{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T06:50:53Z","timestamp":1759042253439,"version":"3.37.3"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ18F010001"],"award-info":[{"award-number":["LQ18F010001"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NSFC 61674128","61731019"],"award-info":[{"award-number":["NSFC 61674128","61731019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2018.2890146","type":"journal-article","created":{"date-parts":[[2018,12,28]],"date-time":"2018-12-28T19:45:46Z","timestamp":1546026346000},"page":"7965-7976","source":"Crossref","is-referenced-by-count":19,"title":["An AMP-Based Low Complexity Generalized Sparse Bayesian Learning Algorithm"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7646-2776","authenticated-orcid":false,"given":"Jiang","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Lin","family":"Han","sequence":"additional","affiliation":[]},{"given":"Xiangming","family":"Meng","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2018.2876806"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2018.8437815"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2017.8006946"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2016.7541826"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2011.6033942"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2015.2455527"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2018.2834904"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2789163"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2017.2764855"},{"key":"ref34","first-page":"1512","article-title":"Vector approximate message passing for the generalized linear model","author":"schniter","year":"2017","journal-title":"Proc Asilomar Conf Signals Syst Comput"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2007.914728"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2007.894265"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2013.6738433"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2010.5684036"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2816739"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/IST.2017.8261491"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2011.00771.x"},{"key":"ref16","first-page":"877","article-title":"Iterative hard thresholding and \n$l_{0}$\n regulatization","volume":"3","author":"blumensath","year":"2007","journal-title":"Proc IEEE ICASP"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.20042"},{"key":"ref18","first-page":"211","article-title":"Sparse Bayesian learning and the relevance vector machine","volume":"1","author":"tipping","year":"2001","journal-title":"J Mach Learn Res"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2004.831016"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2653119"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2272287"},{"journal-title":"Vector approximate message passing","year":"2016","author":"rangan","key":"ref27"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2191780"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2222382"},{"journal-title":"ADC Performance Survey 1997–2013","year":"2016","author":"murmann","key":"ref29"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2273196"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2015.2391287"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2014.2313766"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ITWKSPS.2010.5503193"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2806351"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.871582"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"18914","DOI":"10.1073\/pnas.0909892106","article-title":"Message-passing algorithms for compressed sensing","volume":"106","author":"donoho","year":"2009","journal-title":"Proc Nat Acad Sci USA"},{"key":"ref22","first-page":"2177","article-title":"Expectation consistent approximate inference","volume":"6","author":"opper","year":"2005","journal-title":"J Mach Learn Res"},{"article-title":"A family of algorithms for approximate Bayesian inference","year":"2001","author":"minka","key":"ref21"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178325"},{"journal-title":"The Matrix Cookbook","year":"2012","author":"petersen","key":"ref41"},{"journal-title":"Concise Derivation of Complex Bayesian Approximate Message Passing Via Expectation Propagation","year":"2015","author":"meng","key":"ref23"},{"journal-title":"Approximate message passing with unitary transformation","year":"2015","author":"guo","key":"ref26"},{"key":"ref25","first-page":"1123","article-title":"Swept approximate message passing for sparse estimation","author":"manoel","year":"2015","journal-title":"Proceedings of the 32nd Intl Conf on Machine Learning"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08594658.pdf?arnumber=8594658","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:02:35Z","timestamp":1643241755000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8594658\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/access.2018.2890146","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2019]]}}}