By Topic

Blind Separation and Equalization Using Novel Hill-Climbing Optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Dongxin Xu ; Infoture, Inc., USA, Email: ; Hsiao-Chun Wu ; Chong-Yung Chi

In this paper, we construct a maximum-likelihood-equivalent metric or auxiliary function, which can result in a novel expectation-maximization Hill-Climbing (EM-HC) optimization procedure; it can be easily implemented for the estimation, detection and clustering applications since it is based on the simple auxiliary function. In this paper, one major application of our new EM-HC method, namely the blind separation and blind channel equalization, is presented and an efficient Iterative weighted least-mean squared (IWLMS) algorithm is derived thereupon. The new IWLMS algorithm derived from the EM-HC techniques greatly outperforms the prevalent blind equalization algorithm based on the constant-modulus criteria according to simulations.

Published in:

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers

Date of Conference:

4-7 Nov. 2007