{"id":"https://openalex.org/W7162759671","doi":"https://doi.org/10.48550/arxiv.2605.29995","title":"Low-Overhead Receiver Design for Data-Dependent Superimposed Training via Deep Learning","display_name":"Low-Overhead Receiver Design for Data-Dependent Superimposed Training via Deep Learning","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162759671","doi":"https://doi.org/10.48550/arxiv.2605.29995"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29995","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29995","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.2605.29995","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137336761","display_name":"Xinjie Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xinjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137311613","display_name":"Xingyu Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Xingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137339727","display_name":"Jing Zhang","orcid":"https://orcid.org/0009-0009-8762-2796"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137329984","display_name":"Chao-Kai Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Chao-Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137357102","display_name":"Xiao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137381989","display_name":"Shi Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Shi","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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.3928999900817871,"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":0.3928999900817871,"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.13680000603199005,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.11420000344514847,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/bottleneck","display_name":"Bottleneck","score":0.6643000245094299},{"id":"https://openalex.org/keywords/transmission","display_name":"Transmission (telecommunications)","score":0.555899977684021},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5529000163078308},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.53329998254776},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.5332000255584717},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4855000078678131},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4740000069141388},{"id":"https://openalex.org/keywords/spectral-efficiency","display_name":"Spectral efficiency","score":0.4318000078201294}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6966999769210815},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6643000245094299},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.555899977684021},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5529000163078308},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.53329998254776},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.5332000255584717},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4855000078678131},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4740000069141388},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45350000262260437},{"id":"https://openalex.org/C137246740","wikidata":"https://www.wikidata.org/wiki/Q583970","display_name":"Spectral efficiency","level":3,"score":0.4318000078201294},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.42170000076293945},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4036000072956085},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3732999861240387},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3409000039100647},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C557945733","wikidata":"https://www.wikidata.org/wiki/Q389772","display_name":"Data transmission","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.27469998598098755},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.27070000767707825},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2703000009059906}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29995","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29995","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.2605.29995","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29995","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Superimposed":[0],"pilot":[1,11,16],"(SIP)":[2],"transmission":[3,45,99,119],"improves":[4],"spectral":[5],"efficiency":[6,199],"by":[7,74],"eliminating":[8],"the":[9,34,82,113,117,124,138,151,162,168,180,188],"dedicated":[10],"overhead":[12],"required":[13,172],"in":[14,93,187],"orthogonal":[15,152],"(OP)-based":[17],"schemes.":[18],"However,":[19],"SIP":[20,203],"suffers":[21],"from":[22],"severe":[23],"pilot-data":[24,72],"coupling,":[25],"which":[26],"leads":[27],"to":[28,69,80,87,107,149],"a":[29,43,97,108,142],"critical":[30],"performance-complexity":[31],"bottleneck":[32],"at":[33],"receiver.":[35],"To":[36],"address":[37],"this":[38,40],"issue,":[39],"paper":[41],"proposes":[42],"low-overhead":[44],"framework":[46,182],"that":[47,179],"revitalizes":[48],"data-dependent":[49,76],"superimposed":[50],"training":[51],"(DDST)":[52],"with":[53,123,201],"enhanced":[54,64],"interference":[55,134,174],"mitigation":[56],"strategies.":[57],"First,":[58],"for":[59,173],"quasi-static":[60,170],"block-fading":[61],"channels,":[62],"an":[63],"DDST":[65,86,106],"receiver":[66,146],"is":[67,101,147],"developed":[68],"achieve":[70],"non-iterative":[71],"decoupling":[73],"exploiting":[75],"algebraic":[77],"structures.":[78],"Second,":[79],"overcome":[81],"sensitivity":[83],"of":[84,110,121,127],"conventional":[85],"channel":[88,164],"variations":[89],"and":[90,133,156],"symbol":[91],"misidentification":[92],"fast":[94],"time-varying":[95,193],"environments,":[96],"mix":[98,140],"scheme":[100,115],"developed.":[102],"By":[103],"strategically":[104],"applying":[105],"subset":[109],"resource":[111],"elements,":[112],"proposed":[114,139,181],"combines":[116],"interference-free":[118],"property":[120],"OP":[122],"zero-pilot-overhead":[125],"advantage":[126],"SIP,":[128],"thereby":[129,166],"improving":[130],"demapping":[131],"reliability":[132],"suppression.":[135],"Furthermore,":[136],"under":[137,192],"scheme,":[141],"Vision":[143],"Transformer-based":[144],"neural":[145],"designed":[148],"capture":[150],"structure":[153],"between":[154],"pilots":[155],"perturbation-bearing":[157],"data,":[158],"as":[159,161],"well":[160],"underlying":[163],"correlations,":[165],"relaxing":[167],"stringent":[169],"assumption":[171],"disentanglement.":[175],"Simulation":[176],"results":[177],"demonstrate":[178],"achieves":[183],"significant":[184],"performance":[185],"gains":[186],"low-to-medium":[189],"SNR":[190],"regime":[191],"channels":[194],"while":[195],"providing":[196],"superior":[197],"computational":[198],"compared":[200],"state-of-the-art":[202],"receivers.":[204]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}
