(To be removed) Equalize using decision feedback equalizer that updates weights with LMS algorithm
LMS Decision Feedback Equalizer will be removed in a future release. Use Decision Feedback Equalizer instead.
The LMS Decision Feedback Equalizer block uses a decision feedback 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. If the Number of samples per symbol parameter is
1, then the block implements a symbol-spaced equalizer;
otherwise, the block implements a fractionally 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
signal. Valid training symbols are those symbols listed in the Signal
Set the Reference tap parameter so it is greater than zero and less than the value for the Number of forward taps parameter.
The port labeled
Equalized outputs the result of the
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 the
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 of the forward filter.
The number of taps in the forward filter of the decision feedback equalizer.
The number of taps in the feedback filter of the decision feedback equalizer.
The number of input samples for each symbol.
A vector of complex numbers that specifies the constellation for the modulation.
A positive integer less than or equal to the number of forward 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 concatenates the initial weights for the forward and feedback taps.
If you select this check box, the block has an input port that enables 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 forward and feedback weights, concatenated into one vector.
 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.