{"title":"On the design of symbol timing recovery for WLAN OFDM systems","authors":"Chia-Horng Liu","doi":"10.1109/ISSSTA.2004.1371689","DOIUrl":null,"url":null,"abstract":"We present a simple and robust symbol timing recovery algorithm for OFDM systems, such as IEEE 802.11a/g. The algorithm is derived according to a maximum likelihood (ML) algorithm utilizing the cyclic extension or cyclic prefix (CP), which is appended in front of each OFDM symbol. Simulation results show that the performance of the proposed algorithm is better than the conventional ML algorithm in the additive white Gaussian noise (AWGN) channel and approaches to the ML algorithm in the Rayleigh fading channel. Furthermore, the implementation complexity is lower than that of the ML algorithm.","PeriodicalId":340769,"journal":{"name":"Eighth IEEE International Symposium on Spread Spectrum Techniques and Applications - Programme and Book of Abstracts (IEEE Cat. No.04TH8738)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Eighth IEEE International Symposium on Spread Spectrum Techniques and Applications - Programme and Book of Abstracts (IEEE Cat. No.04TH8738)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSSTA.2004.1371689","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
We present a simple and robust symbol timing recovery algorithm for OFDM systems, such as IEEE 802.11a/g. The algorithm is derived according to a maximum likelihood (ML) algorithm utilizing the cyclic extension or cyclic prefix (CP), which is appended in front of each OFDM symbol. Simulation results show that the performance of the proposed algorithm is better than the conventional ML algorithm in the additive white Gaussian noise (AWGN) channel and approaches to the ML algorithm in the Rayleigh fading channel. Furthermore, the implementation complexity is lower than that of the ML algorithm.