article · 1999
Performance analysis of a recursive fractional super-exponential algorithm
Victor Barroso
Cited by 0Abstract and details only
Abstract
The super-exponential algorithm is a block-based tech-nique for blind channel equalization and system identi-cation. Due to its fast convergence rate, and no a pri-ori parameterization other than the block length, it is a useful tool for linear equalization of moderately distortive channels. This paper presents a recursive implementation of the super-exponential algorithm for fractionally-sampled PAM signals. Although the resulting algorithm is still block-based, recursive propagation of several key variables allows the block length to be signicantly reduced without compromising the algorithm's accuracy or speed, thereby enhancing its ability to track channel variations. The con-vergence rate is only mildly in\nuenced by specic channel responses, and oversampling provides smaller output vari-ance and almost perfect tolerance to sampling errors. Simu-lation results demonstrate the eectiveness of the proposed technique. 1.
Cite this paper
Barroso, V. (1999). Performance analysis of a recursive fractional super-exponential algorithm.
Barroso, Victor. “Performance analysis of a recursive fractional super-exponential algorithm.” 1999.
Barroso, Victor. 1999. “Performance analysis of a recursive fractional super-exponential algorithm.”
Barroso, V. (1999) ‘Performance analysis of a recursive fractional super-exponential algorithm’.
@article{barroso1999performance,
title = {{Performance analysis of a recursive fractional super-exponential algorithm}},
author = {Barroso, Victor},
year = {1999}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.