| Communications Toolbox | |
Construct variable-step-size least mean square (LMS) adaptive algorithm object
alg = varlms(initstep,incstep,minstep,maxstep)
The varlms 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 = varlms(initstep,incstep,minstep,maxstep) constructs an adaptive algorithm object based on the variable-step-size least mean square (LMS) algorithm. initstep is the initial value of the step size parameter. incstep is the increment by which the step size changes from iteration to iteration. minstep and maxstep are the limits between which the step size can vary.
The table below describes the properties of the variable-step-size 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 | Fixed value, 'Variable Step Size LMS' |
| 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. |
| InitStep | Initial value of step size when the algorithm starts |
| IncStep | Increment by which the step size changes from iteration to iteration |
| MinStep | Minimum value of step size |
| MaxStep | Maximum value of step size |
Also, when you use this adaptive algorithm object to create an equalizer object (via the lineareq or dfe function), the equalizer object has a StepSize property. The property value is a vector that lists the current step size for each weight in the equalizer.
For an example that uses this function, see Linked Properties of an Equalizer Object.
Referring to the schematics presented in Overview of Adaptive Equalizer Classes, define w as the vector of all current weights wi and define u as the vector of all inputs ui. Based on the current step size, μ, this adaptive algorithm first computes the quantity
μ0 = μ + (IncStep) Re(ggprev)
where g = ue*, gprev is the analogous expression from the previous iteration, and the * operator denotes the complex conjugate.
Then the new step size is given by
μ0, if it is between MinStep and MaxStep
MinStep, if μ0 < MinStep
MaxStep, if μ0 > MaxStep
The new set of weights is given by
(LeakageFactor) w + 2 μ g*
lms, signlms, normlms, rls, cma, lineareq, dfe, equalize, Equalizers
[1] Farhang-Boroujeny, B., Adaptive Filters: Theory and Applications, Chichester, England, Wiley, 1998.
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