(To be removed) Construct signed least mean square (LMS) adaptive algorithm object
alg = signlms(stepsize)
alg = signlms(stepsize,
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
alg = signlms(stepsize,
constructs an adaptive algorithm object of type
the family of signed LMS algorithms. The table below lists the possible values of
|Value of ||Type of Signed LMS Algorithm|
|Sign LMS (default)|
|Signed regressor 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 Equalization.
|Type of signed LMS algorithm, corresponding to the
|LMS step size parameter, a nonnegative real number|
|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.|
This example shows to use a signed least mean square (LMS) algorithm to create an adaptive equalizer object.
Set the number of weights and the step size for the equalizer.
nWeights = 2; stepSize = 0.05;
Create the adaptive algorithm object using the signed regressor LMS algorithm type.
alg = signlms(stepSize,'Signed Regressor LMS');
Construct a linear equalizer using the algorithm object.
eqObj = lineareq(nWeights,alg)
eqObj = EqType: 'Linear Equalizer' AlgType: 'Signed Regressor LMS' nWeights: 2 nSampPerSym: 1 RefTap: 1 SigConst: [-1 1] StepSize: 0.0500 LeakageFactor: 1 Weights: [0 0] WeightInputs: [0 0] ResetBeforeFiltering: 1 NumSamplesProcessed: 0
Referring to the schematics presented in Equalization, 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
(LeakageFactor) w + (StepSize) sgn(Re(u)) sgn(Re(e)), for
* operator denotes the complex conjugate and
sgn denotes the signum function (
MATLAB® technical computing software).
 Farhang-Boroujeny, B., Adaptive Filters: Theory and Applications, Chichester, England, John Wiley & Sons, 1998.
 Kurzweil, J., An Introduction to Digital Communications, New York, John Wiley & Sons, 2000.