| Communications Toolbox | |
Construct a signed least mean square (LMS) adaptive algorithm object
alg = signlms(stepsize)
alg = lms(stepsize,algtype)
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 Using Adaptive Equalizer Functions and Objects.
alg = signlms(stepsize) constructs an adaptive algorithm object based on the signed least mean square (LMS) algorithm with a step size of stepsize.
alg = lms(stepsize,algtype) constructs an adaptive algorithm object of type algtype from the family of signed LMS algorithms. The table below lists the possible values of 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 |
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 Accessing Properties of an Adaptive Algorithm.
| Property | Description |
|---|---|
| AlgType | The type of signed LMS algorithm, corresponding to the algtype input argument. You cannot change the value of this property after creating the object. |
| 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. |
Referring to the schematics presented in Overview of Adaptive Equalizer Classes, define w as the vector of all weights wi and define u as the vector of all inputs ui. Based on the current set of weights, w, this adaptive algorithm creates the new set of weights given by
(LeakageFactor) w + (StepSize) u*sgn(Re(e)), for sign LMS
(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).
lms, normlms, varlms, rls, cma, lineareq, dfe, equalize, Equalizers
[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.
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