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 …
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 …
Advancing RAN slicing with offline reinforcement learning
Dynamic radio resource management (RRM) in wireless networks presents significant
challenges, particularly in the context of Radio Access Network (RAN) slicing. This …
challenges, particularly in the context of Radio Access Network (RAN) slicing. This …
Intent-aware radio resource scheduling in a ran slicing scenario using reinforcement learning
Network slicing at the radio access network (RAN) domain, called RAN slicing, requires
elasticity, efficient resource sharing, and customization. In this scenario, radio resource …
elasticity, efficient resource sharing, and customization. In this scenario, radio resource …
Accelerating reinforcement learning via predictive policy transfer in 6G RAN slicing
AM Nagib, H Abou-Zeid… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Reinforcement Learning (RL) algorithms have recently been proposed to solve dynamic
radio resource management (RRM) problems in beyond 5G networks. However, RL-based …
radio resource management (RRM) problems in beyond 5G networks. However, RL-based …
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 …
Efficient handover mechanism for radio access network slicing by exploiting distributed learning
Network slicing is identified as a fundamental architectural technology for future mobile
networks since it can logically separate networks into multiple slices and provide tailored …
networks since it can logically separate networks into multiple slices and provide tailored …
Deep reinforcement learning for resource management in network slicing
Network slicing is born as an emerging business to operators by allowing them to sell the
customized slices to various tenants at different prices. In order to provide better-performing …
customized slices to various tenants at different prices. In order to provide better-performing …
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 …
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 …