(To be removed) Equalize using linear equalizer that updates weights with normalized LMS algorithm
Normalized LMS Linear Equalizer will be removed in a future release. Use Linear Equalizer instead.
The Normalized LMS Linear Equalizer block uses a linear equalizer and the normalized
LMS algorithm to equalize a linearly modulated baseband signal through a dispersive
channel. During the simulation, the block uses the normalized LMS algorithm to update
the weights, once per symbol. When you set the Number of samples per
symbol parameter to
1, the block implements a
symbol-spaced (i.e. T-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
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 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.
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 modulation.
A positive integer less than or equal to the number of taps in the equalizer.
The step size of the normalized LMS algorithm.
The leakage factor of the normalized 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.
The bias parameter of the normalized LMS algorithm, a nonnegative real number. This parameter is used to overcome difficulties when the algorithm's input signal is small.
A vector that lists the initial weights for the taps.
When 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, for decision directed, the mode should 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 check this box, the block outputs the error signal, which is the difference between the equalized signal and the reference signal.
If you check this box, the block outputs the current weights.
 Farhang-Boroujeny, B., Adaptive Filters: Theory and Applications, Chichester, England, Wiley, 1998.