{"id":"https://openalex.org/W7165008305","doi":"https://doi.org/10.48550/arxiv.2606.17893","title":"Condition-Wise Sinkhorn Drifting for One-Shot Learned Channel Simulation","display_name":"Condition-Wise Sinkhorn Drifting for One-Shot Learned Channel Simulation","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7165008305","doi":"https://doi.org/10.48550/arxiv.2606.17893"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.17893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17893","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.2606.17893","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062815664","display_name":"Rick Fritschek","orcid":"https://orcid.org/0000-0002-2485-5500"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fritschek, Rick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5038203207","display_name":"Rafael F. Schaefer","orcid":"https://orcid.org/0000-0002-1702-9075"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schaefer, Rafael F.","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/T10125","display_name":"Advanced Wireless Communication Techniques","score":0.12849999964237213,"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/T10125","display_name":"Advanced Wireless Communication Techniques","score":0.12849999964237213,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.12460000067949295,"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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.10809999704360962,"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/channel","display_name":"Channel (broadcasting)","score":0.5507000088691711},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.489300012588501},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.477400004863739},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4523000121116638},{"id":"https://openalex.org/keywords/symbol","display_name":"Symbol (formal)","score":0.44769999384880066},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.40369999408721924},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3968000113964081},{"id":"https://openalex.org/keywords/conditional-expectation","display_name":"Conditional expectation","score":0.3359000086784363},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3353999853134155},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.335099995136261}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6733999848365784},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5507000088691711},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.489300012588501},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.477400004863739},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4523000121116638},{"id":"https://openalex.org/C134400042","wikidata":"https://www.wikidata.org/wiki/Q2372244","display_name":"Symbol (formal)","level":2,"score":0.44769999384880066},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.40369999408721924},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3968000113964081},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36480000615119934},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.335099995136261},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3314000070095062},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.3138999938964844},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C21430997","wikidata":"https://www.wikidata.org/wiki/Q5159279","display_name":"Conditional variance","level":4,"score":0.3125},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.30709999799728394},{"id":"https://openalex.org/C51955184","wikidata":"https://www.wikidata.org/wiki/Q1545585","display_name":"Stochastic differential equation","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2754000127315521},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2685999870300293},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.2637999951839447},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C21031990","wikidata":"https://www.wikidata.org/wiki/Q355020","display_name":"Probability measure","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.17893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17893","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.2606.17893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17893","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":{"Learned":[0],"communication":[1],"systems":[2],"may":[3],"evaluate":[4],"stochastic":[5],"channel":[6,28,90,158],"surrogates":[7],"millions":[8],"of":[9],"times":[10],"inside":[11],"differentiable":[12],"training":[13],"loops,":[14],"making":[15],"diffusion-style":[16,161],"reverse":[17],"sampling":[18,162],"expensive.":[19],"This":[20],"paper":[21],"proposes":[22],"condition-wise":[23,97,124],"Sinkhorn":[24,48,95,98,125],"drifting,":[25,93,96,99],"a":[26,46,86,150],"one-shot":[27,121,152],"surrogate":[29],"that":[30],"preserves":[31],"the":[32,38,54,60,119,139],"transmitted":[33,56],"symbol":[34,57],"and":[35,58,85,111,131],"transports":[36],"only":[37],"conditional":[39,47,100,129],"output":[40],"laws":[41],"\\(p(y\\mid":[42],"x)\\).":[43],"We":[44],"formulate":[45],"objective":[49],"over":[50],"repeated":[51,157],"outputs":[52],"at":[53],"same":[55],"train":[59],"generator":[61],"with":[62],"finite-sample":[63],"barycentric":[64],"velocities":[65],"followed":[66],"by":[67],"detached":[68],"particle":[69],"regression.":[70],"Experiments":[71],"on":[72,138],"additive":[73],"white":[74],"Gaussian":[75],"noise":[76],"(AWGN),":[77],"Rayleigh":[78],"fading,":[79],"solid-state":[80],"power":[81],"amplifier":[82],"(SSPA)":[83],"nonlinearity,":[84],"compact":[87],"tapped-delay-line":[88],"(TDL)":[89],"compare":[91],"direct":[92],"joint":[94],"denoising":[101,106],"diffusion":[102,107,135],"probabilistic":[103],"modeling":[104,109],"(DDPM),":[105],"implicit":[108],"(DDIM),":[110],"Wasserstein":[112],"generative":[113],"adversarial":[114],"network":[115],"(WGAN)":[116],"references.":[117],"Within":[118],"evaluated":[120],"drifting-family":[122],"variants,":[123],"is":[126,149],"strongest":[127,137],"under":[128],"diagnostics":[130],"symbolic-coding":[132],"checks,":[133],"while":[134],"remains":[136],"hardest":[140],"downstream":[141],"symbol-error-rate":[142],"(SER)":[143],"curves.":[144],"The":[145],"resulting":[146],"operating":[147],"point":[148],"condition-preserving":[151],"simulator":[153],"for":[154],"settings":[155],"where":[156],"calls":[159],"make":[160],"too":[163],"costly.":[164]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-18T00:00:00"}