The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Nov. 30, 1999
Filed:
Apr. 10, 1996
Richard G Yamasaki, Torrance, CA (US);
Ryohei Kuki, Tokyo, JP;
Ho-Ming Lin, Irvine, CA (US);
Craig Tammel, Balboa Island, CA (US);
Silicon Systems, Inc., Tustin, CA (US);
Abstract
A method and apparatus for reducing noise correlation in a partial response channel through optimization of a look-ahead maximum likelihood (ML) detector. In the method of the present invention, the ML detector is optimized in light of the noise correlation generated by the partial response channel. The improved ML detector provides comparable performance to, or better performance than, a Viterbi detector in the presence of colored noise. In the present invention, a set of finite impulse response (FIR) transversal filters are used as the ML estimator for the look-ahead detector. The weighted sum outputs of the FIR filters are compared to a set of thresholds based on previously detected data to make the decision for current detection. The present invention improves the ML detector's performance and reduces its complexity by optimizing the coefficients of the FIR filters in the presence of the correlated or colored noise. The SNR (signal-to-noise ratio) of each FIR filter is determined for a range of coefficients based on the noise autocorrelation of the channel for a given user density, and the coefficients providing the highest SNR are selected for each decision function. The result is a noise whitening ML detector providing improved performance and lower complexity than prior art ML detectors.