Joint multi-objective optimization for radio access network slicing using multi-agent deep reinforcement learning
Radio access network (RAN) slices can provide various customized services for next-
generation wireless networks. Thus, multiple performance metrics of different types of RAN …
generation wireless networks. Thus, multiple performance metrics of different types of RAN …
Flexible resource block allocation to multiple slices for radio access network slicing using deep reinforcement learning
Y Abiko, T Saito, D Ikeda, K Ohta, T Mizuno… - IEEE …, 2020 - ieeexplore.ieee.org
In the fifth-generation of mobile communications, network slicing is used to provide an
optimal network for various services as a slice. In this paper, we propose a radio access …
optimal network for various services as a slice. In this paper, we propose a radio access …
Intelligent radio access network slicing for service provisioning in 6G: A hierarchical deep reinforcement learning approach
Network slicing is a key paradigm in 5G and is expected to be inherited in future 6G
networks for the concurrent provisioning of diverse quality of service (QoS). Unfortunately …
networks for the concurrent provisioning of diverse quality of service (QoS). Unfortunately …
Transfer learning-based accelerated deep reinforcement learning for 5G RAN slicing
AM Nagib, H Abou-Zeid… - 2021 IEEE 46th …, 2021 - ieeexplore.ieee.org
Deep Reinforcement Learning (DRL) algorithms have been recently proposed to solve
dynamic Radio Resource Management (RRM) problems in 5G networks. However, the slow …
dynamic Radio Resource Management (RRM) problems in 5G networks. However, the slow …
Dynamic SDN-based radio access network slicing with deep reinforcement learning for URLLC and eMBB services
A Filali, Z Mlika, S Cherkaoui… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Radio access network (RAN) slicing is a key technology that enables 5G network to support
heterogeneous requirements of generic services, namely ultra-reliable low-latency …
heterogeneous requirements of generic services, namely ultra-reliable low-latency …
A realization of fog-RAN slicing via deep reinforcement learning
To meet the wide range of 5G use cases in a cost-efficient way, network slicing has been
advocated as a key enabler. Unlike the core network slicing in a virtualized environment …
advocated as a key enabler. Unlike the core network slicing in a virtualized environment …
Online convex optimization for efficient and robust inter-slice radio resource management
T Wang, S Wang - IEEE Transactions on Communications, 2021 - ieeexplore.ieee.org
Radio access network (RAN) slicing is one of the key technologies in 5G and beyond mobile
networks, where multiple logical subnets, ie, RAN slices, are allowed to run on top of the …
networks, where multiple logical subnets, ie, RAN slices, are allowed to run on top of the …
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 …
Intelligent resource scheduling for 5G radio access network slicing
It is widely acknowledged that network slicing can tackle the diverse use cases and
connectivity services of the forthcoming next-generation mobile networks (5G). Resource …
connectivity services of the forthcoming next-generation mobile networks (5G). Resource …
Reinforcement learning for radio resource management in ran slicing: A survey
M Zangooei, N Saha, M Golkarifard… - IEEE Communications …, 2023 - ieeexplore.ieee.org
Dynamic radio resource allocation to network slices in mobile networks is challenging due to
the diverse requirements of RAN slices and the dynamic environment of wireless networks …
the diverse requirements of RAN slices and the dynamic environment of wireless networks …