Z. Jin, Xiuling Ding, Zhengxiong Jiang, Yingsong Li
{"title":"块稀疏系统估计的改进μ律比例NLMS算法","authors":"Z. Jin, Xiuling Ding, Zhengxiong Jiang, Yingsong Li","doi":"10.1109/ICEICT.2019.8846290","DOIUrl":null,"url":null,"abstract":"An improved μ-law proportionate normalized least mean square (MPNLMS) algorithm is presented and analyzed for giving an estimation of block-sparse systems, which is also named as block-sparse MPNLMS (BS-MPNLMS). The proposed BS-MPNLMS algorithm introduces a hybrid $l_{2,1}$-norm into the MPNLMS’s cost function to create a penalty. The devised new BS-MPNLMS is derived in detail and is analyzed for estimating the network echo signals whose response has a typical block-sparse characteristic. Numerical simulation results show that the devised algorithm has better convergence and stability performance for handling the block-sparse systems compared with related algorithms.","PeriodicalId":382686,"journal":{"name":"2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Improved μ-law Proportionate NLMS Algorithm for Estimating Block-Sparse Systems\",\"authors\":\"Z. Jin, Xiuling Ding, Zhengxiong Jiang, Yingsong Li\",\"doi\":\"10.1109/ICEICT.2019.8846290\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An improved μ-law proportionate normalized least mean square (MPNLMS) algorithm is presented and analyzed for giving an estimation of block-sparse systems, which is also named as block-sparse MPNLMS (BS-MPNLMS). The proposed BS-MPNLMS algorithm introduces a hybrid $l_{2,1}$-norm into the MPNLMS’s cost function to create a penalty. The devised new BS-MPNLMS is derived in detail and is analyzed for estimating the network echo signals whose response has a typical block-sparse characteristic. Numerical simulation results show that the devised algorithm has better convergence and stability performance for handling the block-sparse systems compared with related algorithms.\",\"PeriodicalId\":382686,\"journal\":{\"name\":\"2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)\",\"volume\":\"87 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEICT.2019.8846290\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEICT.2019.8846290","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Improved μ-law Proportionate NLMS Algorithm for Estimating Block-Sparse Systems
An improved μ-law proportionate normalized least mean square (MPNLMS) algorithm is presented and analyzed for giving an estimation of block-sparse systems, which is also named as block-sparse MPNLMS (BS-MPNLMS). The proposed BS-MPNLMS algorithm introduces a hybrid $l_{2,1}$-norm into the MPNLMS’s cost function to create a penalty. The devised new BS-MPNLMS is derived in detail and is analyzed for estimating the network echo signals whose response has a typical block-sparse characteristic. Numerical simulation results show that the devised algorithm has better convergence and stability performance for handling the block-sparse systems compared with related algorithms.