{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T23:28:37Z","timestamp":1784244517153,"version":"3.55.0"},"reference-count":68,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","funder":[{"name":"European Union\u2019s Horizon 2020 ICT Cloud Computing Program under the Adaptive Edge\/Cloud Compute and Network to Support Nextgen Applications (ACCORDION) Project","award":["871793"],"award-info":[{"award-number":["871793"]}]},{"name":"European Union\u2019s Horizon 2020 Research and Innovation Program under the Cloud for Holography and Augmented Reality (CHARITY) Project","award":["101016509"],"award-info":[{"award-number":["101016509"]}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland Project 6Genesis","doi-asserted-by":"publisher","award":["318927"],"award-info":[{"award-number":["318927"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Select. Areas Commun."],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1109\/jsac.2021.3078501","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T19:34:16Z","timestamp":1621884856000},"page":"2241-2253","source":"Crossref","is-referenced-by-count":34,"title":["Toward Using Reinforcement Learning for Trigger Selection in Network Slice Mobility"],"prefix":"10.1109","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8917-9670","authenticated-orcid":false,"given":"Rami Akrem","family":"Addad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego Leonel Cadette","family":"Dutra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarik","family":"Taleb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannu","family":"Flinck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Playing atari with deep reinforcement learning","author":"mnih","year":"2013","journal-title":"arXiv 1312 5602"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1002\/spe.995"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/UCC.2018.00035"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/SEAA.2013.23"},{"key":"ref31","year":"2018","journal-title":"System Architecture for the 5G System Stage 2"},{"key":"ref30","year":"2019","journal-title":"Experiential Networked Intelligence (ENI) ENI Use Cases"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/INTECH.2016.7845053"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.23919\/INM.2017.7987308"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992698"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/Eco-friendly.2014.92"},{"key":"ref60","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref62","first-page":"1","article-title":"Replicated softmax: An undirected topic model","author":"hinton","year":"2009","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref61","first-page":"1","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume":"30","author":"maas","year":"2013","journal-title":"Proc ICML"},{"key":"ref63","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2942390"},{"key":"ref64","year":"2016","journal-title":"MOSA!C Lab research group"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2017.1700200"},{"key":"ref65","year":"2020","journal-title":"Triggers Selection in Network Slice Mobility Framework"},{"key":"ref66","article-title":"Deep reinforcement learning in large discrete action spaces","author":"dulac-arnold","year":"2015","journal-title":"arXiv 1512 07679"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-019-09344-w"},{"key":"ref67","article-title":"Trust region policy optimization","author":"schulman","year":"2015","journal-title":"arXiv 1502 05477 [cs]"},{"key":"ref68","article-title":"Proximal policy optimization algorithms","author":"schulman","year":"2017","journal-title":"arXiv 1707 06347"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700108"},{"key":"ref1","year":"2019","journal-title":"White Paper Validating 5G Technology Performance Assessing 5G architecture and Application Scenarios"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1800268"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2019.2943405"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013983"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2941458"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2019.1800498"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2019.2916583"},{"key":"ref50","first-page":"333","article-title":"Function approximation using artificial neural networks","author":"zainuddin","year":"2007","journal-title":"Proc 12th WSEAS Int Conf Appl Math (MATH)"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"ref59","first-page":"155","article-title":"Back-Propagation","volume":"12","author":"jones","year":"1987","journal-title":"Byte"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/BF00115009"},{"key":"ref57","article-title":"Prioritized experience replay","author":"schaul","year":"2015","journal-title":"arXiv 1511 05952"},{"key":"ref56","first-page":"1","article-title":"Policy gradient methods for reinforcement learning with function approximation","author":"sutton","year":"1999","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref55","article-title":"Sample efficient actor-critic with experience replay","author":"wang","year":"2016","journal-title":"arXiv 1611 01224"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1137\/S0363012901385691"},{"key":"ref53","first-page":"1928","article-title":"Asynchronous methods for deep reinforcement learning","author":"mnih","year":"2016","journal-title":"Proc 33rd Int Conf Int Conf Mach Learn (ICML)"},{"key":"ref52","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"mnih","year":"2015","journal-title":"Nature"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600947"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1900423"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40517-4_8"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1800072"},{"key":"ref13","year":"2017","journal-title":"Network Functions Virtualisation (NFV) Release 3 Evolution and Ecosystem Report on Network Slicing Support with ETSI NFV Architecture Framework"},{"key":"ref14","year":"2018","journal-title":"Study on management and orchestration of network slicing for next generation network"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600935"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2930059"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2018.1800267"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CSCN.2018.8581836"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1800289"},{"key":"ref4","year":"2020","journal-title":"5G Evolution and 6G White Paper"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2018.8648002"},{"key":"ref6","year":"2020","journal-title":"6G-Waves Magazine"},{"key":"ref5","article-title":"Key drivers and research challenges for 6G ubiquitous wireless intelligence","author":"latva-aho","year":"2019"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2815638"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2705720"},{"key":"ref49","year":"2019","journal-title":"Multi-Access Edge Computing (MEC) Study on MEC Support for Alternative Virtualization Technologies V2 1 1"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.2197\/ipsjjip.25.153"},{"key":"ref46","author":"sutton","year":"1998","journal-title":"Introduction to Reinforcement Learning"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/2620728.2620744"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2743240"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1613\/jair.301"},{"key":"ref42","year":"2019","journal-title":"Developing Software for Multi-Access Edge Computing"},{"key":"ref41","year":"2018","journal-title":"Mobile Edge Computing (MEC) Deployment of Mobile Edge Computing in an NFV Environment"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/MCC.2014.51"},{"key":"ref43","article-title":"OpenAI gym","author":"brockman","year":"2016","journal-title":"arXiv 1606 01540 [cs]"}],"container-title":["IEEE Journal on Selected Areas in Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/49\/9457209\/09439923.pdf?arnumber=9439923","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T19:18:45Z","timestamp":1643224725000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9439923\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":68,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/jsac.2021.3078501","relation":{},"ISSN":["0733-8716","1558-0008"],"issn-type":[{"value":"0733-8716","type":"print"},{"value":"1558-0008","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7]]}}}