{"title":"神经波形预失真在行波管实验数据中的应用","authors":"A. Bernardini, S. de Fina","doi":"10.1109/MELCON.1991.161878","DOIUrl":null,"url":null,"abstract":"An evaluation is made of the predistorter's achievable performance using HPA (high-power amplifier) models matched to experimental TWT data. How the neural net capability for inverse modeling, and then as predistorter, is related to the various TWTs that must be fitted is discussed. A supervised neural net is used with one internal neuron and no more than 10 internal unit, and the backpropagation algorithm for the learning process. The results related to TWT data obtained confirm the performances achievable with the generic TWT model: an average gain of 3 dB for the 64-QAM and an average gain of 5.5 dB for the 256-QAM systems, with respect to a baseband predistorter.<<ETX>>","PeriodicalId":193917,"journal":{"name":"[1991 Proceedings] 6th Mediterranean Electrotechnical Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Application of neural waveform predistortion to experimental TWT data\",\"authors\":\"A. Bernardini, S. de Fina\",\"doi\":\"10.1109/MELCON.1991.161878\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An evaluation is made of the predistorter's achievable performance using HPA (high-power amplifier) models matched to experimental TWT data. How the neural net capability for inverse modeling, and then as predistorter, is related to the various TWTs that must be fitted is discussed. A supervised neural net is used with one internal neuron and no more than 10 internal unit, and the backpropagation algorithm for the learning process. The results related to TWT data obtained confirm the performances achievable with the generic TWT model: an average gain of 3 dB for the 64-QAM and an average gain of 5.5 dB for the 256-QAM systems, with respect to a baseband predistorter.<<ETX>>\",\"PeriodicalId\":193917,\"journal\":{\"name\":\"[1991 Proceedings] 6th Mediterranean Electrotechnical Conference\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1991 Proceedings] 6th Mediterranean Electrotechnical Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MELCON.1991.161878\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1991 Proceedings] 6th Mediterranean Electrotechnical Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MELCON.1991.161878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of neural waveform predistortion to experimental TWT data
An evaluation is made of the predistorter's achievable performance using HPA (high-power amplifier) models matched to experimental TWT data. How the neural net capability for inverse modeling, and then as predistorter, is related to the various TWTs that must be fitted is discussed. A supervised neural net is used with one internal neuron and no more than 10 internal unit, and the backpropagation algorithm for the learning process. The results related to TWT data obtained confirm the performances achievable with the generic TWT model: an average gain of 3 dB for the 64-QAM and an average gain of 5.5 dB for the 256-QAM systems, with respect to a baseband predistorter.<>
![[1991 Proceedings] 6th Mediterranean Electrotechnical Conference](/Content/css/sci/img/zetp.jpg)
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
