{"title":"An improved method for OFDM carrier frequency offset estimation","authors":"Jijun Zheng, Weile Zhu","doi":"10.1109/ICCCAS.2007.6250052","DOIUrl":null,"url":null,"abstract":"An efficient maximum likelihood estimator has been proposed by Moose [4] for carrier frequency estimation in orthogonal frequency division multiplexing systems. The scheme is based on the observation of two consecutive and identical symbols in the frequency domain and its estimation range is less than or equal to +0.5 of the subcarrier spacing. In this paper, we extend the algorithm by using training symbols in the time-domain. It can widen the estimation range and the performance is the same as the Moose algorithm at the cost of increasing the computational complexity.","PeriodicalId":218351,"journal":{"name":"2007 International Conference on Communications, Circuits and Systems","volume":"167 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Conference on Communications, Circuits and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCAS.2007.6250052","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
An efficient maximum likelihood estimator has been proposed by Moose [4] for carrier frequency estimation in orthogonal frequency division multiplexing systems. The scheme is based on the observation of two consecutive and identical symbols in the frequency domain and its estimation range is less than or equal to +0.5 of the subcarrier spacing. In this paper, we extend the algorithm by using training symbols in the time-domain. It can widen the estimation range and the performance is the same as the Moose algorithm at the cost of increasing the computational complexity.
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