{"title":"A Novel Least Mean Squares Algorithm for tracking a Discrete-time fBm Process","authors":"A. Gupta, S. Joshi","doi":"10.1109/INDCON.2006.302790","DOIUrl":null,"url":null,"abstract":"This paper presents a novel variable step-size LMS (VSLMS) algorithm for tracking a discrete-time fractional Brownian motion that is inherently non-stationary. In the proposed work, one of the step-size values requires time-varying constraints for the algorithm to converge to the optimal weights whereas the constraints on the remaining step-size values are time-invariant in the decoupled weight vector space. It computes the step-size matrix by estimating the Hurst exponent required to characterize the statistical properties of the signal at the input of the adaptive filter. The experimental set-up of an adaptive channel equalizer is considered for equalization of these signals transmitted over stationary AWGN channel. The performance of the proposed variable step-size LMS algorithm is compared with the unsigned VSLMS algorithm and is observed to be better for the class of non-stationary signals considered","PeriodicalId":122715,"journal":{"name":"2006 Annual IEEE India Conference","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 Annual IEEE India Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDCON.2006.302790","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
This paper presents a novel variable step-size LMS (VSLMS) algorithm for tracking a discrete-time fractional Brownian motion that is inherently non-stationary. In the proposed work, one of the step-size values requires time-varying constraints for the algorithm to converge to the optimal weights whereas the constraints on the remaining step-size values are time-invariant in the decoupled weight vector space. It computes the step-size matrix by estimating the Hurst exponent required to characterize the statistical properties of the signal at the input of the adaptive filter. The experimental set-up of an adaptive channel equalizer is considered for equalization of these signals transmitted over stationary AWGN channel. The performance of the proposed variable step-size LMS algorithm is compared with the unsigned VSLMS algorithm and is observed to be better for the class of non-stationary signals considered