{"title":"A Fuzzy Logic Based Channel Estimation in Walsh-Hadamard Transform Employed OFDM Systems","authors":"A. Ozen, B. Soysal, L. Kay","doi":"10.1109/SIU.2007.4298601","DOIUrl":null,"url":null,"abstract":"Performances of channel estimation and carrier frequency offset (CFO) tracking algorithms for a Walsh Hadamard transform employed OFDM systems are investigated for frequency selective Rayleigh fading channels in this paper. Since the channel estimation and CFO tracking performance of the simple least mean squares (LMS) is found to be poorer, a fuzzy based step-size controller is employed to increase speed and accuracy of LMS training. The simulation results show that the proposed method increases the performance of the LMS algorithm significantly and converges to the performances of the RLS algorithm.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE 15th Signal Processing and Communications Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2007.4298601","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
Performances of channel estimation and carrier frequency offset (CFO) tracking algorithms for a Walsh Hadamard transform employed OFDM systems are investigated for frequency selective Rayleigh fading channels in this paper. Since the channel estimation and CFO tracking performance of the simple least mean squares (LMS) is found to be poorer, a fuzzy based step-size controller is employed to increase speed and accuracy of LMS training. The simulation results show that the proposed method increases the performance of the LMS algorithm significantly and converges to the performances of the RLS algorithm.