{"id":"https://openalex.org/W2139011151","doi":"https://doi.org/10.1109/wcnc.2004.1311508","title":"SNR estimation in decibel domain over communication channels","display_name":"SNR estimation in decibel domain over communication channels","publication_year":2004,"publication_date":"2004-10-19","ids":{"openalex":"https://openalex.org/W2139011151","doi":"https://doi.org/10.1109/wcnc.2004.1311508","mag":"2139011151"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc.2004.1311508","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc.2004.1311508","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733)","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/A5101651851","display_name":"Youngchai Ko","orcid":"https://orcid.org/0000-0001-5707-0032"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Young-Chai Ko","raw_affiliation_strings":["Department of Electronics and Computer Engineering, Korea University, Seoul, South Korea","[Dept. of Electron. & Comput. Eng., Korea Univ., Seoul, South Korea]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Computer Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"[Dept. of Electron. & Comput. Eng., Korea Univ., Seoul, South Korea]","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002713029","display_name":"Tao Luo","orcid":"https://orcid.org/0000-0003-4870-5942"},"institutions":[{"id":"https://openalex.org/I74760111","display_name":"Texas Instruments (United States)","ror":"https://ror.org/03vsmv677","country_code":"US","type":"company","lineage":["https://openalex.org/I74760111"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tao Luo","raw_affiliation_strings":["Wireless Center, Texas Instruments, Inc., San Diego, CA, USA","[Wireless Center, Texas Instruments, Inc., San Diego, CA, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wireless Center, Texas Instruments, Inc., San Diego, CA, USA","institution_ids":["https://openalex.org/I74760111"]},{"raw_affiliation_string":"[Wireless Center, Texas Instruments, Inc., San Diego, CA, USA]","institution_ids":["https://openalex.org/I74760111"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2495,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67489043,"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/T10575","display_name":"Wireless Communication Networks Research","score":0.9994999766349792,"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/T10575","display_name":"Wireless Communication Networks Research","score":0.9994999766349792,"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/T10125","display_name":"Advanced Wireless Communication Techniques","score":0.998199999332428,"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/T10860","display_name":"Speech and Audio Processing","score":0.9940999746322632,"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/estimator","display_name":"Estimator","score":0.8137055039405823},{"id":"https://openalex.org/keywords/fading","display_name":"Fading","score":0.6578265428543091},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5781041979789734},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.5680685639381409},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.560693621635437},{"id":"https://openalex.org/keywords/multipath-propagation","display_name":"Multipath propagation","score":0.5025451183319092},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.49562036991119385},{"id":"https://openalex.org/keywords/rayleigh-fading","display_name":"Rayleigh fading","score":0.480338454246521},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4609132409095764},{"id":"https://openalex.org/keywords/bias-of-an-estimator","display_name":"Bias of an estimator","score":0.46020787954330444},{"id":"https://openalex.org/keywords/nakagami-distribution","display_name":"Nakagami distribution","score":0.43368327617645264},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.4301169514656067},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.412285178899765},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3777289390563965},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.08682394027709961}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.8137055039405823},{"id":"https://openalex.org/C81978471","wikidata":"https://www.wikidata.org/wiki/Q1196572","display_name":"Fading","level":3,"score":0.6578265428543091},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5781041979789734},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.5680685639381409},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.560693621635437},{"id":"https://openalex.org/C161218011","wikidata":"https://www.wikidata.org/wiki/Q11827794","display_name":"Multipath propagation","level":3,"score":0.5025451183319092},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.49562036991119385},{"id":"https://openalex.org/C56985126","wikidata":"https://www.wikidata.org/wiki/Q854039","display_name":"Rayleigh fading","level":4,"score":0.480338454246521},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4609132409095764},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.46020787954330444},{"id":"https://openalex.org/C115098869","wikidata":"https://www.wikidata.org/wiki/Q3258347","display_name":"Nakagami distribution","level":4,"score":0.43368327617645264},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.4301169514656067},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.412285178899765},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3777289390563965},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.08682394027709961},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcnc.2004.1311508","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc.2004.1311508","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733)","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":12,"referenced_works":["https://openalex.org/W598996319","https://openalex.org/W1522045078","https://openalex.org/W2113685396","https://openalex.org/W2131499054","https://openalex.org/W2145614561","https://openalex.org/W2146185352","https://openalex.org/W2157005274","https://openalex.org/W2168307902","https://openalex.org/W2543235636","https://openalex.org/W2913817822","https://openalex.org/W3119264854","https://openalex.org/W6728991526"],"related_works":["https://openalex.org/W2048738685","https://openalex.org/W2349547417","https://openalex.org/W4237435333","https://openalex.org/W4210503132","https://openalex.org/W2999390738","https://openalex.org/W2352602506","https://openalex.org/W3092888124","https://openalex.org/W2093865141","https://openalex.org/W4239491110","https://openalex.org/W2368191880"],"abstract_inverted_index":{"The":[0,25],"statistics":[1],"of":[2,28,56,66,73],"the":[3,8,29,33,40,54,57,64,67,85,94,102,106,113,124,127,132,140,148,151,157],"signal":[4,9,77,163],"estimator":[5,31,69,129,137,153],"for":[6,101,112,156],"estimating":[7],"value":[10],"in":[11,16],"decibel":[12],"(dB)":[13],"domain":[14],"proposed":[15,30,58,86,128,152],"T.":[17],"Luo":[18],"and":[19,36,161],"Y.C.":[20],"Ko":[21],"(2004),":[22],"is":[23,60,99,116],"analyzed.":[24],"basic":[26],"idea":[27],"combines":[32],"linear":[34],"averaging":[35,44,49],"log":[37],"domain.":[38],"Using":[39],"most":[41],"frequently":[42],"used":[43,111],"filter,":[45,50],"i.e.,":[46],"sample":[47],"mean":[48],"we":[51,121],"prove":[52],"that":[53,126,150],"performance":[55],"method":[59,87],"very":[61],"close":[62],"to":[63,91,109],"one":[65],"optimum":[68,103,133],"with":[70],"an":[71],"assumption":[72],"independent":[74],"identically":[75],"distributed":[76],"samples":[78,108],"under":[79,139],"Nakagami":[80,95],"fading":[81,142,159],"channel":[82],"model.":[83],"Moreover,":[84],"does":[88],"not":[89],"need":[90],"explicitly":[92],"estimate":[93],"parameter,":[96],"m,":[97],"which":[98,115],"required":[100],"estimator.":[104],"For":[105],"correlated":[107],"be":[110],"estimation,":[114],"a":[117],"more":[118],"practical":[119],"situation,":[120],"show":[122,146],"from":[123,147],"simulation":[125],"outperforms":[130],"significantly":[131],"minimum":[134],"variance":[135],"unbiased":[136],"obtained":[138],"Rayleigh":[141],"channels.":[143],"We":[144],"also":[145],"simulations":[149],"works":[154],"well":[155],"multipath":[158],"case":[160],"low":[162],"strength.":[164]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
