{"id":"https://openalex.org/W4408324802","doi":"https://doi.org/10.1109/globecom52923.2024.10901317","title":"Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning","display_name":"Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning","publication_year":2024,"publication_date":"2024-12-08","ids":{"openalex":"https://openalex.org/W4408324802","doi":"https://doi.org/10.1109/globecom52923.2024.10901317"},"language":"en","primary_location":{"id":"doi:10.1109/globecom52923.2024.10901317","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom52923.2024.10901317","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2024 - 2024 IEEE Global Communications Conference","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":"https://openalex.org/A5033844485","display_name":"Momin Abbas","orcid":null},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Momin Abbas","raw_affiliation_strings":["Rensselaer Polytechnic Institute,Department of Electrical, Computer and Systems Engineering,Troy,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute,Department of Electrical, Computer and Systems Engineering,Troy,NY,USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090686321","display_name":"Koushik Kar","orcid":"https://orcid.org/0000-0003-2506-672X"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Koushik Kar","raw_affiliation_strings":["Rensselaer Polytechnic Institute,Department of Electrical, Computer and Systems Engineering,Troy,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute,Department of Electrical, Computer and Systems Engineering,Troy,NY,USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100783476","display_name":"Tianyi Chen","orcid":"https://orcid.org/0000-0003-3477-1439"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyi Chen","raw_affiliation_strings":["Rensselaer Polytechnic Institute,Department of Electrical, Computer and Systems Engineering,Troy,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute,Department of Electrical, Computer and Systems Engineering,Troy,NY,USA","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":null,"apc_paid":null,"fwci":1.4929,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.86126054,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5217","last_page":"5222"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9588000178337097,"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":0.9588000178337097,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9352999925613403,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9117000102996826,"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/computer-science","display_name":"Computer science","score":0.8024872541427612},{"id":"https://openalex.org/keywords/symbol","display_name":"Symbol (formal)","score":0.6236934065818787},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5837633013725281},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.5442259311676025},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.5309543013572693},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3950929343700409},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.36763471364974976},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.1671011745929718},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.14434802532196045}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8024872541427612},{"id":"https://openalex.org/C134400042","wikidata":"https://www.wikidata.org/wiki/Q2372244","display_name":"Symbol (formal)","level":2,"score":0.6236934065818787},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5837633013725281},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.5442259311676025},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.5309543013572693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3950929343700409},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36763471364974976},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.1671011745929718},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.14434802532196045},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom52923.2024.10901317","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom52923.2024.10901317","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2024 - 2024 IEEE Global Communications Conference","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":34,"referenced_works":["https://openalex.org/W1667658377","https://openalex.org/W1995875735","https://openalex.org/W2012942264","https://openalex.org/W2106440681","https://openalex.org/W2254249950","https://openalex.org/W2592328156","https://openalex.org/W2885033069","https://openalex.org/W2986517689","https://openalex.org/W3000769608","https://openalex.org/W3014018904","https://openalex.org/W3015903302","https://openalex.org/W3022312941","https://openalex.org/W3048657120","https://openalex.org/W3089389858","https://openalex.org/W3097372264","https://openalex.org/W3110675837","https://openalex.org/W4282962298","https://openalex.org/W4312926990","https://openalex.org/W4318040964","https://openalex.org/W4361012985","https://openalex.org/W4375869019","https://openalex.org/W4385567149","https://openalex.org/W4387870681","https://openalex.org/W6636501900","https://openalex.org/W6739651123","https://openalex.org/W6762122294","https://openalex.org/W6778883912","https://openalex.org/W6779629370","https://openalex.org/W6791446462","https://openalex.org/W6810879928","https://openalex.org/W6811340617","https://openalex.org/W6838540236","https://openalex.org/W6854866820","https://openalex.org/W6861521715"],"related_works":["https://openalex.org/W2349222429","https://openalex.org/W2116230991","https://openalex.org/W3117430770","https://openalex.org/W2046058552","https://openalex.org/W2590751808","https://openalex.org/W1972377868","https://openalex.org/W2132709506","https://openalex.org/W2098730537","https://openalex.org/W2186895195","https://openalex.org/W2748557612"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"(DNNs)":[3],"have":[4,50],"made":[5],"significant":[6],"strides":[7],"in":[8,12,73,96],"tackling":[9],"challenging":[10,70],"tasks":[11,72,95],"wireless":[13,19,74,94],"systems,":[14],"especially":[15],"when":[16,25,121,161],"an":[17],"accurate":[18],"model":[20],"is":[21,28,76],"not":[22],"available.":[23],"However,":[24],"available":[26],"data":[27,99],"limited,":[29],"traditional":[30,149],"DNNs":[31,107,150],"often":[32],"yield":[33],"subpar":[34],"results":[35,139],"due":[36],"to":[37,82,92],"underfitting.":[38],"At":[39],"the":[40,84,97,115,133,152],"same":[41],"time,":[42],"large":[43],"language":[44,61],"models":[45],"(LLMs)":[46],"exemplified":[47],"by":[48],"GPT-3,":[49],"remarkably":[51],"showcased":[52],"their":[53],"capabilities":[54],"across":[55],"a":[56],"broad":[57],"range":[58],"of":[59,90,117],"natural":[60],"processing":[62],"tasks.But":[63],"whether":[64],"and":[65,156],"how":[66],"LLMs":[67,91,118,143],"can":[68],"benefit":[69],"non-language":[71],"systems":[75],"unexplored.In":[77],"this":[78,129],"work,":[79],"we":[80,131],"propose":[81],"leverage":[83],"in-context":[85],"learning":[86],"ability":[87],"(a.k.a.":[88],"prompting)":[89],"solve":[93,128],"low":[98],"regime":[100],"without":[101],"any":[102],"training":[103],"or":[104],"fine-tuning,":[105],"unlike":[106],"which":[108],"require":[109],"training.":[110],"We":[111],"further":[112],"demonstrate":[113],"that":[114,141],"performance":[116],"varies":[119],"significantly":[120],"employed":[122],"with":[123,163],"different":[124],"prompt":[125],"templates.":[126],"To":[127],"issue,":[130],"employ":[132],"latest":[134],"LLM":[135],"calibration":[136,164],"methods.":[137],"Our":[138],"reveal":[140],"using":[142],"via":[144],"ICL":[145],"methods":[146],"generally":[147],"outperforms":[148],"on":[151],"symbol":[153],"demodulation":[154],"task":[155],"yields":[157],"highly":[158],"confident":[159],"predictions":[160],"coupled":[162],"techniques.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}