(To be removed) Construct constant modulus algorithm (CMA) object
alg = cma(stepsize)
alg = cma(stepsize,leakagefactor)
cma 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.
After you use either
create a CMA equalizer object, you should initialize the equalizer object's
Weights property with a nonzero vector. Typically, CMA is
used with differential modulation; otherwise, the initial weights are very
important. A typical vector of initial weights has a 1 corresponding to the center
tap and 0s elsewhere.
alg = cma(stepsize) constructs an adaptive
algorithm object based on the constant modulus algorithm (CMA) with a step size of
alg = cma(stepsize,leakagefactor) sets the
leakage factor of the CMA.
leakagefactor must be 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.
The table below describes the properties of the CMA adaptive algorithm object. To learn how to view or change the values of an adaptive algorithm object, see Equalization.
|Fixed value, |
|CMA step size parameter, a nonnegative real number|
|CMA leakage factor, a real number between 0 and 1|
This example configures the recommended
comm.LinearEqualizer System object™ and the legacy
lineareq feature with comparable settings.
Initialize Variables and Supporting Objects
d = randi([0 3],1000,1); x = pskmod(d,4,pi/4); r = awgn(x,25); sps = 2; %samples per symbol for oversampled cases nTaps = 6; txFilter = comm.RaisedCosineTransmitFilter('FilterSpanInSymbols',nTaps, ... 'OutputSamplesPerSymbol',4); rxFilter = comm.RaisedCosineReceiveFilter('FilterSpanInSymbols',nTaps, ... 'InputSamplesPerSymbol',4,'DecimationFactor',2); x2 = txFilter(x); r2 = rxFilter(awgn(x2,25,0.5)); filterDelay = txFilter.FilterSpanInSymbols/2 + ... rxFilter.FilterSpanInSymbols/2; % Total filter delay in symbols
To compare the equalized output, plot the constellations using code such as:
% plot(yNew,'*') % hold on % plot(yOld,'o') % hold off; legend('New Eq','Old Eq'); grid on
Use CMA Algorithm with Linear Equalizer
comm.LinearEqualizer objects with comparable settings. The
LeakageFactor property has been removed from CMA
comm.LinearEqualizer System object™ assumes
that leakage factor is always 1.
eqOld = lineareq(5,cma(0.05),pskmod(0:3,4,pi/4))
eqOld = EqType: 'Linear Equalizer' AlgType: 'Constant Modulus' nWeights: 5 nSampPerSym: 1 SigConst: [0.7071 + 0.7071i -0.7071 + 0.7071i -0.7071 - 0.7071i 0.7071 - 0.7071i] StepSize: 0.0500 LeakageFactor: 1 Weights: [1 0 0 0 0] WeightInputs: [0 0 0 0 0] ResetBeforeFiltering: 1 NumSamplesProcessed: 0
eqNew = comm.LinearEqualizer('NumTaps',5,'Algorithm','CMA','StepSize',0.05, ... 'Constellation',pskmod(0:3,4,pi/4),'ReferenceTap',1)
eqNew = comm.LinearEqualizer with properties: Algorithm: 'CMA' NumTaps: 5 StepSize: 0.0500 Constellation: [0.7071 + 0.7071i -0.7071 + 0.7071i -0.7071 - 0.7071i 0.7071 - 0.7071i] ReferenceTap: 1 InputSamplesPerSymbol: 1 AdaptWeightsSource: 'Property' AdaptWeights: true InitialWeightsSource: 'Auto' WeightUpdatePeriod: 1
Call the equalizers.
yOld = equalize(eqOld,r); yNew = eqNew(r);
Referring to the schematics 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 + (
where the * operator denotes the complex conjugate.
cmawill be removed
Warns starting in R2020a
 Haykin, Simon, Adaptive Filter Theory, Third Ed., Upper Saddle River, NJ, Prentice-Hall, 1996.
 Johnson, Richard C., Jr., Philip Schniter, Thomas. J. Endres, et al., “Blind Equalization Using the Constant Modulus Criterion: A Review,” Proceedings of the IEEE, Vol. 86, October 1998, pp. 1927–1950.