Vehicular multi-slice optimization in 5G: Dynamic preference policy using reinforcement learning
Network slicing, as an effective way of using heterogeneous network resources, is widely
used in today's radio access network (RAN). However, because of the greater randomness …
used in today's radio access network (RAN). However, because of the greater randomness …
Reinforcement learning for dynamic resource optimization in 5G radio access network slicing
The paper presents a reinforcement learning solution to dynamic resource allocation for 5G
radio access network slicing. Available communication resources (frequency-time blocks …
radio access network slicing. Available communication resources (frequency-time blocks …
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 …
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 …
Two-tier resource allocation in dynamic network slicing paradigm with deep reinforcement learning
Network slicing is treated as a key technology of the rapidly developing 5G system.
Nevertheless, the environment of the users is extremely complex, leading to a great …
Nevertheless, the environment of the users is extremely complex, leading to a great …
Reinforcement learning-based radio access network slicing for a 5G system with support for cellular V2X
HDR Albonda, J Pérez-Romero - … 2019, Poznan, Poland, June 11–12 …, 2019 - Springer
Abstract 5G mobile systems are expected to host a variety of services and applications such
as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) …
as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) …
Proposal of allocating radio resources to multiple slices in 5G using deep reinforcement learning
Y Abiko, D Mochizuki, T Saito, D Ikeda… - 2019 IEEE 8th …, 2019 - ieeexplore.ieee.org
Fifth-generation (5G) mobile communication is expected to provide a suitable network for all
service requirements. Automation of network slicing is required to respond to the …
service requirements. Automation of network slicing is required to respond to the …
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 …
Deep Reinforcement Learning for Online Resource Allocation in Network Slicing
Network slicing is a key enabler of 5G and beyond networks to satisfy the diverse quality of
service (QoS) requirements of different services simultaneously. In network slicing, radio …
service (QoS) requirements of different services simultaneously. In network slicing, radio …
Dynamic Resource Allocation in Network Slicing with Deep Reinforcement Learning
Network slicing is key to enabling 6G and beyond networks to simultaneously meet the
diverse quality of service (QoS) requirements of various services. In network slicing, radio …
diverse quality of service (QoS) requirements of various services. In network slicing, radio …