Channel estimation enhanced LMS equalizer

Pulse or digital communications – Equalizers – Automatic

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Reexamination Certificate

active

07397849

ABSTRACT:
The present invention is related to an enhanced equalizer using channel estimation. A scaled version of a channel estimate is used as an expected average behavior of the product of a transmitted signal and a received signal to implement Griffith algorithm. The present invention also uses advance or prediction of a channel estimate to overcome the lag problem inherent in a least means square (LMS) algorithm in a time varying channel. Therefore, the present invention enables the use of a small step size while attaining the same tracking capability with a large step size. A channel estimate at some time in the future is used for updating equalizer filter tap coefficients. This may be performed with a prediction filter. Alternatively, a delay may be introduced in the input data to the filter tap coefficient generator, which makes a channel estimate look like a prediction to the filter tap coefficient generator.

REFERENCES:
patent: 6608862 (2003-08-01), Zangi et al.
patent: 6618433 (2003-09-01), Yellin
patent: 6937676 (2005-08-01), Takada et al.
patent: 2006/0114974 (2006-06-01), Zeira et al.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Channel estimation enhanced LMS equalizer does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Channel estimation enhanced LMS equalizer, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Channel estimation enhanced LMS equalizer will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-2813382

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.