{"title":"中等复杂度下基于迭代相位的扩展阈值和估计范围频率估计","authors":"I. Perisa, J. Lindner","doi":"10.1109/SPAWC.2006.346347","DOIUrl":null,"url":null,"abstract":"In previous works, many efficient phase-based estimators with small computational complexity have been proposed. Most of them suffer from a very high threshold - i.e. below a certain signal-to-noise ratio (SNR) the estimator variance increases rapidly. Most of the approaches that improve the threshold are quite complex or have a limited estimation range. In a previous paper it was shown that iterative frequency offset estimation schemes can achieve a low threshold at a moderate complexity. Here we show how they can be combined with different estimators to achieve a faster convergence rate. This enables the construction of estimators that can easily be adapted to different requirements concerning estimation range, performance, and complexity","PeriodicalId":414942,"journal":{"name":"2006 IEEE 7th Workshop on Signal Processing Advances in Wireless Communications","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Iterative Phase-Based Frequency Estimation with Extended Threshold and Estimation Range at Moderate Complexity\",\"authors\":\"I. Perisa, J. Lindner\",\"doi\":\"10.1109/SPAWC.2006.346347\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In previous works, many efficient phase-based estimators with small computational complexity have been proposed. Most of them suffer from a very high threshold - i.e. below a certain signal-to-noise ratio (SNR) the estimator variance increases rapidly. Most of the approaches that improve the threshold are quite complex or have a limited estimation range. In a previous paper it was shown that iterative frequency offset estimation schemes can achieve a low threshold at a moderate complexity. Here we show how they can be combined with different estimators to achieve a faster convergence rate. This enables the construction of estimators that can easily be adapted to different requirements concerning estimation range, performance, and complexity\",\"PeriodicalId\":414942,\"journal\":{\"name\":\"2006 IEEE 7th Workshop on Signal Processing Advances in Wireless Communications\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE 7th Workshop on Signal Processing Advances in Wireless Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPAWC.2006.346347\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE 7th Workshop on Signal Processing Advances in Wireless Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPAWC.2006.346347","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Iterative Phase-Based Frequency Estimation with Extended Threshold and Estimation Range at Moderate Complexity
In previous works, many efficient phase-based estimators with small computational complexity have been proposed. Most of them suffer from a very high threshold - i.e. below a certain signal-to-noise ratio (SNR) the estimator variance increases rapidly. Most of the approaches that improve the threshold are quite complex or have a limited estimation range. In a previous paper it was shown that iterative frequency offset estimation schemes can achieve a low threshold at a moderate complexity. Here we show how they can be combined with different estimators to achieve a faster convergence rate. This enables the construction of estimators that can easily be adapted to different requirements concerning estimation range, performance, and complexity