{"id":"https://openalex.org/W3110347254","doi":"https://doi.org/10.1109/jsac.2020.3041377","title":"PR-NN: RNN-Based Detection for Coded Partial-Response Channels","display_name":"PR-NN: RNN-Based Detection for Coded Partial-Response Channels","publication_year":2020,"publication_date":"2020-12-03","ids":{"openalex":"https://openalex.org/W3110347254","doi":"https://doi.org/10.1109/jsac.2020.3041377","mag":"3110347254"},"language":"en","primary_location":{"id":"doi:10.1109/jsac.2020.3041377","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsac.2020.3041377","pdf_url":null,"source":{"id":"https://openalex.org/S90422530","display_name":"IEEE Journal on Selected Areas in Communications","issn_l":"0733-8716","issn":["0733-8716","1558-0008"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal on Selected Areas in Communications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068119335","display_name":"Simeng Zheng","orcid":"https://orcid.org/0000-0003-2369-6031"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Simeng Zheng","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Center for Memory and Recording Research, University of California at San Diego, La Jolla, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Center for Memory and Recording Research, University of California at San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100330649","display_name":"Yi Liu","orcid":"https://orcid.org/0000-0003-4238-6557"},"institutions":[{"id":"https://openalex.org/I4210153776","display_name":"Apple (United States)","ror":"https://ror.org/059hsda18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153776"]},{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Liu","raw_affiliation_strings":["Apple Corporation, Cupertino, CA, USA","Department of Electrical and Computer Engineering, Center for Memory and Recording Research, University of California at San Diego, La Jolla, CA, USA"],"affiliations":[{"raw_affiliation_string":"Apple Corporation, Cupertino, CA, USA","institution_ids":["https://openalex.org/I4210153776"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Center for Memory and Recording Research, University of California at San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060049531","display_name":"Paul H. Siegel","orcid":"https://orcid.org/0000-0001-5850-0874"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Paul H. Siegel","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Center for Memory and Recording Research, University of California at San Diego, La Jolla, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Center for Memory and Recording Research, University of California at San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068119335"],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":1.4582,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.8622274,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"39","issue":"7","first_page":"1967","last_page":"1982"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9872000217437744,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11321","display_name":"Error Correcting Code Techniques","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7797607183456421},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.7631264328956604},{"id":"https://openalex.org/keywords/viterbi-algorithm","display_name":"Viterbi algorithm","score":0.6985043883323669},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6070243120193481},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5655558109283447},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.46307408809661865},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.46214330196380615},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4615492820739746},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.45379531383514404},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45202115178108215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42289653420448303},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.3644985258579254},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3616427779197693},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3590502440929413},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.17721381783485413}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7797607183456421},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.7631264328956604},{"id":"https://openalex.org/C60582962","wikidata":"https://www.wikidata.org/wiki/Q83886","display_name":"Viterbi algorithm","level":3,"score":0.6985043883323669},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6070243120193481},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5655558109283447},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.46307408809661865},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.46214330196380615},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4615492820739746},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.45379531383514404},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45202115178108215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42289653420448303},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3644985258579254},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3616427779197693},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3590502440929413},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.17721381783485413},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jsac.2020.3041377","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsac.2020.3041377","pdf_url":null,"source":{"id":"https://openalex.org/S90422530","display_name":"IEEE Journal on Selected Areas in Communications","issn_l":"0733-8716","issn":["0733-8716","1558-0008"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal on Selected Areas in Communications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1491748776","https://openalex.org/W1522301498","https://openalex.org/W1588493836","https://openalex.org/W1597836871","https://openalex.org/W1979988243","https://openalex.org/W1991133427","https://openalex.org/W2012753842","https://openalex.org/W2016220155","https://openalex.org/W2020992040","https://openalex.org/W2025128646","https://openalex.org/W2045407304","https://openalex.org/W2064675550","https://openalex.org/W2107192798","https://openalex.org/W2112145383","https://openalex.org/W2119313247","https://openalex.org/W2119732941","https://openalex.org/W2120680665","https://openalex.org/W2122007740","https://openalex.org/W2124235059","https://openalex.org/W2125109000","https://openalex.org/W2131774270","https://openalex.org/W2141284907","https://openalex.org/W2156452163","https://openalex.org/W2157331557","https://openalex.org/W2164445690","https://openalex.org/W2167547511","https://openalex.org/W2169335080","https://openalex.org/W2200601882","https://openalex.org/W2584943905","https://openalex.org/W2786361328","https://openalex.org/W2811484325","https://openalex.org/W2952911118","https://openalex.org/W2963408536","https://openalex.org/W2964070430","https://openalex.org/W2964121744","https://openalex.org/W2964199361","https://openalex.org/W2970898848","https://openalex.org/W2971351707","https://openalex.org/W2976077638","https://openalex.org/W3009052662","https://openalex.org/W3013383814","https://openalex.org/W3081045327","https://openalex.org/W3105371711","https://openalex.org/W3152713649","https://openalex.org/W6631190155","https://openalex.org/W6635303264","https://openalex.org/W6752394437"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W2062804535","https://openalex.org/W3017902212","https://openalex.org/W2964335273","https://openalex.org/W2982145560"],"abstract_inverted_index":{"In":[0],"this":[1,201],"paper,":[2],"we":[3],"investigate":[4],"the":[5,24,51,62,77,93,122,139,147,174],"use":[6],"of":[7,13,74,84,124,129,149,209],"recurrent":[8,46],"neural":[9],"network":[10,63],"(RNN)-based":[11],"detection":[12,26,126,131,144,154],"magnetic":[14,224],"recording":[15,32,89,225],"channels":[16,33],"with":[17,34,104,116,168,183,227],"inter-symbol":[18],"interference":[19],"(ISI).":[20],"We":[21,42],"refer":[22],"to":[23,49,67,82,186,220],"proposed":[25],"method,":[27],"which":[28,92],"is":[29,80,203],"intended":[30],"for":[31],"partial-response":[35,111],"equalization,":[36],"as":[37],"Partial-Response":[38],"Neural":[39],"Network":[40],"(PR-NN).":[41],"train":[43],"bi-directional":[44],"gated":[45],"units":[47],"(bi-GRUs)":[48],"recover":[50],"ISI":[52],"channel":[53,57,112,162,189,226],"inputs":[54],"from":[55],"noisy":[56],"output":[58],"sequences":[59],"and":[60,145,171],"evaluate":[61],"performance":[64,123,148,175,194,217],"when":[65,181,218],"applied":[66,219],"continuous,":[68],"streaming":[69],"data.":[70],"The":[71,88],"computational":[72],"complexity":[73],"PR-NN":[75,125,140,165,191,214],"during":[76],"evaluation":[78],"process":[79],"comparable":[81],"that":[83,121,128,200],"a":[85,98,206,221],"Viterbi":[86,130,143],"detector.":[87],"system":[90],"on":[91],"experiments":[94],"were":[95],"conducted":[96],"uses":[97],"rate-2/3,":[99],"(1,7)":[100],"runlength-limited":[101],"(RLL)":[102],"code":[103],"an":[105],"E":[106],"<sup":[107],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[108],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[109],"PR4":[110],"target.":[113],"Experimental":[114],"results":[115],"ideal":[117],"PR":[118],"signals":[119],"show":[120],"approaches":[127],"in":[132,155],"additive":[133,156],"white":[134],"gaussian":[135],"noise":[136,158],"(AWGN).":[137],"Moreover,":[138],"detector":[141,166],"outperforms":[142],"achieves":[146],"Noise-Predictive":[150],"Maximum":[151],"Likelihood":[152],"(NPML)":[153],"colored":[157],"(ACN)":[159],"at":[160,195],"different":[161,188],"densities.":[163,197],"A":[164],"trained":[167,182],"both":[169,196],"AWGN":[170],"ACN":[172,184],"maintains":[173,192],"observed":[176],"under":[177],"separate":[178],"training.":[179],"Similarly,":[180],"corresponding":[185],"two":[187],"densities,":[190],"its":[193],"Experiments":[198],"confirm":[199],"robustness":[202],"consistent":[204],"over":[205],"wide":[207],"range":[208],"signal-to-noise":[210],"ratios":[211],"(SNRs).":[212],"Finally,":[213],"displays":[215],"robust":[216],"more":[222],"realistic":[223],"MMSE-equalized":[228],"Lorentzian":[229],"signals.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}