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

conference paper
2021
Arora, A.

Dimitrovski, T.

Litjens, R.

Zhang, H.
Topics
5GEmergency scenariosMachine learningNetwork slicingSlice managementLearning systemsQueueing networksReinforcement learningResource allocationAlternative solutionsBackground trafficEmergency scenarioInnovative methodMulti armed banditRadio resource allocationReinforcement learning modelsResource demands5G mobile communication systems
TNO Identifier
958442
Repository link
https://resolver.tno.nl/uuid:57356d81-d97b-4c2c-a0c2-7d8302687b04
Publisher
Institute of Electrical and Electronics Engineers Inc.
Article nr.
9482547
Source title
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
Pages
193-198
Files
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