Edgeslice: Slicing wireless edge computing network with decentralized deep reinforcement learning
5G and edge computing will serve various emerging use cases that have diverse
requirements of multiple resources, eg, radio, transportation, and computing. Network slicing …
requirements of multiple resources, eg, radio, transportation, and computing. Network slicing …
Deep Q-learning-based dynamic network slicing and task offloading in edge network
Recently, Edge Computing (EC) has become a promising enabler to support emerging
applications in 5G mobile networks by offloading compute-intensive tasks from devices to …
applications in 5G mobile networks by offloading compute-intensive tasks from devices to …
Customized slicing for 6G: Enforcing artificial intelligence on resource management
Next generation wireless networks are expected to support diverse vertical industries and
offer countless emerging use cases. To satisfy stringent requirements of diversified services …
offer countless emerging use cases. To satisfy stringent requirements of diversified services …
DeepSlicing: Deep reinforcement learning assisted resource allocation for network slicing
Network slicing enables multiple virtual networks run on the same physical infrastructure to
support various use cases in 5G and beyond. These use cases, however, have very diverse …
support various use cases in 5G and beyond. These use cases, however, have very diverse …
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 …
Data-driven dynamic resource scheduling for network slicing: A deep reinforcement learning approach
Network slicing is designed to support a variety of emerging applications with diverse
performance and flexibility requirements, by dividing the physical network into multiple …
performance and flexibility requirements, by dividing the physical network into multiple …
Reinforcement learning based resource management for network slicing
Network slicing to create multiple virtual networks, called network slice, is a promising
technology to enable networking resource sharing among multiple tenants for the 5th …
technology to enable networking resource sharing among multiple tenants for the 5th …
Blockchain-based computing resource trading in autonomous multi-access edge network slicing: A dueling double deep Q-learning approach
T Kwantwi, G Sun, NAE Kuadey… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
We investigate the computing resource allocation in multi-access edge network slicing (NS)
in the context of revenue and multi-access edge computing (MEC) resource management …
in the context of revenue and multi-access edge computing (MEC) resource management …
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 …
Admission control for 5G core network slicing based on deep reinforcement learning
WF Villota-Jacome, OMC Rendon… - IEEE Systems …, 2022 - ieeexplore.ieee.org
Network slicing is a promising technology for providing customized logical and virtualized
networks for the fifth-generation (5G) use-cases (enhanced mobile broadband, ultrareliable …
networks for the fifth-generation (5G) use-cases (enhanced mobile broadband, ultrareliable …