Shujaat Khan, Muhammad Usman, I. Naseem, R. Togneri, Bennamoun
{"title":"VP-FLMS: A Novel Variable Power Fractional LMS Algorithm","authors":"Shujaat Khan, Muhammad Usman, I. Naseem, R. Togneri, Bennamoun","doi":"10.1109/ICUFN.2017.7993796","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an adaptive framework for the variable power of the fractional least mean square (FLMS) algorithm using the concept of instantaneous error energy. The proposed algorithm named variable power-FLMS (VP-FLMS) is computationally less expensive and dynamically adapts the fractional power of the FLMS to achieve a high convergence rate with a low steady state error. For the evaluation purpose, the problems of channel estimation and channel equalization are considered. The experiments clearly show that the proposed approach achieves better convergence rate and lower steady-state error compared to the FLMS.","PeriodicalId":284480,"journal":{"name":"2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICUFN.2017.7993796","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
In this paper, we propose an adaptive framework for the variable power of the fractional least mean square (FLMS) algorithm using the concept of instantaneous error energy. The proposed algorithm named variable power-FLMS (VP-FLMS) is computationally less expensive and dynamically adapts the fractional power of the FLMS to achieve a high convergence rate with a low steady state error. For the evaluation purpose, the problems of channel estimation and channel equalization are considered. The experiments clearly show that the proposed approach achieves better convergence rate and lower steady-state error compared to the FLMS.