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 …
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 …
Deep reinforcement learning for adaptive network slicing in 5G for intelligent vehicular systems and smart cities
Intelligent vehicular systems and smart city applications are the fastest growing Internet-of-
Things (IoT) implementations at a compound annual growth rate of 30%. In view of the …
Things (IoT) implementations at a compound annual growth rate of 30%. In view of the …
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 …
Deep reinforcement learning-based network slicing for beyond 5G
With the advent of 5G era, network slicing has received a great deal of attention as a means
to support a variety of wireless services in a flexible manner. Network slicing is a technique …
to support a variety of wireless services in a flexible manner. Network slicing is a technique …
GAN-powered deep distributional reinforcement learning for resource management in network slicing
Network slicing is a key technology in 5G communications system. Its purpose is to
dynamically and efficiently allocate resources for diversified services with distinct …
dynamically and efficiently allocate resources for diversified services with distinct …
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 …
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 …
An efficient RAN slicing strategy for a heterogeneous network with eMBB and V2X services
HDR Albonda, J Pérez-Romero - IEEE access, 2019 - ieeexplore.ieee.org
Emerging 5G wireless technology will support services and use cases with vastly
heterogeneous requirements. Network slicing, which allows composing multiple dedicated …
heterogeneous requirements. Network slicing, which allows composing multiple dedicated …
The LSTM-based advantage actor-critic learning for resource management in network slicing with user mobility
Network slicing aims to efficiently provision diversified services with distinct requirements
over the same physical infrastructure. Therein, in order to efficiently allocate resources …
over the same physical infrastructure. Therein, in order to efficiently allocate resources …