{"id":"https://openalex.org/W7155090792","doi":"https://doi.org/10.1109/ncc68160.2026.11479015","title":"Multi-Path Bayesian Inference for Symbol Detection in Affine Frequency Division Multiplexing","display_name":"Multi-Path Bayesian Inference for Symbol Detection in Affine Frequency Division Multiplexing","publication_year":2026,"publication_date":"2026-02-26","ids":{"openalex":"https://openalex.org/W7155090792","doi":"https://doi.org/10.1109/ncc68160.2026.11479015"},"language":null,"primary_location":{"id":"doi:10.1109/ncc68160.2026.11479015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc68160.2026.11479015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 National Conference on Communications (NCC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5134133594","display_name":"A Anoop","orcid":null},"institutions":[{"id":"https://openalex.org/I68695296","display_name":"Indian Institute of Information Technology and Management, Kerala","ror":"https://ror.org/02w9cdh50","country_code":"IN","type":"education","lineage":["https://openalex.org/I68695296"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"A Anoop","raw_affiliation_strings":["Indian Institute of Information Technology Kottayam,Kerala,India,686635"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Information Technology Kottayam,Kerala,India,686635","institution_ids":["https://openalex.org/I68695296"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128874100","display_name":"Christo Kurisummoottil Thomas","orcid":null},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christo Kurisummoottil Thomas","raw_affiliation_strings":["Worcester Polytechnic Institute,Worcester,MA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute,Worcester,MA,USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134101869","display_name":"J V Bibal Benifa","orcid":null},"institutions":[{"id":"https://openalex.org/I68695296","display_name":"Indian Institute of Information Technology and Management, Kerala","ror":"https://ror.org/02w9cdh50","country_code":"IN","type":"education","lineage":["https://openalex.org/I68695296"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"J V Bibal Benifa","raw_affiliation_strings":["Indian Institute of Information Technology Kottayam,Kerala,India,686635"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Information Technology Kottayam,Kerala,India,686635","institution_ids":["https://openalex.org/I68695296"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134096259","display_name":"S Kala","orcid":null},"institutions":[{"id":"https://openalex.org/I68695296","display_name":"Indian Institute of Information Technology and Management, Kerala","ror":"https://ror.org/02w9cdh50","country_code":"IN","type":"education","lineage":["https://openalex.org/I68695296"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"S Kala","raw_affiliation_strings":["Indian Institute of Information Technology Kottayam,Kerala,India,686635"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Information Technology Kottayam,Kerala,India,686635","institution_ids":["https://openalex.org/I68695296"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35935757,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12146","display_name":"Power Line Communications and Noise","score":0.1444000005722046,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12146","display_name":"Power Line Communications and Noise","score":0.1444000005722046,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.11060000211000443,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.08380000293254852,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4620000123977661},{"id":"https://openalex.org/keywords/division","display_name":"Division (mathematics)","score":0.42160001397132874},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.40119999647140503},{"id":"https://openalex.org/keywords/symbol","display_name":"Symbol (formal)","score":0.4011000096797943},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.3880000114440918},{"id":"https://openalex.org/keywords/multiplexing","display_name":"Multiplexing","score":0.38449999690055847},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3828999996185303}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6208000183105469},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5169000029563904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4862000048160553},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4620000123977661},{"id":"https://openalex.org/C60798267","wikidata":"https://www.wikidata.org/wiki/Q1226939","display_name":"Division (mathematics)","level":2,"score":0.42160001397132874},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.40119999647140503},{"id":"https://openalex.org/C134400042","wikidata":"https://www.wikidata.org/wiki/Q2372244","display_name":"Symbol (formal)","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.3880000114440918},{"id":"https://openalex.org/C19275194","wikidata":"https://www.wikidata.org/wiki/Q222903","display_name":"Multiplexing","level":2,"score":0.38449999690055847},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.36410000920295715},{"id":"https://openalex.org/C50661577","wikidata":"https://www.wikidata.org/wiki/Q901831","display_name":"Time-division multiplexing","level":3,"score":0.34389999508857727},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33500000834465027},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.33500000834465027},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C89061704","wikidata":"https://www.wikidata.org/wiki/Q5555726","display_name":"Frequency-division multiplexing","level":4,"score":0.27570000290870667},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.2728999853134155},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ncc68160.2026.11479015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc68160.2026.11479015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 National Conference on Communications (NCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W1506342804","https://openalex.org/W3200668305","https://openalex.org/W4361853646"],"related_works":[],"abstract_inverted_index":{"Affine":[0],"Frequency":[1],"Division":[2],"Multiplexing":[3],"(AFDM)":[4],"achieves":[5],"full":[6],"delay-Doppler":[7,61],"representation":[8],"of":[9,36,98,156],"doubly-dispersive":[10],"channels":[11],"through":[12,122],"chirp-based":[13],"modulation,":[14],"making":[15],"it":[16],"promising":[17],"for":[18,159],"high-mobility":[19,129],"wireless":[20],"communications.":[21],"This":[22],"paper":[23],"proposes":[24],"a":[25,73,96],"Bayesian-based":[26],"symbol":[27,88],"detector":[28,48],"that":[29,63,85,147],"exploits":[30],"the":[31,37,68,92,108],"unique":[32],"sparse":[33],"banded":[34],"structure":[35],"AFDM":[38,152],"effective":[39,154],"channel":[40,120],"matrix":[41],"by":[42,90],"leveraging":[43],"inherent":[44],"path":[45,143,149],"diversity.":[46],"The":[47],"performs":[49],"Bayesian":[50],"inference":[51],"across":[52],"multiple":[53],"independent":[54,99],"channel-impaired":[55],"signal":[56],"copies":[57],"corresponding":[58],"to":[59,107],"distinct":[60],"paths":[62],"are":[64],"completely":[65],"separated":[66],"in":[67,128,151],"DAFT":[69],"domain.":[70],"We":[71],"develop":[72],"multipath":[74,157],"parallel":[75],"interference":[76],"cancellation":[77],"scheme":[78],"combined":[79],"with":[80,138],"reliability-weighted":[81],"decision":[82],"statistics":[83],"combining":[84],"iteratively":[86],"refines":[87],"estimates":[89],"formulating":[91],"posterior":[93],"probability":[94],"as":[95,142],"product":[97],"Gaussian":[100],"distributions":[101],"from":[102],"each":[103],"resolvable":[104],"path.":[105],"Convergence":[106],"linear":[109],"minimum":[110],"mean":[111],"square":[112],"error":[113],"solution":[114],"is":[115],"established":[116],"under":[117],"positive":[118],"definite":[119],"conditions":[121],"spectral":[123],"radius":[124],"analysis.":[125],"Simulation":[126],"results":[127],"scenarios":[130],"demonstrate":[131],"substantial":[132],"performance":[133],"gains":[134],"over":[135],"conventional":[136],"detectors,":[137],"improvements":[139],"increasing":[140],"significantly":[141],"diversity":[144,158],"grows,":[145],"validating":[146],"complete":[148],"separation":[150],"enables":[153],"exploitation":[155],"enhanced":[160],"detection.":[161]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-04-22T00:00:00"}