{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:07:06Z","timestamp":1774631226974,"version":"3.50.1"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"name":"EU H2020 eWINE Project","award":["688116"],"award-info":[{"award-number":["688116"]}]},{"name":"SBO SAMURAI Project"},{"name":"AWS Educate\/GitHub Student Developer Pack"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2018]]},"DOI":"10.1109\/access.2018.2818794","type":"journal-article","created":{"date-parts":[[2018,3,26]],"date-time":"2018-03-26T21:01:51Z","timestamp":1522098111000},"page":"18484-18501","source":"Crossref","is-referenced-by-count":317,"title":["End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7008-7648","authenticated-orcid":false,"given":"Merima","family":"Kulin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3428-9642","authenticated-orcid":false,"given":"Tarik","family":"Kazaz","sequence":"additional","affiliation":[]},{"given":"Ingrid","family":"Moerman","sequence":"additional","affiliation":[]},{"given":"Eli","family":"De Poorter","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","author":"zaidi","year":"2017","journal-title":"Will SDN be part of 5G?"},{"key":"ref38","first-page":"1","article-title":"Hardware accelerated SDR platform for adaptive air interfaces","author":"kazaz","year":"2016","journal-title":"Proc Workshop Future Radio Technol (ETSI) Air Interfaces"},{"key":"ref33","author":"rajendran","year":"2017","journal-title":"Electrosense Open and big spectrum data"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1186\/s13638-015-0291-8"},{"key":"ref31","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref30","author":"kingma","year":"2014","journal-title":"Adam A method for stochastic optimization"},{"key":"ref37","first-page":"173","article-title":"Atomix: A framework for deploying signal processing applications on wireless infrastructure","author":"bansal","year":"2015","journal-title":"Proc NSDI"},{"key":"ref36","first-page":"246","article-title":"Demo: WiSCoP&#x2014;Wireless sensor communication prototyping platform","author":"kazaz","year":"2017","journal-title":"Proc Int Conf Wireless Netw Embedded Syst"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2014.2329213"},{"key":"ref34","author":"zaslavsky","year":"2013","journal-title":"Sensing As A Service and Big Data"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2017.8254105"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2015.05.007"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ascom.2017.01.002"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/INDIN.2017.8104767"},{"key":"ref13","author":"rajendran","year":"2017","journal-title":"Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2017.2758370"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052577"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/1614320.1614323"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2004.839380"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2008.4481339"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2013.01.010"},{"key":"ref28","first-page":"25","article-title":"Transient-based identification of wireless sensor nodes","author":"danev","year":"2009","journal-title":"Proc Int Conf Inf Process Sensor Netw"},{"key":"ref4","first-page":"739","article-title":"Off-road obstacle avoidance through end-to-end learning","author":"m\u00fcller","year":"2006","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2016.1500356WC"},{"key":"ref3","author":"ding","year":"2014","journal-title":"Big spectrum data The new resource for cognitive wireless networking"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/DYSPAN.2007.35"},{"key":"ref29","author":"chollet","year":"2015","journal-title":"Keras"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2183771"},{"key":"ref8","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/DYSPAN.2005.1542629"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2643230.2643235"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/978-3-319-44188-7_16","article-title":"Convolutional radio modulation recognition networks","author":"o\u2019shea","year":"2016","journal-title":"Proc Conf Eng Appl Neural Networks"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2016.2559525"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2043164.2018456"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2014.2311513"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2017.1600404"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/2342441.2342464"},{"key":"ref24","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2014.2355255"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MSPEC.2004.1270548"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/s16060790"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/5.237536"},{"key":"ref25","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8274985\/08325299.pdf?arnumber=8325299","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T07:04:06Z","timestamp":1643180646000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/8325299\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/access.2018.2818794","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]}}}