Multiple access interference plus noise constrained least mean square algorithm

In this work, a constrained least-mean-square (LMS) algorithm, which incorporates the knowledge of the number of users, spreading sequence length and additive noise variance, is developed subject to the new combined constraint comprising both the MAI and noise variance. The novelty of this constrain...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the ... European Signal Processing Conference (EUSIPCO) pp. 1 - 5
Main Authors Zerguine, Azzedine, Moinuddin, Muhammad, Sheikh, Asrar U. H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2008
Subjects
Abstracts
Correlation
Decision support systems
Least squares approximations
Transient analysis
Zinc
Online AccessGet full text
ISSN2219-5491
2219-5491

Cover

More Information
Summary:In this work, a constrained least-mean-square (LMS) algorithm, which incorporates the knowledge of the number of users, spreading sequence length and additive noise variance, is developed subject to the new combined constraint comprising both the MAI and noise variance. The novelty of this constraint resides in the fact that the MAI variance was never used as a constraint. This constrained optimization technique results in an (MAI plus noise)-constrained LMS (MNCLMS) algorithm. Convergence analysis is carried out of the proposed algorithm in the presence of MAI. Finally, a number of simulations are conducted to compare performance of MNC-LMS algorithm with other adaptive algorithms.
ISSN:2219-5491
2219-5491