Priority-Based Load Balancing With Multi-Agent Deep Reinforcement Learning for Space-Air-Ground Integrated Network Slicing
H Tu, P Bellavista, L Zhao, G Zheng… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
Space-air-ground integrated network (SAGIN) slicing has been studied for supporting
diverse applications, which consists of the terrestrial layer (TL) deployed with base stations …
diverse applications, which consists of the terrestrial layer (TL) deployed with base stations …
Multi-objective Optimization of Space-Air-Ground Integrated Network Slicing Relying on a Pair of Central and Distributed Learning Algorithms
As an attractive enabling technology for next-generation wireless communications, network
slicing supports diverse customized services in the global space–air–ground-integrated …
slicing supports diverse customized services in the global space–air–ground-integrated …
GAN-based deep distributional reinforcement learning for resource management in network slicing
Network slicing is a key technology in 5G communications system, which aims to
dynamically and efficiently allocate resources for diversified services with distinct …
dynamically and efficiently allocate resources for diversified services with distinct …
Dynamic Resource Allocation in Network Slicing with Deep Reinforcement Learning
Network slicing is key to enabling 6G and beyond networks to simultaneously meet the
diverse quality of service (QoS) requirements of various services. In network slicing, radio …
diverse quality of service (QoS) requirements of various services. In network slicing, radio …
Hierarchical DRL-empowered Network Slicing in Space-Air-Ground Networks
The space-air-ground integrated network (SAGIN) is an emerging architecture that has the
potential to provide seamless, high data rates, and reliable transmission with a vastly …
potential to provide seamless, high data rates, and reliable transmission with a vastly …
Deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing
Network slicing promises to provision diversified services with distinct requirements in one
infrastructure. Deep reinforcement learning (eg, deep Q-learning, DQL) is assumed to be an …
infrastructure. Deep reinforcement learning (eg, deep Q-learning, DQL) is assumed to be an …
Network slicing via transfer learning aided distributed deep reinforcement learning
Deep reinforcement learning (DRL) has been in-creasingly employed to handle the dynamic
and complex re-source management in network slicing. The deployment of DRL policies in …
and complex re-source management in network slicing. The deployment of DRL policies in …
Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
Q Qi, W Lin, B Guo, J Chen, C Deng, G Lin, X Sun… - Electronics, 2022 - mdpi.com
Network slicing enables the multiplexing of independent logical networks on the same
physical network infrastructure to provide different network services for different applications …
physical network infrastructure to provide different network services for different applications …
Deep Reinforcement Learning for Online Resource Allocation in Network Slicing
Network slicing is a key enabler of 5G and beyond networks to satisfy the diverse quality of
service (QoS) requirements of different services simultaneously. In network slicing, radio …
service (QoS) requirements of different services simultaneously. In network slicing, radio …
Intelligent Edge-Aided Network Slicing for 5G and Beyond Networks
Network slicing at the edge is becoming a new enabler for 5G and beyond networks to
support diverse and differential services, with efficient employment of virtualized resources …
support diverse and differential services, with efficient employment of virtualized resources …