{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T22:11:20Z","timestamp":1767996680178,"version":"3.49.0"},"reference-count":107,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"name":"Fondazione CaRiPaRo through the Project A Novel Approach to Wireless Networking based on Cognitive Science and Distributed Intelligence within the framework \u201cProgetti di Eccellenza 2012.\u201d"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2015]]},"DOI":"10.1109\/access.2015.2471178","type":"journal-article","created":{"date-parts":[[2015,8,21]],"date-time":"2015-08-21T18:44:51Z","timestamp":1440182691000},"page":"1512-1530","source":"Crossref","is-referenced-by-count":98,"title":["Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence"],"prefix":"10.1109","volume":"3","author":[{"given":"Michele","family":"Zorzi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3671-5190","authenticated-orcid":false,"given":"Andrea","family":"Zanella","sequence":"additional","affiliation":[]},{"given":"Alberto","family":"Testolin","sequence":"additional","affiliation":[]},{"given":"Michele","family":"De Filippo De Grazia","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Zorzi","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/1064212.1064220"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2008.080406"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2007.4300983"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2012.100412.00017"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2010.5547921"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2012.062012.111342"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICAC.2004.1301346"},{"key":"ref36","first-page":"36","article-title":"Failure diagnosis using decision trees","author":"zheng","year":"2004","journal-title":"Proc Int Conf Auto Comput"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2004.08.027"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ISSNIP.2007.4496871"},{"key":"ref28","first-page":"23","article-title":"Cognitive wireless networks: Your network just became a teenager","author":"m\u00e4h\u00f6nen","year":"2006","journal-title":"Proc IEEE InfoCom"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/863955.863957"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4020-5979-7_20"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2006.05.001"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2009.01.002"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2009.01.001"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/DYSPAN.2005.1542652"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4020-5979-7"},{"key":"ref101","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"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2006.273099"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2008.11-07-647"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4020-5542-3_2"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2012.6214281"},{"key":"ref51","author":"sutton","year":"1998","journal-title":"Reinforcement Learning"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.1997.0101"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1038\/nn.2996"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2013.00251"},{"key":"ref54","first-page":"1","article-title":"Deep learning of representations for unsupervised and transfer learning","volume":"7","author":"bengio","year":"2011","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2013.00515"},{"key":"ref52","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/7011.001.0001","author":"hinton","year":"1999","journal-title":"Unsupervised Learning Foundations of Neural Computation"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/1163593.1163596"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcan.2015.02.001"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MVT.2013.2269187"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/MedHocNet.2014.6849115"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2011.011811.101798"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/icc.2011.5963296"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC.2010.5671622"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2012.6384454"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2011.2176324"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2012.021312.00116"},{"key":"ref45","author":"agoulmine","year":"2010","journal-title":"Autonomic Network Management Principles From Concepts to Applications"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1002\/bltj.20463"},{"key":"ref47","first-page":"170","article-title":"A cooperative reinforcement learning approach for inter-cell interference coordination in OFDMA cellular networks","author":"dirani","year":"2010","journal-title":"Proc Int Symp Model Optim Mobile Ad-Hoc Wireless Netw (WiOpt)"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/2486001.2486020"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/1162678.1162679"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/2486001.2486025"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2009.2037812"},{"key":"ref73","first-page":"609","article-title":"ImageNet classification with deep convolutional neural networks","volume":"24","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref72","first-page":"1","article-title":"Building high-level features using large scale unsupervised learning","author":"le","year":"2012","journal-title":"Proc 29th Int Conf Mach Learn"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553486"},{"key":"ref70","first-page":"1232","article-title":"Large scale distributed deep networks","volume":"24","author":"dean","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1021\/ci500747n"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1126\/science.1254806"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2011.2109382"},{"key":"ref75","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"collobert","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref79","first-page":"1601","article-title":"The recurrent temporal restricted Boltzmann machine","volume":"21","author":"sutskever","year":"2008","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog0901_7"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1162\/089976602760128018"},{"key":"ref61","author":"koller","year":"2009","journal-title":"Probabilistic Graphical Models Principles and Techniques"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1561\/2200000001"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"ref65","first-page":"153","article-title":"Greedy layer-wise training of deep networks","volume":"19","author":"bengio","year":"2007","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2007.09.004"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/1.1.1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2014.6812298"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/1365490.1365500"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2014.6736746"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/WoWMoM.2014.6918997"},{"key":"ref94","first-page":"1345","article-title":"Modeling human motion using binary latent variables","volume":"19","author":"taylor","year":"2007","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref107","first-page":"621","article-title":"Parallelization of deep networks","author":"de filippo de grazia","year":"2012","journal-title":"Proc ESANN"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(01)00086-3"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2014.6894459"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2009.2026102"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1145\/2666652.2666656"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2014.7040217"},{"key":"ref104","first-page":"297","article-title":"Can&#x2019;t you hear me knocking: Identification of user actions on Android apps via traffic analysis","author":"conti","year":"2015","journal-title":"Proc 5th ACM SIGSAC CODASPY"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2014.140407"},{"key":"ref103","first-page":"41","article-title":"Cache privacy in named-data networking","author":"\u00e1cs","year":"2013","journal-title":"Proc 33rd Int Conf Distrib Comput Syst (ICDCS)"},{"key":"ref102","first-page":"2654","article-title":"Do deep nets really need to be deep?","volume":"27","author":"ba","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/QOMEX.2010.5518277"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/WIOPT.2014.6850361"},{"key":"ref97","year":"2003","journal-title":"Advanced video coding for generic audiovisual services"},{"key":"ref10","author":"h\u00e4m\u00e4l\u00e4inen","year":"2012","journal-title":"LTE Self-Organising Networks (SON) Network Management Automation for Operational Efficiency"},{"key":"ref11","year":"2008","journal-title":"Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Overall description Stage 2"},{"key":"ref12","year":"2008","journal-title":"Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Self-configuring and Self-optimizing Network (SON) Use Cases and Solutions"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2013.081313.00231"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2005.1561928"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2005.1404568"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2014.2371999"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref17","article-title":"Cognitive radio: An integrated agent architecture for software defined radio","author":"mitola","year":"2000"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1111\/cogs.12258"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2004.839380"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2012.00151"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2008.929286"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2012.10.038"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553505"},{"key":"ref89","first-page":"2222","article-title":"Multimodal learning with deep Boltzmann machines","author":"srivastava","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref85","first-page":"3","article-title":"HyperFlow: A distributed control plane for OpenFlow","author":"tootoonchian","year":"2010","journal-title":"Proc Internet Netw Manage Conf Res Enterprise Netw"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/ICCNC.2015.7069422"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/MedHocNet.2014.6849102"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-280910-1.50012-X"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/7042252\/07217798.pdf?arnumber=7217798","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T02:35:25Z","timestamp":1633919725000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/7217798\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":107,"URL":"https:\/\/doi.org\/10.1109\/access.2015.2471178","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015]]}}}