Hypergraph convolution mix DDPG for multi-aerial base station deployment
Aerial base stations (AeBS), as crucial components of air-ground integrated networks, can
serve as the edge nodes to provide flexible services to ground users. Optimizing the …
serve as the edge nodes to provide flexible services to ground users. Optimizing the …
HAP-assisted multi-aerial base station deployment for capacity enhancement via federated deep reinforcement learning
Aerial base stations (AeBSs), as crucial components of air-ground integrated networks, are
widely employed in cloud computing, disaster relief, and various applications. How to …
widely employed in cloud computing, disaster relief, and various applications. How to …
Federated Deep Reinforcement Learning for Joint AeBSs Deployment and Computation Offloading in Aerial Edge Computing Network
In the 6G aerial network, all aerial communication nodes have computing and storage
functions and can perform real-time wireless signal processing and resource management …
functions and can perform real-time wireless signal processing and resource management …
Computing assistance from the sky: Decentralized computation efficiency optimization for air-ground integrated MEC networks
This letter proposes a multi-agent deep reinforcement learning (MADRL) framework for
resource allocation in air-ground integrated multi-access edge computing (MEC) networks …
resource allocation in air-ground integrated multi-access edge computing (MEC) networks …
Joint offloading and resource allocation for hybrid cloud and edge computing in SAGINs: A decision assisted hybrid action space deep reinforcement learning …
In recent years, the amalgamation of satellite communications and aerial platforms into
space-air-ground integrated network (SAGINs) has emerged as an indispensable area of …
space-air-ground integrated network (SAGINs) has emerged as an indispensable area of …
Distributed multi-agent empowered resource allocation in deep edge networks
The sixth generation wireless communication networks (6G) are anticipated to bring a
disruptive innovation on multiple scenarios, where deep edge networks (DENs) turn into a …
disruptive innovation on multiple scenarios, where deep edge networks (DENs) turn into a …
Multi-Objective Optimization in Air-to-Air Communication System Based on Multi-Agent Deep Reinforcement Learning
S Lin, Y Chen, S Li - Sensors, 2023 - mdpi.com
With the advantages of real-time data processing and flexible deployment, unmanned aerial
vehicle (UAV)-assisted mobile edge computing systems are widely used in both civil and …
vehicle (UAV)-assisted mobile edge computing systems are widely used in both civil and …
Energy Consumption Modeling and Optimization of UAV-Assisted MEC Networks Using Deep Reinforcement Learning
Unmanned aerial vehicle (UAV)-assisted multiaccess edge computing (MEC) technology
has garnered significant attention and has been successfully implemented in specific …
has garnered significant attention and has been successfully implemented in specific …
Towards energy efficient resource allocation: When green mobile edge computing meets multi-agent deep reinforcement learning
Y Xiao, Y Song, J Liu - ICC 2022-IEEE International …, 2022 - ieeexplore.ieee.org
Mobile edge computing (MEC) extends the computing power to the edge of communication
networks, which has been considered as a promising technology to further improve the …
networks, which has been considered as a promising technology to further improve the …
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 …