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 …
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 …
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 constrained reinforcement learning based approach for network slicing
With the proliferation of mobile networks, we face strong diversification of services,
demanding the current network to embed more flexibility. To satisfy this daring need …
demanding the current network to embed more flexibility. To satisfy this daring need …
DeepSlicing: Deep reinforcement learning assisted resource allocation for network slicing
Network slicing enables multiple virtual networks run on the same physical infrastructure to
support various use cases in 5G and beyond. These use cases, however, have very diverse …
support various use cases in 5G and beyond. These use cases, however, have very diverse …
Data-driven dynamic resource scheduling for network slicing: A deep reinforcement learning approach
Network slicing is designed to support a variety of emerging applications with diverse
performance and flexibility requirements, by dividing the physical network into multiple …
performance and flexibility requirements, by dividing the physical network into multiple …
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 …
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 …
Digital twin-enhanced deep reinforcement learning for resource management in networks slicing
Network slicing-based communication systems can dynamically and efficiently allocate
resources for diversified services. However, due to the limitation of the network interface on …
resources for diversified services. However, due to the limitation of the network interface on …
相关搜索
- deep reinforcement network slicing
- resource allocation network slicing
- deep reinforcement resource management
- distributed reinforcement resource orchestration
- advantage functions network slicing
- constrained reinforcement resource allocation
- constrained reinforcement network slicing
- resource management network slicing
- resource scheduling network slicing
- deep reinforcement resource allocation