Aerospace integrated networks innovation for empowering 6G: A survey and future challenges
The ever-increasing demand for ubiquitous and differentiated services at anytime and
anywhere emphasizes the necessity of aerospace integrated networks (AINs) which consist …
anywhere emphasizes the necessity of aerospace integrated networks (AINs) which consist …
Multi-agent reinforcement learning: A review of challenges and applications
In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …
increasingly appealing to exploit distributed data communication and learning. Specifically …
Multi-agent reinforcement learning based resource management in MEC-and UAV-assisted vehicular networks
In this paper, we investigate multi-dimensional resource management for unmanned aerial
vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource …
vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource …
Optimizing federated learning in distributed industrial IoT: A multi-agent approach
In this paper, we aim to make the best joint decision of device selection and computing and
spectrum resource allocation for optimizing federated learning (FL) performance in …
spectrum resource allocation for optimizing federated learning (FL) performance in …
Towards autonomous multi-UAV wireless network: A survey of reinforcement learning-based approaches
Unmanned aerial vehicle (UAV)-based wireless networks have received increasing
research interest in recent years and are gradually being utilized in various aspects of our …
research interest in recent years and are gradually being utilized in various aspects of our …
Adaptive digital twin and multiagent deep reinforcement learning for vehicular edge computing and networks
Technological advancements of urban informatics and vehicular intelligence have enabled
connected smart vehicles as pervasive edge computing platforms for a plethora of powerful …
connected smart vehicles as pervasive edge computing platforms for a plethora of powerful …
Multi-agent DRL for task offloading and resource allocation in multi-UAV enabled IoT edge network
The Internet of Things (IoT) edge network has connected lots of heterogeneous smart
devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging …
devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging …
Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning
The advanced cruise control system has expanded the energy-saving potential of the hybrid
electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only …
electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only …
Machine learning empowered trajectory and passive beamforming design in UAV-RIS wireless networks
A novel framework is proposed for integrating reconfigurable intelligent surfaces (RIS) in
unmanned aerial vehicle (UAV) enabled wireless networks, where an RIS is deployed for …
unmanned aerial vehicle (UAV) enabled wireless networks, where an RIS is deployed for …