{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T02:40:22Z","timestamp":1730342422998,"version":"3.28.0"},"reference-count":46,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,25]]},"DOI":"10.23919\/cnsm52442.2021.9615550","type":"proceedings-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T20:30:34Z","timestamp":1638477034000},"page":"216-224","source":"Crossref","is-referenced-by-count":3,"title":["Reinforcement Learning for Automated Energy Efficient Mobile Network Performance Tuning"],"prefix":"10.23919","author":[{"given":"Diarmuid","family":"Corcoran","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Per","family":"Kreuger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Magnus","family":"Boman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.5120\/9594-4216"},{"key":"ref38","first-page":"1","author":"telecommunication","year":"2009","journal-title":"Guidelines for evaluation of radio Mobile Activites of ETRI interface technologies for"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2009.4917524"},{"key":"ref32","first-page":"170","article-title":"A cooperative reinforcement learning approach for inter-cell interference coordination in ofdma cellular networks","author":"dirani","year":"0","journal-title":"Third International Symposium on Modeling and Optimization in Mobile Ad Hoc and Wireless Networks"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2002.1003824"},{"key":"ref30","article-title":"Playing atari with deep reinforcement learning","volume":"abs 1312 5602","author":"mnih","year":"2013","journal-title":"CoRR"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCC49849.2020.9238786"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-804575-6.00012-1"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2014.2374237"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2010.2041230"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/nature24270"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/252804"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2019.2933420"},{"key":"ref12","article-title":"Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case","author":"almasan","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref13","first-page":"3818","article-title":"Data center cooling using model-predictive control","author":"lazic","year":"2018","journal-title":"Proc NeurIPS-18"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913495721"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC49053.2021.9417363"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-49435-3_11"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2011.5876497"},{"journal-title":"4G LTE-Advanced Pro and The Road to 5G","year":"2016","author":"dahlman","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-017-9477-4"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2013.6507402"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2014.6963801"},{"key":"ref27","article-title":"Frequency reuse for 4G technologies: A survey","author":"hindia","year":"0","journal-title":"Proc ICMSCE 2015"},{"journal-title":"LTE Self-Organising Networks Network Management Automation for Operational Efficiency","year":"2012","author":"h\u00e4m\u00e4l\u00e4inen","key":"ref3"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2018.07.015"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1995.7.5.950"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.23919\/CNSM.2017.8255972"},{"journal-title":"Artificial Intelligence in RAN &#x2013; A Software Framework for AI-driven RAN Automation","year":"2020","author":"corcoran","key":"ref8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3004964"},{"journal-title":"3rd Generation Partnership Project Technical Specification Group Radio Access Network NR NR and NG-RAN Overall Description Stage 2 (Release 15)","year":"2018","key":"ref2"},{"journal-title":"Reinforcement Learning An Introduction","year":"2018","author":"sutton","key":"ref9"},{"journal-title":"Evolved Universal Terrestrial Radio Access (E-utra) and Evolved Universal Terrestrial Radio Access Network (Eutran) Overall Description Stage 2","year":"2015","key":"ref1"},{"key":"ref46","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"0","journal-title":"Proc NIPS 32"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MCOMSTD.2017.1700042"},{"journal-title":"Numpy a guide to numpy","year":"2006","author":"oliphant","key":"ref45"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2014.2326303"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2012.6146494"},{"journal-title":"Deep Reinforcement Learning Tutorial","year":"2015","author":"silver","key":"ref42"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/VTCSpring.2015.7145712"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/7969102"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2013.2246820"},{"key":"ref44","first-page":"1057","article-title":"Policy gradient methods for reinforcement learning with function approximation","author":"sutton","year":"0","journal-title":"Proc NIPS 12"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2012.060912.00100"},{"journal-title":"Deep Learning","year":"2016","author":"goodfellow","key":"ref43"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1002\/nem.2014"}],"event":{"name":"2021 17th International Conference on Network and Service Management (CNSM)","start":{"date-parts":[[2021,10,25]]},"location":"Izmir, Turkey","end":{"date-parts":[[2021,10,29]]}},"container-title":["2021 17th International Conference on Network and Service Management (CNSM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9615441\/9615442\/09615550.pdf?arnumber=9615550","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T20:53:28Z","timestamp":1647896008000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9615550\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,25]]},"references-count":46,"URL":"https:\/\/doi.org\/10.23919\/cnsm52442.2021.9615550","relation":{},"subject":[],"published":{"date-parts":[[2021,10,25]]}}}