{"id":"https://openalex.org/W3013618983","doi":"https://doi.org/10.1109/ieeeconf44664.2019.9048910","title":"Neural Network DPD via Backpropagation through a Neural Network Model of the PA","display_name":"Neural Network DPD via Backpropagation through a Neural Network Model of the PA","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3013618983","doi":"https://doi.org/10.1109/ieeeconf44664.2019.9048910","mag":"3013618983"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf44664.2019.9048910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf44664.2019.9048910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","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/A5021048797","display_name":"Chance Tarver","orcid":"https://orcid.org/0000-0002-4100-7589"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chance Tarver","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010543325","display_name":"Liwen Jiang","orcid":"https://orcid.org/0000-0002-3459-3700"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liwen Jiang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012036055","display_name":"Aryan Sefidi","orcid":null},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aryan Sefidi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085983829","display_name":"Joseph R. Cavallaro","orcid":"https://orcid.org/0000-0002-9841-1806"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph R. Cavallaro","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA","institution_ids":["https://openalex.org/I74775410"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5021048797"],"corresponding_institution_ids":["https://openalex.org/I74775410"],"apc_list":null,"apc_paid":null,"fwci":2.1461,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.88177658,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"358","last_page":"362"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11248","display_name":"Advanced Power Amplifier Design","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/T11248","display_name":"Advanced Power Amplifier Design","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/T10187","display_name":"Radio Frequency Integrated Circuit Design","score":0.9979000091552734,"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/T11873","display_name":"PAPR reduction in OFDM","score":0.9979000091552734,"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/predistortion","display_name":"Predistortion","score":0.9303950071334839},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8390861749649048},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6762750744819641},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.6438871026039124},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.553633987903595},{"id":"https://openalex.org/keywords/amplifier","display_name":"Amplifier","score":0.5462607145309448},{"id":"https://openalex.org/keywords/polynomial","display_name":"Polynomial","score":0.5062360167503357},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5007138252258301},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43344321846961975},{"id":"https://openalex.org/keywords/adjacent-channel-power-ratio","display_name":"Adjacent channel power ratio","score":0.41979196667671204},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3370729088783264},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08638244867324829},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07206308841705322}],"concepts":[{"id":"https://openalex.org/C2778587875","wikidata":"https://www.wikidata.org/wiki/Q7239686","display_name":"Predistortion","level":4,"score":0.9303950071334839},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8390861749649048},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6762750744819641},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.6438871026039124},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.553633987903595},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.5462607145309448},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.5062360167503357},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5007138252258301},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43344321846961975},{"id":"https://openalex.org/C2776191232","wikidata":"https://www.wikidata.org/wiki/Q4683143","display_name":"Adjacent channel power ratio","level":5,"score":0.41979196667671204},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3370729088783264},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08638244867324829},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07206308841705322},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf44664.2019.9048910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf44664.2019.9048910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1604104756","https://openalex.org/W1988115241","https://openalex.org/W2105862752","https://openalex.org/W2112794584","https://openalex.org/W2132090204","https://openalex.org/W2135227058","https://openalex.org/W2139057700","https://openalex.org/W2147914016","https://openalex.org/W2171395034","https://openalex.org/W2927402017","https://openalex.org/W6631190155"],"related_works":["https://openalex.org/W3211209249","https://openalex.org/W4226281858","https://openalex.org/W4389544589","https://openalex.org/W2172252884","https://openalex.org/W2173141353","https://openalex.org/W2766668816","https://openalex.org/W2347312039","https://openalex.org/W1996322867","https://openalex.org/W2971526072","https://openalex.org/W2559032902"],"abstract_inverted_index":{"We":[0,124],"demonstrate":[1,125],"digital":[2],"predistortion":[3],"(DPD)":[4],"using":[5,66,132],"a":[6,77,85,108,115],"novel,":[7],"neural-network":[8],"(NN)":[9],"method":[10,131],"to":[11,62,79],"combat":[12],"the":[13,21,28,69,81,90,99,126,140],"nonlinearities":[14],"in":[15],"power":[16,22,135],"amplifiers":[17],"(PAs),":[18],"which":[19,50,103],"limit":[20],"efficiency":[23],"of":[24,128],"mobile":[25,57],"devices,":[26,58],"increase":[27],"error":[29],"vector":[30],"magnitude,":[31],"and":[32,59,111,121,138],"cause":[33],"inadequate":[34],"spectral":[35],"containment.":[36],"DPD":[37,96,122],"is":[38],"commonly":[39],"done":[40],"with":[41,72],"polynomial-based":[42,109],"methods":[43],"that":[44],"use":[45],"an":[46],"indirect-learning":[47],"architecture":[48],"(ILA)":[49],"can":[51],"be":[52],"computationally":[53],"intensive,":[54],"especially":[55],"for":[56],"overly":[60],"sensitive":[61],"noise.":[63],"Our":[64],"approach":[65],"NNs":[67],"avoids":[68],"problems":[70],"associated":[71],"ILAs":[73],"by":[74,87],"first":[75],"training":[76,84],"NN":[78,92,95,130],"model":[80],"PA":[82,91,101],"then":[83],"predistorter":[86],"backpropagating":[88],"through":[89],"model.":[93],"The":[94],"effectively":[97],"learns":[98],"unique":[100],"distortions,":[102],"may":[104,113],"not":[105],"easily":[106],"fit":[107],"model,":[110],"hence":[112],"offer":[114],"favorable":[116],"tradeoff":[117],"between":[118],"computation":[119],"overhead":[120],"performance.":[123],"performance":[127],"our":[129],"two":[133],"different":[134],"amplifier":[136],"systems":[137],"investigate":[139],"complexity":[141],"tradeoffs.":[142]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-02-07T06:11:34.122080","created_date":"2025-10-10T00:00:00"}
