The steady-state of the (Normalized) LMS is schur convex

Handle URI:
http://hdl.handle.net/10754/621365
Title:
The steady-state of the (Normalized) LMS is schur convex
Authors:
Al-Hujaili, Khaled A.; Al-Naffouri, Tareq Y. ( 0000-0003-2843-5084 ) ; Moinuddin, Muhammad
Abstract:
In this work, we demonstrate how the theory of majorization and schur-convexity can be used to assess the impact of input-spread on the Mean Squares Error (MSE) performance of adaptive filters. First, we show that the concept of majorization can be utilized to measure the spread in input-regressors and subsequently order the input-regressors according to their spread. Second, we prove that the MSE of the Least Mean Squares Error (LMS) and Normalized LMS (NLMS) algorithms are schur-convex, that is, the MSE of the LMS and the NLMS algorithms preserve the majorization order of the inputs which provide an analytical justification to why and how much the MSE performance of the LMS and the NLMS algorithms deteriorate as the spread in input increases. © 2016 IEEE.
KAUST Department:
Electrical Engineering Program
Citation:
Al-Hujaili KA, Al-Naffouri TY, Moinuddin M (2016) The steady-state of the (Normalized) LMS is schur convex. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Available: http://dx.doi.org/10.1109/ICASSP.2016.7472609.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference/Event name:
41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Issue Date:
24-Jun-2016
DOI:
10.1109/ICASSP.2016.7472609
Type:
Conference Paper
Appears in Collections:
Conference Papers; Electrical Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorAl-Hujaili, Khaled A.en
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.contributor.authorMoinuddin, Muhammaden
dc.date.accessioned2016-11-03T06:58:36Z-
dc.date.available2016-11-03T06:58:36Z-
dc.date.issued2016-06-24en
dc.identifier.citationAl-Hujaili KA, Al-Naffouri TY, Moinuddin M (2016) The steady-state of the (Normalized) LMS is schur convex. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Available: http://dx.doi.org/10.1109/ICASSP.2016.7472609.en
dc.identifier.doi10.1109/ICASSP.2016.7472609en
dc.identifier.urihttp://hdl.handle.net/10754/621365-
dc.description.abstractIn this work, we demonstrate how the theory of majorization and schur-convexity can be used to assess the impact of input-spread on the Mean Squares Error (MSE) performance of adaptive filters. First, we show that the concept of majorization can be utilized to measure the spread in input-regressors and subsequently order the input-regressors according to their spread. Second, we prove that the MSE of the Least Mean Squares Error (LMS) and Normalized LMS (NLMS) algorithms are schur-convex, that is, the MSE of the LMS and the NLMS algorithms preserve the majorization order of the inputs which provide an analytical justification to why and how much the MSE performance of the LMS and the NLMS algorithms deteriorate as the spread in input increases. © 2016 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectAdaptive Filtersen
dc.titleThe steady-state of the (Normalized) LMS is schur convexen
dc.typeConference Paperen
dc.contributor.departmentElectrical Engineering Programen
dc.identifier.journal2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)en
dc.conference.date20 March 2016 through 25 March 2016en
dc.conference.name41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016en
dc.contributor.institutionDepartment of Electrical Engineering, Taibah University, Saudi Arabiaen
dc.contributor.institutionElectrical and Computer Engineering Department, King Abdulaziz University, Saudi Arabiaen
dc.contributor.institutionCenter of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Saudi Arabiaen
kaust.authorAl-Naffouri, Tareq Y.en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.