Deep reinforcement learning for resource management on network slicing: A survey
JA Hurtado Sánchez, K Casilimas… - Sensors, 2022 - mdpi.com
Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving
5G and 6G networks. A 5G/6G network can comprise various network slices from unique or …
5G and 6G networks. A 5G/6G network can comprise various network slices from unique or …
6G massive radio access networks: Key applications, requirements and challenges
Driven by the emerging use cases in massive access future networks, technological
advancements and evolutions are needed for wireless communications beyond the fifth …
advancements and evolutions are needed for wireless communications beyond the fifth …
Reinforcement learning for intelligent healthcare systems: A comprehensive survey
The rapid increase in the percentage of chronic disease patients along with the recent
pandemic pose immediate threats on healthcare expenditure and elevate causes of death …
pandemic pose immediate threats on healthcare expenditure and elevate causes of death …
Toward tailored resource allocation of slices in 6G networks with softwarization and virtualization
Compared with 5G networks, 6G networks are guaranteed to provide various tailored end-to-
end network services and emerging cloud-edge applications. Network slicing (NS) is …
end network services and emerging cloud-edge applications. Network slicing (NS) is …
SliceBlock: Context-aware authentication handover and secure network slicing using DAG-blockchain in edge-assisted SDN/NFV-6G environment
IH Abdulqadder, S Zhou - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
Network slicing in a 6G environment is an important research area in the current years.
However, satisfying the demands of network slice requests is a challenging task. Energy …
However, satisfying the demands of network slice requests is a challenging task. Energy …
RAN resource slicing in 5G using multi-agent correlated Q-learning
5G is regarded as a revolutionary mobile network, which is expected to satisfy a vast number
of novel services, ranging from remote health care to smart cities. However, heterogeneous …
of novel services, ranging from remote health care to smart cities. However, heterogeneous …
Adaptive resource optimized edge federated learning in real-time image sensing classifications
With the exponential growth of the Internet of things (IoT) in remote sensing image
applications, network resource orchestration and data privacy are significant aspects to …
applications, network resource orchestration and data privacy are significant aspects to …
Energy-optimal end-to-end network slicing in cloud-based architecture
Network slicing is a promising technology for realizing the vision of supporting a wide range
of services with diverse and heterogeneous service requirements. With network slicing, the …
of services with diverse and heterogeneous service requirements. With network slicing, the …
Learning from peers: Deep transfer reinforcement learning for joint radio and cache resource allocation in 5G RAN slicing
Network slicing is a critical technique for 5G communications that covers radio access
network (RAN), edge, transport and core slicing. The evolving network architecture requires …
network (RAN), edge, transport and core slicing. The evolving network architecture requires …
Machine learning in network slicing-a survey
5G and beyond networks are expected to support a wide range of services, with highly
diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the …
diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the …