{"title":"基于QR分解的递推最小二乘常模算法","authors":"Wang Shuyan, Wu Renbiao, Shi Qing-yan","doi":"10.1109/ICNNSP.2008.4590331","DOIUrl":null,"url":null,"abstract":"A novel QR-RLS constant modulus algorithm called QR-RLS-CMA is proposed. Its potential advantages include numerical stability, computational efficiency and a fast convergence rate. Simulations are performed to compare the convergence performance and the blind extracting ability of the proposed QR-RLS-CMA to the conventional SGD-CMA for adaptive CMA array. Results indicate that the QR-RLS-CMA has a much faster convergence rate than the SGD-CMA in the initial convergence phase. It illustrates the effectiveness of the proposed method.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recursive least squares constant modulus algorithm based on the QR decomposition\",\"authors\":\"Wang Shuyan, Wu Renbiao, Shi Qing-yan\",\"doi\":\"10.1109/ICNNSP.2008.4590331\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel QR-RLS constant modulus algorithm called QR-RLS-CMA is proposed. Its potential advantages include numerical stability, computational efficiency and a fast convergence rate. Simulations are performed to compare the convergence performance and the blind extracting ability of the proposed QR-RLS-CMA to the conventional SGD-CMA for adaptive CMA array. Results indicate that the QR-RLS-CMA has a much faster convergence rate than the SGD-CMA in the initial convergence phase. It illustrates the effectiveness of the proposed method.\",\"PeriodicalId\":250993,\"journal\":{\"name\":\"2008 International Conference on Neural Networks and Signal Processing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Neural Networks and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNNSP.2008.4590331\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Neural Networks and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNNSP.2008.4590331","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recursive least squares constant modulus algorithm based on the QR decomposition
A novel QR-RLS constant modulus algorithm called QR-RLS-CMA is proposed. Its potential advantages include numerical stability, computational efficiency and a fast convergence rate. Simulations are performed to compare the convergence performance and the blind extracting ability of the proposed QR-RLS-CMA to the conventional SGD-CMA for adaptive CMA array. Results indicate that the QR-RLS-CMA has a much faster convergence rate than the SGD-CMA in the initial convergence phase. It illustrates the effectiveness of the proposed method.