{"id":"https://openalex.org/W2131086249","doi":"https://doi.org/10.1109/tit.1972.1054829","title":"Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference","display_name":"Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference","publication_year":1972,"publication_date":"1972-05-01","ids":{"openalex":"https://openalex.org/W2131086249","doi":"https://doi.org/10.1109/tit.1972.1054829","mag":"2131086249"},"language":"en","primary_location":{"id":"doi:10.1109/tit.1972.1054829","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.1972.1054829","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"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 Transactions on Information Theory","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/A5023619541","display_name":"G. David Forney","orcid":"https://orcid.org/0000-0003-1711-5974"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"G. Forney","raw_affiliation_strings":["Codex Corporation, Newton, MA, USA","Department of Electrical Engineering, University of Stanford, Stanford, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Codex Corporation, Newton, MA, USA","institution_ids":[]},{"raw_affiliation_string":"Department of Electrical Engineering, University of Stanford, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5023619541"],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":23.8745,"has_fulltext":false,"cited_by_count":2468,"citation_normalized_percentile":{"value":1.0,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"18","issue":"3","first_page":"363","last_page":"378"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9998000264167786,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/intersymbol-interference","display_name":"Intersymbol interference","score":0.9439105987548828},{"id":"https://openalex.org/keywords/viterbi-algorithm","display_name":"Viterbi algorithm","score":0.7663160562515259},{"id":"https://openalex.org/keywords/maximum-likelihood-sequence-estimation","display_name":"Maximum likelihood sequence estimation","score":0.7166072726249695},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6620240211486816},{"id":"https://openalex.org/keywords/matched-filter","display_name":"Matched filter","score":0.6413300037384033},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5282092690467834},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.515982449054718},{"id":"https://openalex.org/keywords/digital-filter","display_name":"Digital filter","score":0.50174880027771},{"id":"https://openalex.org/keywords/nyquist-isi-criterion","display_name":"Nyquist ISI criterion","score":0.5000429153442383},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4842779040336609},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4811628758907318},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.4743936061859131},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.46956586837768555},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.4349602460861206},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4339432716369629},{"id":"https://openalex.org/keywords/root-raised-cosine-filter","display_name":"Root-raised-cosine filter","score":0.42559614777565},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.4162348508834839},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.4062759280204773},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2681490480899811},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.21411457657814026},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.17091572284698486},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.1350710093975067}],"concepts":[{"id":"https://openalex.org/C97812054","wikidata":"https://www.wikidata.org/wiki/Q2166055","display_name":"Intersymbol interference","level":3,"score":0.9439105987548828},{"id":"https://openalex.org/C60582962","wikidata":"https://www.wikidata.org/wiki/Q83886","display_name":"Viterbi algorithm","level":3,"score":0.7663160562515259},{"id":"https://openalex.org/C191462741","wikidata":"https://www.wikidata.org/wiki/Q6795902","display_name":"Maximum likelihood sequence estimation","level":3,"score":0.7166072726249695},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6620240211486816},{"id":"https://openalex.org/C50151734","wikidata":"https://www.wikidata.org/wiki/Q1759577","display_name":"Matched filter","level":3,"score":0.6413300037384033},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5282092690467834},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.515982449054718},{"id":"https://openalex.org/C36390408","wikidata":"https://www.wikidata.org/wiki/Q1163067","display_name":"Digital filter","level":3,"score":0.50174880027771},{"id":"https://openalex.org/C106743206","wikidata":"https://www.wikidata.org/wiki/Q7071249","display_name":"Nyquist ISI criterion","level":4,"score":0.5000429153442383},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4842779040336609},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4811628758907318},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.4743936061859131},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.46956586837768555},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.4349602460861206},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4339432716369629},{"id":"https://openalex.org/C76826599","wikidata":"https://www.wikidata.org/wiki/Q1248611","display_name":"Root-raised-cosine filter","level":4,"score":0.42559614777565},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.4162348508834839},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.4062759280204773},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2681490480899811},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.21411457657814026},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.17091572284698486},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.1350710093975067},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tit.1972.1054829","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.1972.1054829","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"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 Transactions on Information Theory","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":37,"referenced_works":["https://openalex.org/W1512002052","https://openalex.org/W1515682093","https://openalex.org/W1536252645","https://openalex.org/W1577906631","https://openalex.org/W1991133427","https://openalex.org/W2011816398","https://openalex.org/W2014747900","https://openalex.org/W2039657199","https://openalex.org/W2040712217","https://openalex.org/W2050927701","https://openalex.org/W2064084810","https://openalex.org/W2066383520","https://openalex.org/W2086239785","https://openalex.org/W2096159957","https://openalex.org/W2096546463","https://openalex.org/W2105640639","https://openalex.org/W2107461050","https://openalex.org/W2109203631","https://openalex.org/W2116324626","https://openalex.org/W2118880836","https://openalex.org/W2118968654","https://openalex.org/W2120981061","https://openalex.org/W2122683098","https://openalex.org/W2123001158","https://openalex.org/W2126615699","https://openalex.org/W2139174935","https://openalex.org/W2139724070","https://openalex.org/W2140928791","https://openalex.org/W2145319000","https://openalex.org/W2146719188","https://openalex.org/W2151814693","https://openalex.org/W2161609916","https://openalex.org/W2161905308","https://openalex.org/W2166553701","https://openalex.org/W2166851988","https://openalex.org/W2169252787","https://openalex.org/W2319346475"],"related_works":["https://openalex.org/W2779232303","https://openalex.org/W1998055811","https://openalex.org/W2159401519","https://openalex.org/W2164818839","https://openalex.org/W4388118371","https://openalex.org/W2152887920","https://openalex.org/W2099739932","https://openalex.org/W2551684240","https://openalex.org/W2024650855","https://openalex.org/W2131086249"],"abstract_inverted_index":{"A":[0],"maximum-likelihood":[1],"sequence":[2,8],"estimator":[3],"for":[4,52,65,145],"a":[5,25,30,35,60,71,138],"digital":[6],"pulse-amplitude-modulated":[7],"in":[9,125],"the":[10,40,46,68,146],"presence":[11],"of":[12,45,62,67],"finite":[13],"intersymbol":[14,133],"interference":[15,134],"and":[16,34,95,101,124],"white":[17],"Gaussian":[18],"noise":[19],"is":[20,86,105,112,128,151],"developed,":[21],"The":[22,43,83],"structure":[23,123],"comprises":[24],"sampled":[26,50],"linear":[27,81],"filter,":[28,33,49],"called":[29,39],"whitened":[31,47],"matched":[32,48],"recursive":[36],"nonlinear":[37,93],"processor,":[38],"Viterbi":[41,84],"algorithm.":[42],"outputs":[44],"once":[51],"each":[53],"input":[54,69],"symbol,":[55],"are":[56],"shown":[57,106],"to":[58,88],"form":[59],"set":[61],"sufficient":[63],"statistics":[64],"estimation":[66],"sequence,":[70],"fact":[72],"that":[73,107],"makes":[74],"obvious":[75],"some":[76],"earlier":[77,91],"results":[78],"on":[79],"optimum":[80,92,142],"processors.":[82],"algorithm":[85,143],"easier":[87],"implement":[89],"than":[90],"processors":[94],"its":[96],"performance":[97,108],"can":[98],"be":[99,118],"straightforwardly":[100],"accurately":[102],"estimated.":[103],"It":[104],"(by":[109],"whatever":[110],"criterion)":[111],"effectively":[113,141],"as":[114,116,129,131],"good":[115,130],"could":[117],"attained":[119],"by":[120],"any":[121],"receiver":[122],"many":[126],"cases":[127],"if":[132],"were":[135],"absent.":[136],"Finally,":[137],"simplified":[139],"but":[140],"suitable":[144],"most":[147],"popular":[148],"partial-response":[149],"schemes":[150],"described.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":26},{"year":2021,"cited_by_count":28},{"year":2020,"cited_by_count":24},{"year":2019,"cited_by_count":26},{"year":2018,"cited_by_count":34},{"year":2017,"cited_by_count":61},{"year":2016,"cited_by_count":52},{"year":2015,"cited_by_count":52},{"year":2014,"cited_by_count":43},{"year":2013,"cited_by_count":59},{"year":2012,"cited_by_count":52}],"updated_date":"2026-07-27T08:26:11.824852","created_date":"2025-10-10T00:00:00"}
