{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:31:01Z","timestamp":1773246661255,"version":"3.50.1"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"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":[[2021,6]]},"DOI":"10.1109\/icc42927.2021.9500495","type":"proceedings-article","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T20:49:21Z","timestamp":1628282961000},"page":"1-7","source":"Crossref","is-referenced-by-count":13,"title":["On Latency Prediction with Deep Learning and Passive Probing at High Mobility"],"prefix":"10.1109","author":[{"given":"Daniel Fabian","family":"Kulzer","sequence":"first","affiliation":[{"name":"New Technologies, Innovations,BMW Group Research,Garching,Germany,85748"}]},{"given":"Firas","family":"Debbichi","sequence":"additional","affiliation":[{"name":"New Technologies, Innovations,BMW Group Research,Garching,Germany,85748"}]},{"given":"Slawomir","family":"Stanczak","sequence":"additional","affiliation":[{"name":"Technical University of Berlin,Berlin,Germany,10587"}]},{"given":"Mladen","family":"Botsov","sequence":"additional","affiliation":[{"name":"BMW Group,Munich,Germany,80937"}]}],"member":"263","reference":[{"key":"ref10","first-page":"4765","article-title":"A unified approach to interpreting model predictions","author":"lundberg","year":"0","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6"},{"key":"ref12","first-page":"3146","article-title":"LightGBM: A highly efficient gradient boosting decision tree","author":"ke","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref13","author":"murphy","year":"2012","journal-title":"Machine Learning A Probabilistic Perspective"},{"key":"ref14","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref15","article-title":"C-V2X use cases: Methodology, examples and service level requirements","year":"2019","journal-title":"White Paper"},{"key":"ref16","article-title":"Test case definition and trial site description part 1","volume":"2","year":"2020","journal-title":"Deliverable"},{"key":"ref17","article-title":"Specification of 5G NetMobil Use Cases, their KPIs, requirements and evaluation methodology","volume":"1","year":"2017","journal-title":"Deliverable"},{"key":"ref18","first-page":"1648","article-title":"Active learning literature survey","author":"settles","year":"2010"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.aci.2018.08.003"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/LCNW.2014.6927712"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2013.6555324"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2017.2773824"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/VTCFall.2017.8288296"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.23919\/INM.2017.7987345"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2017.2756937"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2015.2453391"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-19260-9_5"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2930109"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3115\/981658.981684"},{"key":"ref22","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","author":"lee","year":"2013","journal-title":"Workshop on Challenges in Representation Learning"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761934"}],"event":{"name":"ICC 2021 - IEEE International Conference on Communications","location":"Montreal, QC, Canada","start":{"date-parts":[[2021,6,14]]},"end":{"date-parts":[[2021,6,23]]}},"container-title":["ICC 2021 - IEEE International Conference on Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9500243\/9500244\/09500495.pdf?arnumber=9500495","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:48:43Z","timestamp":1652197723000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9500495\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/icc42927.2021.9500495","relation":{},"subject":[],"published":{"date-parts":[[2021,6]]}}}