{"title":"间接学习和闭环估计器在功率放大器数字预失真中的比较","authors":"R. Braithwaite","doi":"10.1109/MWSYM.2015.7166826","DOIUrl":null,"url":null,"abstract":"Indirect learning is often used as an estimator in digital predistortion of power amplifiers (PAs). The estimator has inherent flaws that become apparent when signal bandwidths increase. These include coefficient offsets, excessive ADC sampling requirements, and susceptibility to EVM and PA saturation. A comparison to the closed loop estimator shows that these flaws are specific to the indirect learning estimator.","PeriodicalId":6493,"journal":{"name":"2015 IEEE MTT-S International Microwave Symposium","volume":"460 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"43","resultStr":"{\"title\":\"A comparison of indirect learning and closed loop estimators used in digital predistortion of power amplifiers\",\"authors\":\"R. Braithwaite\",\"doi\":\"10.1109/MWSYM.2015.7166826\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Indirect learning is often used as an estimator in digital predistortion of power amplifiers (PAs). The estimator has inherent flaws that become apparent when signal bandwidths increase. These include coefficient offsets, excessive ADC sampling requirements, and susceptibility to EVM and PA saturation. A comparison to the closed loop estimator shows that these flaws are specific to the indirect learning estimator.\",\"PeriodicalId\":6493,\"journal\":{\"name\":\"2015 IEEE MTT-S International Microwave Symposium\",\"volume\":\"460 1\",\"pages\":\"1-4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"43\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE MTT-S International Microwave Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MWSYM.2015.7166826\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE MTT-S International Microwave Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MWSYM.2015.7166826","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A comparison of indirect learning and closed loop estimators used in digital predistortion of power amplifiers
Indirect learning is often used as an estimator in digital predistortion of power amplifiers (PAs). The estimator has inherent flaws that become apparent when signal bandwidths increase. These include coefficient offsets, excessive ADC sampling requirements, and susceptibility to EVM and PA saturation. A comparison to the closed loop estimator shows that these flaws are specific to the indirect learning estimator.
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