A sub-action aided deep reinforcement learning framework for latency-sensitive network slicing
Network slicing is a core technique of fifth-generation (5G) systems and beyond. To
maximize the number of accepted network slices with limited hardware resources, service …
maximize the number of accepted network slices with limited hardware resources, service …
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 …
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 …
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 …
CLARA: A constrained reinforcement learning based resource allocation framework for network slicing
As mobile networks proliferate, we are experiencing a strong diversification of services,
which requires greater flexibility from the existing network. Network slicing is proposed as a …
which requires greater flexibility from the existing network. Network slicing is proposed as a …
Deep reinforcement learning for end-to-end network slicing: Challenges and solutions
5G and beyond is expected to enable various emerging use cases with diverse performance
requirements from vertical industries. To serve these use cases cost-effectively, network …
requirements from vertical industries. To serve these use cases cost-effectively, network …
An end-to-end network slicing algorithm based on deep Q-learning for 5G network
T Li, X Zhu, X Liu - IEEE Access, 2020 - ieeexplore.ieee.org
As one of key technologies of the fifth-generation (5G) communication system, network
slicing can share the underlying infrastructure with different application requirements and …
slicing can share the underlying infrastructure with different application requirements and …
ECP: Error-Aware, Cost-Effective and Proactive Network Slicing Framework
AE Aboeleneen, AA Abdellatif… - IEEE Open Journal …, 2024 - ieeexplore.ieee.org
Recent advancements in Software Defined Networks (SDN), Open Radio Access Network
(O-RAN), and 5G technology have significantly expanded the capabilities of wireless …
(O-RAN), and 5G technology have significantly expanded the capabilities of wireless …
Applying machine learning to end-to-end slice SLA decomposition
5G is set to revolutionize the network service industry with unprecedented use-cases in
industrial automation, augmented reality, virtual reality and many other domains. Network …
industrial automation, augmented reality, virtual reality and many other domains. Network …
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