{"title":"基于神经网络的Volterra核提取放大器行为建模","authors":"Jelena Misic, V. Markovic, Z. Marinković","doi":"10.1109/BIHTEL.2014.6987646","DOIUrl":null,"url":null,"abstract":"In wireless communication systems the amplifier non-linear distortion problems are extremely challenging. The linearization techniques based on behavioral models of amplifiers, seem to be very promising, therefore developing a suitable non-linear model is of the crucial importance. A rigorous approach for non-linear modeling is using the of Volterra series, however the calculation of the Volterra coefficients is a complex and time-consuming task. In this paper, an easy and advanced approach for extraction of the Volterra kernels will be presented. The third order Volterra kernels are derived from the parameters of a feed-forward time delay neural network with a suitable activation function.","PeriodicalId":415492,"journal":{"name":"2014 X International Symposium on Telecommunications (BIHTEL)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Volterra kernels extraction from neural networks for amplifier behavioral modeling\",\"authors\":\"Jelena Misic, V. Markovic, Z. Marinković\",\"doi\":\"10.1109/BIHTEL.2014.6987646\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In wireless communication systems the amplifier non-linear distortion problems are extremely challenging. The linearization techniques based on behavioral models of amplifiers, seem to be very promising, therefore developing a suitable non-linear model is of the crucial importance. A rigorous approach for non-linear modeling is using the of Volterra series, however the calculation of the Volterra coefficients is a complex and time-consuming task. In this paper, an easy and advanced approach for extraction of the Volterra kernels will be presented. The third order Volterra kernels are derived from the parameters of a feed-forward time delay neural network with a suitable activation function.\",\"PeriodicalId\":415492,\"journal\":{\"name\":\"2014 X International Symposium on Telecommunications (BIHTEL)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 X International Symposium on Telecommunications (BIHTEL)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIHTEL.2014.6987646\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 X International Symposium on Telecommunications (BIHTEL)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIHTEL.2014.6987646","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Volterra kernels extraction from neural networks for amplifier behavioral modeling
In wireless communication systems the amplifier non-linear distortion problems are extremely challenging. The linearization techniques based on behavioral models of amplifiers, seem to be very promising, therefore developing a suitable non-linear model is of the crucial importance. A rigorous approach for non-linear modeling is using the of Volterra series, however the calculation of the Volterra coefficients is a complex and time-consuming task. In this paper, an easy and advanced approach for extraction of the Volterra kernels will be presented. The third order Volterra kernels are derived from the parameters of a feed-forward time delay neural network with a suitable activation function.