By Topic

Convolutive-Error-Measure Analysis for Inverse Filtering and Minimum Total-Model-Order Determination Subject to Positive-Definiteness

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
$31 $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

2 Author(s)
Shih Yu Chang ; Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan ; Hsiao-Chun Wu

To the best of our knowledge, there exists no explicit formulation of the exact L 2 convolutive error for any arbitrary filter (system) and the corresponding truncated inverse filter. In addition, the approach to determine the minimum total-model-order of the inverse filter subject to the maximum allowable L 2 convolutive error is also in demand. In this paper, we first derive the general formula of the L 2 convolutive error measure with respect to the filter coefficients. According to this new error measure function, we design an optimal inverse finite impulse response (FIR) filter given the fixed model orders to achieve the minimum convolutive error. Then, we propose a new algorithm to determine the minimum total-model-order of the appropriate truncated inverse filter to achieve a specified convolutive error based on the discrete filled function approach. The numerical evaluation is demonstrated for a tradeoff between the total-model-order and the system performance, e.g., the bit error rate (BER) for a communication receiver compensated by an FIR equalizer.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:4 ,  Issue: 3 )