{"id":"https://openalex.org/W2936592677","doi":"https://doi.org/10.1109/vtcfall.2018.8690777","title":"Randomization Algorithm for Partial Transmit Sequence with Semidefinite Relaxation","display_name":"Randomization Algorithm for Partial Transmit Sequence with Semidefinite Relaxation","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2936592677","doi":"https://doi.org/10.1109/vtcfall.2018.8690777","mag":"2936592677"},"language":"en","primary_location":{"id":"doi:10.1109/vtcfall.2018.8690777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2018.8690777","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)","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/A5052981892","display_name":"Hirofumi Tsuda","orcid":"https://orcid.org/0000-0002-3527-9211"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hirofumi Tsuda","raw_affiliation_strings":["Department of Applied Mathematics and Physics, Kyoto University, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Physics, Kyoto University, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001619132","display_name":"Ken Umeno","orcid":"https://orcid.org/0000-0002-9162-1261"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ken Umeno","raw_affiliation_strings":["Department of Applied Mathematics and Physics, Kyoto University, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Physics, Kyoto University, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5052981892"],"corresponding_institution_ids":["https://openalex.org/I22299242"],"apc_list":null,"apc_paid":null,"fwci":0.1288,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52532736,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"32","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11873","display_name":"PAPR reduction in OFDM","score":1.0,"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/T11873","display_name":"PAPR reduction in OFDM","score":1.0,"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/T10575","display_name":"Wireless Communication Networks Research","score":0.9994000196456909,"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":0.9980000257492065,"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/covariance-matrix","display_name":"Covariance matrix","score":0.690336287021637},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.669919490814209},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6624926328659058},{"id":"https://openalex.org/keywords/relaxation","display_name":"Relaxation (psychology)","score":0.6163566708564758},{"id":"https://openalex.org/keywords/multivariate-random-variable","display_name":"Multivariate random variable","score":0.6160291433334351},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.49859166145324707},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4858205318450928},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4832945168018341},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4394969046115875},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.43617475032806396},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.29830431938171387},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19446128606796265}],"concepts":[{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.690336287021637},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.669919490814209},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6624926328659058},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.6163566708564758},{"id":"https://openalex.org/C138405894","wikidata":"https://www.wikidata.org/wiki/Q3179949","display_name":"Multivariate random variable","level":3,"score":0.6160291433334351},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.49859166145324707},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4858205318450928},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4832945168018341},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4394969046115875},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.43617475032806396},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.29830431938171387},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19446128606796265},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtcfall.2018.8690777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2018.8690777","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W582041039","https://openalex.org/W1168406117","https://openalex.org/W1569301884","https://openalex.org/W1964793896","https://openalex.org/W1996215314","https://openalex.org/W2014703233","https://openalex.org/W2063011342","https://openalex.org/W2063046368","https://openalex.org/W2068626386","https://openalex.org/W2069944453","https://openalex.org/W2099486881","https://openalex.org/W2100670409","https://openalex.org/W2102483328","https://openalex.org/W2104975786","https://openalex.org/W2106550864","https://openalex.org/W2110411562","https://openalex.org/W2111937770","https://openalex.org/W2112517678","https://openalex.org/W2114518213","https://openalex.org/W2120773774","https://openalex.org/W2129804005","https://openalex.org/W2131758787","https://openalex.org/W2140546582","https://openalex.org/W2142402521","https://openalex.org/W2145080587","https://openalex.org/W2147741675","https://openalex.org/W2153625126","https://openalex.org/W2161110001","https://openalex.org/W2163172977","https://openalex.org/W2167849523","https://openalex.org/W2171878483","https://openalex.org/W2296319761","https://openalex.org/W4250589301","https://openalex.org/W4253609148","https://openalex.org/W6627659712"],"related_works":["https://openalex.org/W2886934452","https://openalex.org/W1489099099","https://openalex.org/W1976411104","https://openalex.org/W2768630603","https://openalex.org/W2024369332","https://openalex.org/W2041949274","https://openalex.org/W2167608933","https://openalex.org/W2718384077","https://openalex.org/W2962891288","https://openalex.org/W4247660897"],"abstract_inverted_index":{"To":[0],"reduce":[1],"peak-to-average":[2,65],"power":[3,66],"ratio":[4,67],"is":[5,34,46,68],"a":[6,19,23,27,47,50,56,73],"significant":[7],"task":[8],"for":[9,26],"Orthogonal":[10],"Frequency":[11],"Multicarrier":[12],"Systems.":[13],"In":[14],"this":[15],"paper,":[16],"we":[17],"propose":[18],"method":[20,33,75],"to":[21,35,54],"choose":[22,55],"suitable":[24],"vector":[25,57],"partial":[28],"transmit":[29],"sequence":[30],"technique.":[31],"Our":[32],"generate":[36],"random":[37,60,81],"vectors":[38],"from":[39,58],"the":[40,59,77],"Gaussian":[41],"distribution":[42],"whose":[43],"covariance":[44],"matrix":[45],"solution":[48],"of":[49,80],"relaxed":[51],"problem":[52],"and":[53],"vectors.":[61,82],"With":[62],"our":[63],"method,":[64],"lower":[69],"than":[70],"one":[71],"with":[72],"conventional":[74],"in":[76],"same":[78],"number":[79]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
