# signlms

(To be removed) Construct signed least mean square (LMS) adaptive algorithm object

**signlms will be removed in a future release. Consider using comm.LinearEqualizer or comm.DecisionFeedback instead.**

## Syntax

`alg = signlms(stepsize)`

alg = signlms(stepsize,* algtype*)

## Description

The `signlms`

function creates an adaptive algorithm object that
you can use with the `lineareq`

function or `dfe`

function to create an equalizer object. You can then use the
equalizer object with the `equalize`

function to equalize a
signal. To learn more about the process for equalizing a signal, see Equalization.

`alg = signlms(stepsize)`

constructs an
adaptive algorithm object based on the signed least mean square (LMS) algorithm with a
step size of `stepsize`

.

`alg = signlms(stepsize,`

constructs an adaptive algorithm object of type * algtype*)

*from the family of signed LMS algorithms. The table below lists the possible values of*

`algtype`

*.*

`algtype`

Value of `algtype` | Type of Signed LMS Algorithm |
---|---|

`'Sign LMS'` | Sign LMS (default) |

`'Signed Regressor LMS'` | Signed regressor LMS |

`'Sign Sign LMS'` | Sign-sign LMS |

### Properties

The table below describes the properties of the signed LMS adaptive algorithm object. To learn how to view or change the values of an adaptive algorithm object, see Equalization.

Property | Description |
---|---|

`AlgType` | Type of signed LMS algorithm, corresponding to the
input argument. You cannot
change the value of this property after creating the object.`algtype` |

`StepSize` | LMS step size parameter, a nonnegative real number |

`LeakageFactor` | LMS leakage factor, a real number between 0 and 1. A value of 1 corresponds to a conventional weight update algorithm, while a value of 0 corresponds to a memoryless update algorithm. |

## Examples

## Algorithms

Referring to the schematics presented in Equalization,
define *w* as the vector of all weights
*w*_{i} and define *u* as the
vector of all inputs *u*_{i}. Based on the current
set of weights, *w*, this adaptive algorithm creates the new set of
weights given by

`(LeakageFactor) w + (StepSize) u`

, for sign LMS^{*}sgn(Re(e))`(LeakageFactor) w + (StepSize) sgn(Re(u)) Re(e)`

, for signed regressor LMS`(LeakageFactor) w + (StepSize) sgn(Re(u)) sgn(Re(e))`

, for sign-sign LMS

where the `*`

operator denotes the complex conjugate and
`sgn`

denotes the signum function (`sign`

in
MATLAB^{®} technical computing software).

## Compatibility Considerations

## References

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

[2] Kurzweil, J., *An Introduction to Digital
Communications*, New York, John Wiley & Sons, 2000.

**Introduced before R2006a**