Print Email Facebook Twitter 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 2021 Subject 5GEmergency scenariosMachine learningNetwork slicingSlice managementLearning systemsQueueing networksReinforcement learningResource allocationAlternative solutionsBackground trafficEmergency scenarioInnovative methodMulti armed banditRadio resource allocationReinforcement learning modelsResource demands5G 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 To receive the publication files, please send an e-mail request to TNO Library.