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 …
Machine learning for network slicing resource management: A comprehensive survey
B Han, HD Schotten - arXiv preprint arXiv:2001.07974, 2020 - arxiv.org
The emerging technology of multi-tenancy network slicing is considered as an essential
feature of 5G cellular networks. It provides network slices as a new type of public cloud …
feature of 5G cellular networks. It provides network slices as a new type of public cloud …
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 …
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 …
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 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 …
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 …
DeepSlice: A deep learning approach towards an efficient and reliable network slicing in 5G networks
Existing cellular communications and the upcoming 5G mobile network requires meeting
high-reliability standards, very low latency, higher capacity, more security, and high-speed …
high-reliability standards, very low latency, higher capacity, more security, and high-speed …
Deep reinforcement learning for network slicing with heterogeneous resource requirements and time varying traffic dynamics
Efficient network slicing is vital to deal with the highly variable and dynamic characteristics of
traffic in 5G networks. Network slicing addresses a challenging dynamic network resource …
traffic in 5G networks. Network slicing addresses a challenging dynamic network resource …
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 …
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