intelligence …
networks, which promise unprecedented levels of connectivity, capacity, and intelligence …