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Title:Channel Estimation and Equalization Using Adaptive Algorithms
Author(s):Ryu, Christopher J.
Contributor(s):Singer, Andrew C.
Subject(s):signal processing
adaptive filters
channel estimation
channel equalization
least mean squares algorithms
recursive least squares algorithms
fast transversal filter algorithms
Abstract:Adaptive filters can be used for channel equalization and estimation. When a signal goes through an unknown, time-varying channel, an equalizer is used to approximate the original signal. Since the channel is time-varying, the adaptive filter (the equalizer) needs to be trained periodically in order to make good approximations. Channel estimation is very similar to equalization except that the input and output of the adaptive filter is swapped from the case of equalization. The adaptive filter estimates the channel by creating filter coefficients that eventually converge to the filter coefficients of the channel after proper amount of training. Three main algorithms, the least mean squares (LMS), the recursive least squares (RLS), and the fast transversal filter (FTF), are dealt with in this study. These algorithms have different characteristics that are observed during simulations. By creating different environments and random input signals in MATLAB, the performances of the filters are compared under controlled parameters.
Issue Date:2012-05
Publication Status:unpublished
Peer Reviewed:not peer reviewed
Date Available in IDEALS:2014-01-09

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