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adaptfilt.fdaf

FIR adaptive filter that uses frequency-domain with bin step size normalization

Syntax

ha = adaptfilt.fdaf(l,step,leakage,delta,lambda,blocklen,
offset,...coeffs,states)

Description

ha = adaptfilt.fdaf(l,step,leakage,delta,lambda,blocklen,
offset,...coeffs,states)
constructs a frequency-domain FIR adaptive filter ha with bin step size normalization. If you omit all the input arguments you create a default object with l = 10 and step = 1.

For information on how to run data through your adaptive filter object, see the Adaptive Filter Syntaxes section of the reference page for filter.

Input Arguments

Entries in the following table describe the input arguments for adaptfilt.fdaf.

Input Argument

Description

l

Adaptive filter length (the number of coefficients or taps). l must be a positive integer; it defaults to 10 when you omit the argument.

step

Step size of the adaptive filter. This is a scalar and should lie in the range (0,1]. step defaults to 1.

leakage

Leakage parameter of the adaptive filter. If this parameter is set to a value between zero and one, you implement a leaky FDAF algorithm. leakage defaults to 1 — no leakage provided in the algorithm.

delta

Initial common value of all of the FFT input signal powers. Its initial value should be positive. delta defaults to 1.

lambda

Specifies the averaging factor used to compute the exponentially-windowed FFT input signal powers for the coefficient updates. lambda should lie in the range (0,1]. lambda defaults to 0.9.

blocklen

Block length for the coefficient updates. This must be a positive integer. For faster execution, (blocklen + l) should be a power of two. blocklen defaults to l.

offset

Offset for the normalization terms in the coefficient updates. Use this to avoid divide by zeros or by very small numbers when any of the FFT input signal powers become very small. offset defaults to zero.

coeffs

Initial time-domain coefficients of the adaptive filter. coeff should be a length l vector. The adaptive filter object uses these coefficients to compute the initial frequency-domain filter coefficients via an FFT computed after zero-padding the time-domain vector by the blocklen.

states

The adaptive filter states. states defaults to a zero vector that has length equal to l.

Properties

Since your adaptfilt.fdaf filter is an object, it has properties that define its behavior in operation. Note that many of the properties are also input arguments for creating adaptfilt.fdaf objects. To show you the properties that apply, this table lists and describes each property for the adaptfilt.fdaf filter object.

Name

Range

Description

Algorithm

None

Defines the adaptive filter algorithm the object uses during adaptation.

AvgFactor

(0, 1]

Specifies the averaging factor used to compute the exponentially-windowed FFT input signal powers for the coefficient updates. Same as the input argument lambda.

BlockLength

Any integer

Block length for the coefficient updates. This must be a positive integer. For faster execution, (blocklen + l) should be a power of two. blocklen defaults to l.

FFTCoefficients

 

Stores the discrete Fourier transform of the filter coefficients in coeffs.

FFTStates

 

States for the FFT operation.

FilterLength

Any positive integer

Reports the length of the filter, the number of coefficients or taps.

Leakage

 

Leakage parameter of the adaptive filter. if this parameter is set to a value between zero and one, you implement a leaky FDAF algorithm. leakage defaults to 1 — no leakage provided in the algorithm.

Offset

Any positive real value

Offset for the normalization terms in the coefficient updates. Use this to avoid dividing by zero or by very small numbers when any of the FFT input signal powers become very small. offset defaults to zero.

PersistentMemory

false or true

Determine whether the filter states get restored to their starting values for each filtering operation. The starting values are the values in place when you create the filter. PersistentMemory returns to zero any state that the filter changes during processing. States that the filter does not change are not affected. Defaults to false.

Power

 

A vector of 2*l elements, each initialized with the value delta from the input arguments. As you filter data, Power gets updated by the filter process.

StepSize

Any scalar from zero to one, inclusive

Specifies the step size taken between filter coefficient updates

Examples

Quadrature Phase Shift Keying (QPSK) adaptive equalization using 1024 iterations of a 32-coefficient FIR filter. After this example code, a figure demonstrates the equalization results.

D = 16;                         % Number of samples of delay
b  = exp(1j*pi/4)*[-0.7 1];      % Numerator coefficients of channel
a  = [1 -0.7];                  % Denominator coefficients of channel
ntr= 1024;                      % Number of iterations
s  = sign(randn(1,ntr+D))+1j*sign(randn(1,ntr+D));  %QPSK signal
n  = 0.1*(randn(1,ntr+D) + 1j*randn(1,ntr+D));        % Noise signal
r  = filter(b,a,s)+n;            % Received signal
x  = r(1+D:ntr+D);               % Input signal (received signal)
d  = s(1:ntr);                   % Desired signal (delayed QPSK signal)
del = 1;                         % Initial FFT input powers
mu  = 0.1;                       % Step size
lam = 0.9;                       % Averaging factor
ha = adaptfilt.fdaf(32,mu,1,del,lam);
[y,e] = filter(ha,x,d);
subplot(2,2,1); plot(1:ntr,real([d;y;e])); title('In-Phase Components');
legend('Desired','Output','Error');
xlabel('Time Index'); ylabel('signal value');
subplot(2,2,2); plot(1:ntr,imag([d;y;e])); title('Quadrature Components');
legend('Desired','Output','Error');
xlabel('Time Index'); ylabel('signal value');
subplot(2,2,3); plot(x(ntr-100:ntr),'.'); axis([-3 3 -3 3]);
title('Received Signal Scatter Plot'); axis('square');
xlabel('Real[x]'); ylabel('Imag[x]'); grid on;
subplot(2,2,4); plot(y(ntr-100:ntr),'.'); axis([-3 3 -3 3]);
title('Equalized Signal Scatter Plot'); axis('square');
xlabel('Real[y]'); ylabel('Imag[y]'); grid on;

References

Shynk, J.J.,"Frequency-Domain and Multirate Adaptive Filtering," IEEE® Signal Processing Magazine, vol. 9, no. 1, pp. 14-37, Jan. 1992

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