{"title":"Blind Identification of Frequency Selective Channel using Higher Order Statistics","authors":"S. Safi, M. Frikel, M. M'Saad, A. Zeroual","doi":"10.1109/ICSPC.2007.4728283","DOIUrl":null,"url":null,"abstract":"The present paper deals with blind identification of frequency selective communication channel. The problem of frequency selectivity is more encountered in mobile communication channel. In this manuscript, we have considered two channels as the Proakis's `B' channel and the Macchi's channel, in order to identify these channel we propose an algorithm based on Higher Order Statistics (HOS). The simulation results in noisy environment and for different data input channel, demonstrate that the proposed method could estimate the phase and magnitude of these channels blindly and without any information about the input, except that the input excitation is i.i.d (Identically and Independent Distributed) and non-Gaussian.","PeriodicalId":425397,"journal":{"name":"2007 IEEE International Conference on Signal Processing and Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Signal Processing and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSPC.2007.4728283","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The present paper deals with blind identification of frequency selective communication channel. The problem of frequency selectivity is more encountered in mobile communication channel. In this manuscript, we have considered two channels as the Proakis's `B' channel and the Macchi's channel, in order to identify these channel we propose an algorithm based on Higher Order Statistics (HOS). The simulation results in noisy environment and for different data input channel, demonstrate that the proposed method could estimate the phase and magnitude of these channels blindly and without any information about the input, except that the input excitation is i.i.d (Identically and Independent Distributed) and non-Gaussian.