{"id":"https://openalex.org/W4415367104","doi":"https://doi.org/10.1109/isit63088.2025.11195222","title":"In-Context Learning Based Efficient Spectrum Sensing","display_name":"In-Context Learning Based Efficient Spectrum Sensing","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4415367104","doi":"https://doi.org/10.1109/isit63088.2025.11195222"},"language":null,"primary_location":{"id":"doi:10.1109/isit63088.2025.11195222","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-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":null,"display_name":"Renpu Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Renpu Liu","raw_affiliation_strings":["University of Virginia"],"affiliations":[{"raw_affiliation_string":"University of Virginia","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087119041","display_name":"Liwen Zhong","orcid":"https://orcid.org/0000-0002-6107-5925"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liwen Zhong","raw_affiliation_strings":["Pennsylvania State University"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007594288","display_name":"Wooram Lee","orcid":"https://orcid.org/0000-0002-9963-549X"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wooram Lee","raw_affiliation_strings":["Pennsylvania State University"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051840260","display_name":"Jing Yang","orcid":"https://orcid.org/0000-0002-4677-797X"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Yang","raw_affiliation_strings":["University of Virginia"],"affiliations":[{"raw_affiliation_string":"University of Virginia","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35706602,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9235000014305115,"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":0.9235000014305115,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5101000070571899},{"id":"https://openalex.org/keywords/spectral-efficiency","display_name":"Spectral efficiency","score":0.4839000105857849},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.47099998593330383},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.4496999979019165},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.41280001401901245},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4106999933719635},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.3944000005722046},{"id":"https://openalex.org/keywords/fast-fourier-transform","display_name":"Fast Fourier transform","score":0.38589999079704285},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.36250001192092896},{"id":"https://openalex.org/keywords/detection-theory","display_name":"Detection theory","score":0.36070001125335693}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6316999793052673},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.5929999947547913},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5101000070571899},{"id":"https://openalex.org/C137246740","wikidata":"https://www.wikidata.org/wiki/Q583970","display_name":"Spectral efficiency","level":3,"score":0.4839000105857849},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.47099998593330383},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.41280001401901245},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4106999933719635},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3944000005722046},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.38589999079704285},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.36070001125335693},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.351500004529953},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.34940001368522644},{"id":"https://openalex.org/C161611012","wikidata":"https://www.wikidata.org/wiki/Q106370","display_name":"Digital signal processor","level":3,"score":0.33889999985694885},{"id":"https://openalex.org/C50151734","wikidata":"https://www.wikidata.org/wiki/Q1759577","display_name":"Matched filter","level":3,"score":0.3287999927997589},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3237999975681305},{"id":"https://openalex.org/C113407356","wikidata":"https://www.wikidata.org/wiki/Q1431214","display_name":"Spectral leakage","level":3,"score":0.3176000118255615},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C13412647","wikidata":"https://www.wikidata.org/wiki/Q174948","display_name":"Analog signal","level":3,"score":0.3009999990463257},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.27000001072883606},{"id":"https://openalex.org/C40409654","wikidata":"https://www.wikidata.org/wiki/Q375889","display_name":"Orthogonal frequency-division multiplexing","level":3,"score":0.26910001039505005},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C36390408","wikidata":"https://www.wikidata.org/wiki/Q1163067","display_name":"Digital filter","level":3,"score":0.2612999975681305},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.26100000739097595},{"id":"https://openalex.org/C105344744","wikidata":"https://www.wikidata.org/wiki/Q958957","display_name":"Spread spectrum","level":3,"score":0.260699987411499},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.2603999972343445},{"id":"https://openalex.org/C2778422915","wikidata":"https://www.wikidata.org/wiki/Q10302051","display_name":"Converters","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.25589999556541443},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.2554999887943268},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit63088.2025.11195222","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2025797389","https://openalex.org/W2055064119","https://openalex.org/W2100556411","https://openalex.org/W2145096794","https://openalex.org/W4250955649","https://openalex.org/W4352977306","https://openalex.org/W4385245566"],"related_works":[],"abstract_inverted_index":{"The":[0],"radio":[1],"frequency":[2],"(RF)":[3],"spectrum":[4,24,28,89,106,127,177,194,205],"is":[5,11],"essential":[6],"for":[7,125,135,165],"wireless":[8],"communication":[9],"but":[10,30],"becoming":[12],"increasingly":[13],"limited":[14],"due":[15],"to":[16,173,215],"the":[17,37,46,87,105,110,141,145,152,162,175,188,191,203,209],"rapid":[18],"growth":[19],"in":[20,93,223],"device":[21],"usage.":[22],"Real-time":[23],"sensing":[25,107,117,136,154,178,195,206],"facilitates":[26],"dynamic":[27],"sharing,":[29],"conventional":[31],"methods":[32],"face":[33],"significant":[34,221],"challenges,":[35],"including":[36],"high":[38],"power":[39],"consumption":[40],"of":[41,49,109,144,161,190],"analog-to-digital":[42],"converters":[43],"(ADCs)":[44],"and":[45,79,207],"computational":[47],"demands":[48],"Fast":[50],"Fourier":[51],"Transforms":[52],"(FFTs).":[53],"To":[54,186],"address":[55],"these":[56],"limitations,":[57],"prior":[58],"work":[59],"introduced":[60],"a":[61,69,80,116,122,132,166],"frequencydomain":[62],"analog":[63,111,146],"signal":[64,84,112,147],"processor.":[65,148],"This":[66],"processor":[67,113],"includes":[68],"digitally":[70],"tunable":[71],"narrow-bandpass":[72],"filter":[73],"implemented":[74],"with":[75,121,168],"programmable":[76],"dispersion-engineered":[77],"elements":[78],"scalable":[81],"pathsharing":[82],"delayed":[83],"combiner.":[85],"However,":[86],"naive":[88],"sweeping":[90],"method":[91,120,219],"employed":[92],"this":[94,101],"design":[95,143],"remains":[96],"highly":[97],"timeand":[98],"energy-intensive.":[99],"In":[100],"work,":[102],"we":[103,130,197],"improve":[104],"efficiency":[108,189],"by":[114],"co-designing":[115],"matrix":[118,137],"generation":[119,138],"decoder-based":[123],"transformer":[124,167],"in-context":[126,183,192,204],"recovery.":[128],"Specifically,":[129],"introduce":[131],"novel":[133],"algorithm":[134],"that":[139,151],"leverages":[140],"hardware":[142],"We":[149],"show":[150],"generated":[153],"matrices":[155,172],"can":[156],"be":[157],"interpreted":[158],"as":[159],"part":[160],"well-designed":[163],"prompts":[164],"specifically":[169],"designed":[170],"parameter":[171],"solve":[174],"sparse":[176],"problem":[179],"efficiently":[180],"through":[181,211],"its":[182],"learning":[184],"capability.":[185],"characterize":[187],"learningenabled":[193],"approach,":[196],"provide":[198],"rigorous":[199],"theoretical":[200],"guarantees":[201],"on":[202],"evaluate":[208],"performances":[210],"empirical":[212],"results.":[213],"Compared":[214],"baseline":[216],"approaches,":[217],"our":[218],"achieves":[220],"improvements":[222],"accuracy.":[224]},"counts_by_year":[],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-21T00:00:00"}
