{"title":"基于递归算法的OFDM下行载波频偏估计","authors":"S. K. L. V. Sai Prakash, J. Lakshmi","doi":"10.1109/SPACES.2015.7058200","DOIUrl":null,"url":null,"abstract":"A novel recursive algorithm for estimating the carrier frequency offset (CFO) is presented which is compared against a basic maximum likelihood (ML) algorithm for orthogonal frequency division multiplexing (OFDM) scheme. Cyclic prefix (CP) is used for estimating the offset which enables the estimation without additional sequences. The recursive algorithm reduces the buffer size of the system by updating the value of offset to the present estimate. A weighted variable is taken for updating the previous offset. Simulations are done for comparing the length of prefix to be used for better estimation accuracy. Results show that the recursive algorithm has better performance than the ML estimator.","PeriodicalId":432479,"journal":{"name":"2015 International Conference on Signal Processing and Communication Engineering Systems","volume":"82 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Carrier frequency offset estimation using novel recursive algorithm for OFDM downlink\",\"authors\":\"S. K. L. V. Sai Prakash, J. Lakshmi\",\"doi\":\"10.1109/SPACES.2015.7058200\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel recursive algorithm for estimating the carrier frequency offset (CFO) is presented which is compared against a basic maximum likelihood (ML) algorithm for orthogonal frequency division multiplexing (OFDM) scheme. Cyclic prefix (CP) is used for estimating the offset which enables the estimation without additional sequences. The recursive algorithm reduces the buffer size of the system by updating the value of offset to the present estimate. A weighted variable is taken for updating the previous offset. Simulations are done for comparing the length of prefix to be used for better estimation accuracy. Results show that the recursive algorithm has better performance than the ML estimator.\",\"PeriodicalId\":432479,\"journal\":{\"name\":\"2015 International Conference on Signal Processing and Communication Engineering Systems\",\"volume\":\"82 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Signal Processing and Communication Engineering Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPACES.2015.7058200\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Signal Processing and Communication Engineering Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPACES.2015.7058200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Carrier frequency offset estimation using novel recursive algorithm for OFDM downlink
A novel recursive algorithm for estimating the carrier frequency offset (CFO) is presented which is compared against a basic maximum likelihood (ML) algorithm for orthogonal frequency division multiplexing (OFDM) scheme. Cyclic prefix (CP) is used for estimating the offset which enables the estimation without additional sequences. The recursive algorithm reduces the buffer size of the system by updating the value of offset to the present estimate. A weighted variable is taken for updating the previous offset. Simulations are done for comparing the length of prefix to be used for better estimation accuracy. Results show that the recursive algorithm has better performance than the ML estimator.