normlms

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

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

Syntax

alg = normlms(stepsize) alg = normlms(stepsize,bias)

Description

The normlms 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 = normlms(stepsize) constructs an adaptive algorithm object based on the normalized least mean square (LMS) algorithm with a step size of stepsize and a bias parameter of zero.

alg = normlms(stepsize,bias) sets the bias parameter of the normalized LMS algorithm. bias must be between 0 and 1. The algorithm uses the bias parameter to overcome difficulties when the algorithm's input signal is small.

Properties

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

Property	Description
`AlgType`	Fixed value, `'Normalized LMS'`
`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.
`Bias`	Normalized LMS bias parameter, a nonnegative real number

Examples

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Configuring Linear Equalizers

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 LMS Algorithm with Linear Equalizer

Configure lineareq and comm.LinearEqualizer objects with comparable settings. The LeakageFactor property has been removed from LMS algorithm. The comm.LinearEqualizer System object™ assumes that leakage factor is always 1.

eqOld = lineareq(5,lms(0.05),pskmod(0:3,4,pi/4))

eqOld =
  EqType: 'Linear Equalizer'
  AlgType: 'LMS'
  nWeights: 5
  nSampPerSym: 1
  RefTap: 1
  SigConst: [0.7071 + 0.7071i -0.7071 + 0.7071i -0.7071 - 0.7071i 0.7071 - 0.7071i]
  StepSize: 0.0500
  LeakageFactor: 1
  Weights: [0 0 0 0 0]
  WeightInputs: [0 0 0 0 0]
  ResetBeforeFiltering: 1
  NumSamplesProcessed: 0

eqNew = comm.LinearEqualizer('NumTaps',5,'Algorithm','LMS','StepSize',0.05, ...
    'Constellation',pskmod(0:3,4,pi/4),'ReferenceTap',1)

eqNew = comm.LinearEqualizer with properties:
  Algorithm: 'LMS'
  NumTaps: 5
  StepSize: 0.0500
  Constellation: [0.7071 + 0.7071i -0.7071 + 0.7071i -0.7071 - 0.7071i 0.7071 - 0.7071i]
  ReferenceTap: 1
  InputDelay: 0
  InputSamplesPerSymbol: 1
  TrainingFlagInputPort: false
  AdaptAfterTraining: true
  InitialWeightsSource: 'Auto'
  WeightUpdatePeriod: 1

Call the equalizers.

yOld = equalize(eqOld,r);
yNew = eqNew(r);

Use Linear Equalizers Considering Signal Delays

Configure lineareq and comm.LinearEqualizer objects with comparable settings. The transmit and receive filters result in a signal delay between the transit and receive signals. Account for this delay by setting the RefTap property of the lineareq to a value close to the delay value in samples. Additionally, nWeights must be set to a value greater than RefTap.

eqOld = lineareq(filterDelay*sps+4,lms(0.01),pskmod(0:3,4,pi/4),sps);
eqOld.RefTap = filterDelay*sps+1 % Adjust to synchronize with delayed signal

eqOld =
  EqType: 'Linear Equalizer'
  AlgType: 'LMS'
  nWeights: 16
  nSampPerSym: 2
  RefTap: 13
  SigConst: [0.7071 + 0.7071i -0.7071 + 0.7071i -0.7071 - 0.7071i 0.7071 - 0.7071i]
  StepSize: 0.0100
  LeakageFactor: 1
  Weights: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
  WeightInputs: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
  ResetBeforeFiltering: 1
  NumSamplesProcessed: 0

eqNew = comm.LinearEqualizer('NumTaps',16,'Algorithm','LMS','StepSize',0.01, ...
    'Constellation',pskmod(0:3,4,pi/4),'InputSamplesPerSymbol',sps, ...
    'ReferenceTap',filterDelay*sps+1,'InputDelay',0)

eqNew = comm.LinearEqualizer with properties:
  Algorithm: 'LMS'
  NumTaps: 16
  StepSize: 0.0100
  Constellation: [0.7071 + 0.7071i -0.7071 + 0.7071i -0.7071 - 0.7071i 0.7071 - 0.7071i]
  ReferenceTap: 13
  InputDelay: 0
  InputSamplesPerSymbol: 2
  TrainingFlagInputPort: false
  AdaptAfterTraining: true
  InitialWeightsSource: 'Auto'
  WeightUpdatePeriod: 1

Call the equalizers. When ResetBeforeFiltering is set to true, each call of the equalize object resets the equalizer. To get the equivalent behavior call reset after each call of the comm.LinearEqualizer object.

yOld1 = equalize(eqOld,r,x(1:100));
yOld2 = equalize(eqOld,r,x(1:100));

yNew1 = eqNew(r,x(1:100));
reset(eqNew)
yNew2 = eqNew(r,x(1:100));

In the comm.LinearEqualizer object, InputDelay is used to synchronize with the delayed signal. NumTaps and ReferenceTap are independent of delay value. We can reduce the number of taps by utilizing the InputDelay to synchronize instead of reference tap. Reducing the number of taps also reduces equalizer self noise.

eqNew = comm.LinearEqualizer('NumTaps',11,'Algorithm','LMS','StepSize',0.01, ...
    'Constellation',pskmod(0:3,4,pi/4),'InputSamplesPerSymbol',sps, ...
    'ReferenceTap',6,'InputDelay',filterDelay*sps)

eqNew = comm.LinearEqualizer with properties:
  Algorithm: 'LMS'
  NumTaps: 11
  StepSize: 0.0100
  Constellation: [0.7071 + 0.7071i -0.7071 + 0.7071i -0.7071 - 0.7071i 0.7071 - 0.7071i]
  ReferenceTap: 6
  InputDelay: 12
  InputSamplesPerSymbol: 2
  TrainingFlagInputPort: false
  AdaptAfterTraining: true
  InitialWeightsSource: 'Auto'
  WeightUpdatePeriod: 1

yNew1 = eqNew(r2,x(1:100));
reset(eqNew)
yNew2 = eqNew(r2,x(1:100));

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

$(LeakageFactor) w + \frac{(StepSize) u^{*} e}{u^{H} u + Bias}$

where the * operator denotes the complex conjugate and H denotes the Hermitian transpose.

Compatibility Considerations

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`normlms` will be removed

Warns starting in R2020a

normlms will be removed in a future release. Consider using comm.LinearEqualizer or comm.DecisionFeedback instead with the adaptive algorithm set to LMS.
The comm.LinearEqualizer or comm.DecisionFeedback System objects do not have a leakage factor property. This is equivalent to setting LeakageFactor to 1 in the normlms function.
For examples comparing setup of comm.LinearEqualizer to lineareq, see Configuring Linear Equalizers.

References

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

Documentation

normlms

Syntax

Description

Properties

Examples

Configuring Linear Equalizers

Algorithms

Compatibility Considerations

`normlms` will be removed

References

See Also

Objects

Topics

Communications Toolbox Documentation

Support