Ziad Hatab, Hiroaki Takahashi, M. Gadringer, W. Bosch
{"title":"OFDM Symbol-timing and Carrier-frequency Offset Estimation Based on Singular Value Decomposition","authors":"Ziad Hatab, Hiroaki Takahashi, M. Gadringer, W. Bosch","doi":"10.1109/CoBCom55489.2022.9880699","DOIUrl":null,"url":null,"abstract":"In this paper, we present a new technique for estimating symbol-timing offset (STO) and carrier frequency offset (CFO) in orthogonal frequency division multiplexing (OFDM) systems. The method we present is based on detecting a training sequence at the beginning of an OFDM stream using singular value decomposition (SVD), where STO and CFO are simultaneously estimated. We show by numerical simulations that our algorithm significantly improves STO and CFO estimation compared to conventional maximum likelihood (ML) techniques at low signal-to-noise ratio (SNR) values.","PeriodicalId":131597,"journal":{"name":"2022 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CoBCom55489.2022.9880699","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
In this paper, we present a new technique for estimating symbol-timing offset (STO) and carrier frequency offset (CFO) in orthogonal frequency division multiplexing (OFDM) systems. The method we present is based on detecting a training sequence at the beginning of an OFDM stream using singular value decomposition (SVD), where STO and CFO are simultaneously estimated. We show by numerical simulations that our algorithm significantly improves STO and CFO estimation compared to conventional maximum likelihood (ML) techniques at low signal-to-noise ratio (SNR) values.