{"id":"https://openalex.org/W7165010536","doi":"https://doi.org/10.48550/arxiv.2606.17705","title":"A Hypothesis-Testing Analysis of Blind Recognition for Polar Codes","display_name":"A Hypothesis-Testing Analysis of Blind Recognition for Polar Codes","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7165010536","doi":"https://doi.org/10.48550/arxiv.2606.17705"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.17705","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17705","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.17705","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138807676","display_name":"Changwei Tu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tu, Changwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138806033","display_name":"Cheng Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113400378","display_name":"Xianzhao Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Xianzhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138768213","display_name":"Kai Niu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu, Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11321","display_name":"Error Correcting Code Techniques","score":0.7124000191688538,"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"}},"topics":[{"id":"https://openalex.org/T11321","display_name":"Error Correcting Code Techniques","score":0.7124000191688538,"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"}},{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.24950000643730164,"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/T10125","display_name":"Advanced Wireless Communication Techniques","score":0.00860000029206276,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6396999955177307},{"id":"https://openalex.org/keywords/polar-code","display_name":"Polar code","score":0.5684000253677368},{"id":"https://openalex.org/keywords/ideal","display_name":"Ideal (ethics)","score":0.5480999946594238},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5274999737739563},{"id":"https://openalex.org/keywords/bhattacharyya-distance","display_name":"Bhattacharyya distance","score":0.5263000130653381},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4871000051498413},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.46869999170303345},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.42879998683929443}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6396999955177307},{"id":"https://openalex.org/C2778396592","wikidata":"https://www.wikidata.org/wiki/Q7209069","display_name":"Polar code","level":3,"score":0.5684000253677368},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5536999702453613},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.5480999946594238},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5274999737739563},{"id":"https://openalex.org/C24145651","wikidata":"https://www.wikidata.org/wiki/Q2901249","display_name":"Bhattacharyya distance","level":2,"score":0.5263000130653381},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4871000051498413},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4869999885559082},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44609999656677246},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42879998683929443},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4251999855041504},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.423799991607666},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3912999927997589},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3646000027656555},{"id":"https://openalex.org/C168773036","wikidata":"https://www.wikidata.org/wiki/Q264164","display_name":"Recursion (computer science)","level":2,"score":0.3571999967098236},{"id":"https://openalex.org/C29705727","wikidata":"https://www.wikidata.org/wiki/Q294562","display_name":"Polar","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.32199999690055847},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.265500009059906},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.17705","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17705","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.17705","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17705","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5952324271202087}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Blind":[0],"recognition":[1,54,81,109,163,202],"of":[2,55,213,218],"polar-coded":[3],"transmissions":[4],"is":[5,21,32,58,174],"an":[6,74,112,183],"important":[7],"task":[8],"in":[9,36,226],"non-cooperative":[10],"wireless":[11],"forensics":[12],"and":[13,42,89,128,148,155,161,189,209,221],"security-oriented":[14],"signal":[15],"analysis.":[16],"When":[17],"the":[18,28,38,65,106,122,137,144,159,178,197,201,205,211,216,227,232,241],"code":[19,214],"length":[20],"known":[22],"or":[23,133],"has":[24],"been":[25],"estimated,":[26],"recovering":[27],"frozen/information":[29],"bit-position":[30],"pattern":[31],"a":[33,61,83,94,236],"key":[34],"step":[35],"identifying":[37],"underlying":[39],"polar-code":[40],"structure":[41],"enabling":[43],"subsequent":[44],"information":[45],"recovery":[46],"from":[47,60],"intercepted":[48,219],"observations.":[49,169],"In":[50],"this":[51,149],"paper,":[52],"blind":[53],"polar":[56,190],"codes":[57],"investigated":[59],"hypothesis-testing":[62],"perspective":[63],"under":[64,73,125,143,204],"successive":[66],"cancellation":[67],"(SC)-based":[68],"synthetic":[69],"bit-channel":[70],"representation.":[71],"First,":[72],"ideal":[75,145,206],"SC-consistent":[76,146,207,233],"condition,":[77],"we":[78,103,152],"formulate":[79],"position-wise":[80,160],"as":[82,121],"binary":[84],"hypothesis":[85],"test":[86],"between":[87,186],"frozen-position":[88],"information-position":[90],"models,":[91],"which":[92],"provides":[93,235],"theoretical":[95],"benchmark":[96],"for":[97],"analyzing":[98],"their":[99],"intrinsic":[100],"distinguishability.":[101],"Second,":[102],"show":[104,195],"that":[105,196,231],"adopted":[107],"soft":[108],"metric":[110],"admits":[111],"exact":[113],"shifted":[114],"log-likelihood-ratio":[115],"interpretation.":[116],"This":[117],"justifies":[118],"ln":[119],"2":[120],"neutral":[123,150],"threshold":[124,140],"equal":[126],"priors":[127,132],"costs,":[129],"while":[130],"unequal":[131],"costs":[134],"lead":[135],"to":[136,177,240],"corresponding":[138],"Bayesian":[139],"shift.":[141],"Third,":[142],"model":[147,208],"setting,":[151],"derive":[153],"upper":[154],"lower":[156],"bounds":[157,199],"on":[158],"sequence-level":[162,238],"error":[164],"probabilities":[165],"with":[166],"multiple":[167],"independent":[168],"The":[170],"resulting":[171],"overlap":[172],"coefficient":[173],"further":[175],"related":[176],"classical":[179],"Bhattacharyya":[180],"parameter,":[181],"establishing":[182],"interpretable":[184],"link":[185],"blind-recognition":[187],"difficulty":[188],"synthetic-channel":[191],"reliability.":[192],"Simulation":[193],"results":[194],"derived":[198],"characterize":[200],"performance":[203],"capture":[210],"effects":[212],"length,":[215],"number":[217],"observations,":[220],"SNR.":[222],"Further":[223],"paired":[224],"comparisons":[225],"tested":[228],"settings":[229],"indicate":[230],"recursion":[234],"good":[237],"match":[239],"realistic":[242],"SC-recursive":[243],"procedure.":[244]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-18T00:00:00"}