Equalize using decision feedback equalizer that updates weights with variable-step-size LMS algorithm
The Variable Step LMS Decision Feedback Equalizer block uses a decision feedback equalizer and the variable-step-size LMS algorithm to equalize a linearly modulated baseband signal through a dispersive channel. During the simulation, the block uses the variable-step-size LMS algorithm to update the weights, once per symbol. When you set the Number of samples per symbol parameter to 1, then the block implements a symbol-spaced equalizer and updates the filter weights once for each symbol. When you set the Number of samples per symbol parameter to a value greater than 1, the weights are updated once every Nth sample, for a T/N-spaced equalizer.
The 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 vector.
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 equalization process.
You can configure the block to have one or more of these extra ports:
Mode input, as described in Reference Signal and Operation Modes in Communications System Toolbox™ User's Guide.
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.
Weights output, as described in Adaptive Algorithms in Communications System Toolbox User's Guide.
To learn the conditions under which the equalizer operates in training or decision-directed mode, see Adaptive Algorithms in Communications System Toolbox User's Guide.
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 that the variable-step-size LMS algorithm uses at the beginning of the simulation.
The increment by which the step size changes from iteration to iteration
The smallest value that the step size can assume.
The largest value that the step size can assume.
The leakage factor of the variable-step-size 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.
When 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, for decision directed, the mode should be 0. The equalizer will train for the length of the Desired signal. If the mode input is not present, the equalizer will train at the beginning of every frame for the length of the Desired signal.
When you select this check box, the block outputs the error signal, which is the difference between the equalized signal and the reference signal.
When 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.