Sign LMS Linear Equalizer

Equalize using linear equalizer that updates weights with signed LMS algorithm

Library

Equalizers

Description

The Sign LMS Linear Equalizer block uses a linear equalizer and an algorithm from the family of signed LMS algorithms to equalize a linearly modulated baseband signal through a dispersive channel. The supported algorithms, corresponding to the Update algorithm parameter, are

  • Sign LMS

  • Sign Regressor LMS

  • Sign Sign LMS

During the simulation, the block uses the particular signed 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.

Input and Output Signals

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 taps parameter.

The Equalized port 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 Adaptive Algorithms 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.

Decision-Directed Mode and Training Mode

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.

Equalizer Delay

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.

Dialog Box

Update algorithm

The specific type of signed LMS algorithm that the block uses to update the equalizer weights.

Number of taps

The number of taps in the filter of the linear equalizer.

Number of samples per symbol

The number of input samples for each symbol.

Signal constellation

A vector of complex numbers that specifies the constellation for the modulation.

Reference tap

A positive integer less than or equal to the number of taps in the equalizer.

Step size

The step size of the signed LMS algorithm.

Leakage factor

The leakage factor of the signed 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.

Initial weights

A vector that lists the initial weights for the taps.

Mode input port

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.

Output error

When you select this check box, the block outputs the error signal, which is the difference between the equalized signal and the reference signal.

Output weights

When you select this check box, the block outputs the current weights.

Examples

See the Adaptive Equalization example.

References

[1] Farhang-Boroujeny, B., Adaptive Filters: Theory and Applications, Chichester, England, Wiley, 1998.

[2] Kurzweil, Jack, An Introduction to Digital Communications, New York, Wiley, 2000.

Was this topic helpful?