Machine learning-based slice management in 5G networks for emergency scenarios

Title

Machine learning-based slice management in 5G networks for emergency scenarios

Author

Arora, A.
Dimitrovski, T.
Litjens, R.
Zhang, H.

Publication year

Subject

5G
Emergency scenarios
Machine learning
Network slicing
Slice management
Learning systems
Queueing networks
Reinforcement learning
Resource allocation
Alternative solutions
Background traffic
Emergency scenario
Innovative method
Multi armed bandit
Radio resource allocation
Reinforcement learning models
Resource demands
5G mobile communication systems

To reference this document use:

http://resolver.tudelft.nl/uuid:57356d81-d97b-4c2c-a0c2-7d8302687b04

TNO identifier

958442

Publisher

Institute of Electrical and Electronics Engineers Inc.

Source

2021 Joint European Conference on Networks and Communications and 6G Summit, EuCNC/6G Summit 2021, Joint 30th European Conference on Networks and Communications and 3rd 6G Summit, EuCNC/6G Summit 2021, 8 June 2021 through 11 June 2021, 193-198

Document type

conference paper

Files

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