{"title":"非线性数字预失真的重定向学习体系结构","authors":"Aaron F. Ramsey, Andrew K. Bolstad","doi":"10.1109/ICECS49266.2020.9294791","DOIUrl":null,"url":null,"abstract":"This paper introduces the redirected learning architecture (RLA) for estimating non-linear digital pre-distortion models for non-linear devices such as power amplifiers. Existing architectures can be classified as direct learning architectures (DLA) or indirect learning architectures (ILA). DLAs first learn a model of the device and then determine a pre-distorter by either directly inverting the device model or estimating a pre-inverse of the model. The RLA is similar to a DLA, but rather than learning a model of the device, the RLA uses fixed-point iteration to determine a set of input/output pairs which characterize the device. These pairs are then used to estimate the pre-distorter by redirecting learning from the device to the pre-distorter. The fixed-point iteration is shown to converge under a mild condition. Simulation of a class AB power amplifier reveals improved suppression of harmonic distortion compared to a pth order inverse approach.","PeriodicalId":404022,"journal":{"name":"2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Redirected Learning Architecture for Non-linear Digital Pre-distortion\",\"authors\":\"Aaron F. Ramsey, Andrew K. Bolstad\",\"doi\":\"10.1109/ICECS49266.2020.9294791\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces the redirected learning architecture (RLA) for estimating non-linear digital pre-distortion models for non-linear devices such as power amplifiers. Existing architectures can be classified as direct learning architectures (DLA) or indirect learning architectures (ILA). DLAs first learn a model of the device and then determine a pre-distorter by either directly inverting the device model or estimating a pre-inverse of the model. The RLA is similar to a DLA, but rather than learning a model of the device, the RLA uses fixed-point iteration to determine a set of input/output pairs which characterize the device. These pairs are then used to estimate the pre-distorter by redirecting learning from the device to the pre-distorter. The fixed-point iteration is shown to converge under a mild condition. Simulation of a class AB power amplifier reveals improved suppression of harmonic distortion compared to a pth order inverse approach.\",\"PeriodicalId\":404022,\"journal\":{\"name\":\"2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICECS49266.2020.9294791\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECS49266.2020.9294791","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Redirected Learning Architecture for Non-linear Digital Pre-distortion
This paper introduces the redirected learning architecture (RLA) for estimating non-linear digital pre-distortion models for non-linear devices such as power amplifiers. Existing architectures can be classified as direct learning architectures (DLA) or indirect learning architectures (ILA). DLAs first learn a model of the device and then determine a pre-distorter by either directly inverting the device model or estimating a pre-inverse of the model. The RLA is similar to a DLA, but rather than learning a model of the device, the RLA uses fixed-point iteration to determine a set of input/output pairs which characterize the device. These pairs are then used to estimate the pre-distorter by redirecting learning from the device to the pre-distorter. The fixed-point iteration is shown to converge under a mild condition. Simulation of a class AB power amplifier reveals improved suppression of harmonic distortion compared to a pth order inverse approach.