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 with discrete normalized advantage functions for resource management in network slicing
Network slicing promises to provision diversified services with distinct requirements in one
infrastructure. Deep reinforcement learning (eg, deep Q-learning, DQL) is assumed to be an …
infrastructure. Deep reinforcement learning (eg, deep Q-learning, DQL) is assumed to be an …
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 …
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 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 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 …
A constrained reinforcement learning based approach for network slicing
With the proliferation of mobile networks, we face strong diversification of services,
demanding the current network to embed more flexibility. To satisfy this daring need …
demanding the current network to embed more flexibility. To satisfy this daring need …
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 …
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 …
Graph attention network-based multi-agent reinforcement learning for slicing resource management in dense cellular network
Network slicing (NS) management devotes to providing various services to meet distinct
requirements over the same physical communication infrastructure and allocating resources …
requirements over the same physical communication infrastructure and allocating resources …
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- network slicing resource management
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- user mobility resource management
- actor critic resource management
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- network slicing constrained reinforcement