(To be removed) Equalize using linear equalizer that updates weights with LMS algorithm
LMS Linear Equalizer will be removed in a future release. Use Linear Equalizer instead.
The LMS Linear Equalizer block uses a linear equalizer and the LMS algorithm to equalize a
linearly modulated baseband signal through a dispersive channel. During the simulation, the
block uses the LMS algorithm to update the weights, once per symbol. When you set the
Number of samples per symbol parameter to
the block implements a symbol-spaced (i.e. T-spaced) equalizer. When you set the
Number of samples per symbol parameter to a value greater than one, the
block updates the weights once every Nth sample
for a T/N-spaced equalizer.
Input port accepts a column vector input signal. The
Desired port receives a training sequence with a length that is less
than or equal to the number of symbols in the
Input signal. Valid
training symbols are those symbols listed in the Signal constellation
Set the Reference tap parameter so it is greater than zero and less than the value for the Number of taps parameter.
Equalized port outputs the result of the equalization
You can configure the block to have one or more of these extra ports:
Err output for the error signal, which is the difference between
Equalized output and the reference signal. The reference signal
consists of training symbols in training mode, and detected symbols otherwise.
To learn the conditions under which the equalizer operates in training or decision-directed mode, see Equalization.
For proper equalization, you should set the Reference tap parameter so that it exceeds the delay, in symbols, between the transmitter's modulator output and the equalizer input. When this condition is satisfied, the total delay, in symbols, between the modulator output and the equalizer output is equal to
1+(Reference tap-1)/(Number of samples per symbol)
Because the channel delay is typically unknown, a common practice is to set the reference tap to the center tap.
The number of taps in the filter of the linear equalizer.
The number of input samples for each symbol.
A vector of complex numbers that specifies the constellation for the modulated signal, as determined by the modulator in your model
A positive integer less than or equal to the number of taps in the equalizer.
The step size of the LMS algorithm.
The leakage factor of the LMS algorithm, a number between 0 and 1. A value of 1 corresponds to a conventional weight update algorithm, and a value of 0 corresponds to a memoryless update algorithm.
A vector that lists the initial weights for the taps.
If you select this check box, the block has an input port that allows you to toggle between training and decision-directed mode. For training, the mode input must be 1, and for decision directed, the mode must be 0. For every frame in which the mode input is 1 or not present, the equalizer trains at the beginning of the frame for the length of the desired signal.
If you select this check box, the block outputs the error signal, which is the difference between the equalized signal and the reference signal.
If you select this check box, the block outputs the current weights.
 Farhang-Boroujeny, B., Adaptive Filters: Theory and Applications, Chichester, England, Wiley, 1998.
 Haykin, Simon, Adaptive Filter Theory, Third Ed., Upper Saddle River, N.J., Prentice-Hall, 1996.
 Kurzweil, Jack, An Introduction to Digital Communications, New York, Wiley, 2000.
 Proakis, John G., Digital Communications, Fourth Ed., New York, McGraw-Hill, 2001.