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 …
Constrained reinforcement learning for resource allocation in network slicing
In network slicing, dynamic resource allocation is the key to network performance
optimization. Deep reinforcement learning (DRL) is a promising method to exploit the …
optimization. Deep reinforcement learning (DRL) is a promising method to exploit the …
A multi-agent reinforcement learning architecture for network slicing orchestration
The Network Slicing (NS) paradigm is one of the pillars of the future 5G networks and is
gathering great attention from both industry and scientific communities. In a NS scenario …
gathering great attention from both industry and scientific communities. In a NS scenario …
Resource allocation method for network slicing using constrained reinforcement learning
With the proliferation of mobile networks, we face strong diversification of services,
demanding the network to be more flexible. To satisfy this dire need, network slicing is …
demanding the network to be more flexible. To satisfy this dire need, network slicing is …
AI-based resource allocation in end-to-end network slicing under demand and CSI uncertainties
Network slicing (NwS) is one of the main technologies in the fifth-generation of mobile
communication and beyond (5G+). One of the important challenges in the NwS is …
communication and beyond (5G+). One of the important challenges in the NwS is …
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 …
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 …
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 …
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 …
Using distributed reinforcement learning for resource orchestration in a network slicing scenario
The Network Slicing (NS) paradigm enables the partition of physical and virtual resources
among multiple logical networks, possibly managed by different tenants. In such a scenario …
among multiple logical networks, possibly managed by different tenants. In such a scenario …