# msepred

Predicted mean squared error for LMS adaptive filter

## Syntax

## Description

## Examples

### Predict Mean Squared Error for LMS Filter

The mean squared error (MSE) measures the average of the squares of the errors between the desired signal and the primary signal input to the adaptive filter. Reducing this error converges the primary input to the desired signal. Determine the predicted value of MSE and the simulated value of MSE at each time instant using the `msepred`

and `msesim`

functions. Compare these MSE values with each other and with respect to the minimum MSE and steady-state MSE values. In addition, compute the sum of the squares of the coefficient errors given by the trace of the coefficient covariance matrix.

**Note**: If you are using R2016a or an earlier release, replace each call to the object with the equivalent step syntax. For example, `obj(x)`

becomes `step(obj,x)`

.

**Initialization**

Create a `dsp.FIRFilter`

System object™ that represents the unknown system. Pass the signal, `x`

*,* to the FIR filter. The output of the unknown system is the desired signal, `d`

, which is the sum of the output of the unknown system (FIR filter) and an additive noise signal, `n`

.

num = fir1(31,0.5); fir = dsp.FIRFilter('Numerator',num); iir = dsp.IIRFilter('Numerator',sqrt(0.75),... 'Denominator',[1 -0.5]); x = iir(sign(randn(2000,25))); n = 0.1*randn(size(x)); d = fir(x) + n;

**LMS Filter**

Create a `dsp.LMSFilter`

System object to create a filter that adapts to output the desired signal. Set the length of the adaptive filter to 32 taps, step size to 0.008, and the decimation factor for analysis and simulation to 5. The variable `simmse`

represents the simulated MSE between the output of the unknown system, `d`

, and the output of the adaptive filter. The variable `mse`

gives the corresponding predicted value.

l = 32; mu = 0.008; m = 5; lms = dsp.LMSFilter('Length',l,'StepSize',mu); [mmse,emse,meanW,mse,traceK] = msepred(lms,x,d,m); [simmse,meanWsim,Wsim,traceKsim] = msesim(lms,x,d,m);

**Plot the MSE Results**

Compare the values of simulated MSE, predicted MSE, minimum MSE, and the final MSE. The final MSE value is given by the sum of minimum MSE and excess MSE.

nn = m:m:size(x,1); semilogy(nn,simmse,[0 size(x,1)],[(emse+mmse)... (emse+mmse)],nn,mse,[0 size(x,1)],[mmse mmse]) title('Mean Squared Error Performance') axis([0 size(x,1) 0.001 10]) legend('MSE (Sim.)','Final MSE','MSE','Min. MSE') xlabel('Time Index') ylabel('Squared Error Value')

The predicted MSE follows the same trajectory as the simulated MSE. Both these trajectories converge with the steady-state (final) MSE.

**Plot the Coefficient Trajectories**

`meanWsim`

is the mean value of the simulated coefficients given by `msesim`

. `meanW`

is the mean value of the predicted coefficients given by `msepred`

.

Compare the simulated and predicted mean values of LMS filter coefficients 12,13,14, and 15.

plot(nn,meanWsim(:,12),'b',nn,meanW(:,12),'r',nn,... meanWsim(:,13:15),'b',nn,meanW(:,13:15),'r') PlotTitle ={'Average Coefficient Trajectories for';... 'W(12), W(13), W(14), and W(15)'}

`PlotTitle = `*2x1 cell*
{'Average Coefficient Trajectories for'}
{'W(12), W(13), W(14), and W(15)' }

title(PlotTitle) legend('Simulation','Theory') xlabel('Time Index') ylabel('Coefficient Value')

In steady state, both the trajectories converge.

**Sum of Squared Coefficient Errors**

Compare the sum of the squared coefficient errors given by `msepred`

and `msesim`

. These values are given by the trace of the coefficient covariance matrix.

semilogy(nn,traceKsim,nn,traceK,'r') title('Sum-of-Squared Coefficient Errors') axis([0 size(x,1) 0.0001 1]) legend('Simulation','Theory') xlabel('Time Index') ylabel('Squared Error Value')

## Input Arguments

`lmsFilt`

— LMS adaptive filter System object

`dsp.LMSFilter`

`dsp.LMSFilter`

LMS adaptive filter, specified as a `dsp.LMSFilter`

System object.

`x`

— Input signal

scalar | column vector | matrix

Input signal, specified as a scalar, column vector, or matrix. Columns of
the matrix `x`

contain individual input signal sequences.
The input, `x`

, and the desired signal,
`d`

, must have the same size, data type, and
complexity.

**Data Types: **`single`

| `double`

`d`

— Desired signal

scalar | column vector | matrix

Desired response signal, specified as a scalar, column vector, or matrix.
Columns of the matrix `d`

contain individual desired
signal sequences. The input, `x`

, and the desired signal,
`d`

, must have the same size, data type, and
complexity.

**Data Types: **`single`

| `double`

`m`

— Decimation factor

`1`

(default) | positive scalar

Decimation factor, specified as a positive scalar. Every
`m`

th predicted value of the 3rd, 4th, and 5th
predicted sequences output is saved into the corresponding output arguments,
`meanw`

, `mse`

, and
`tracek`

. If `m`

equals 1, every
value of these sequences is saved.

`m`

must be a factor of the input frame size.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

| `logical`

## Output Arguments

`mmse`

— Minimum mean squared error

scalar

Minimum mean squared error (mmse), returned as a scalar. This parameter is
estimated using a Wiener filter. The Wiener filter minimizes the mean
squared error between the desired signal and the input signal filtered by
the Wiener filter. A large value of the mean squared error indicates that
the adaptive filter cannot accurately track the desired signal. The minimal
value of the mean squared error ensures that the adaptive filter is optimal.
The minimum mean squared error between a particular frame of the desired
signal and the filtered signal is computed as the variance between the two
frames of signals. The `msepred`

function outputs the
average of the mmse values for all the frames. For more details on how this
parameter is calculated, see Algorithms.

**Data Types: **`single`

| `double`

`emse`

— Steady state excess mean squared error

scalar

Excess mean squared error, returned as a scalar. This error is the difference between the mean squared error introduced by adaptive filters and the minimum mean squared error produced by corresponding Wiener filter. For details on how this parameter is calculated, see Algorithms.

**Data Types: **`single`

| `double`

`meanw`

— Sequence of coefficient vector means

matrix

Sequence of coefficient vector means of the adaptive filter at each time
instant, returned as a matrix. The columns of this matrix contain
predictions of the mean values of the LMS adaptive filter coefficients at
each time instant. If the decimation factor, `m`

equals
1, the dimensions of `meanw`

is
*M*-by-*N*. *M* is
the frame size (number of rows) of the input signal, `x`

.
*N* is the length of the FIR filter weights vector,
specified by the `Length`

property of the
`lmsFilt`

System object. If `m`

> 1, the dimensions of
`meanw`

is
*M*/`m`

-by-*N*.

For details on how this parameter is calculated, see Algorithms.

**Data Types: **`double`

`mse`

— Sequence of mean squared errors

column vector

Predictions of the mean squared error of the LMS adaptive filter at each
time instant, returned as a column vector. If the decimation factor,
`m`

equals 1, the length of `mse`

equals the frame size (number of rows) of the input signal,
*M*. If `m`

> 1, the length of
`mse`

equals
*M*/`m`

.

For details on how this parameter is calculated, see Algorithms.

**Data Types: **`double`

`tracek`

— Sequence of total coefficient error powers

column vector

Predictions of the total coefficient error power of the LMS adaptive
filter at each time instant, returned as a column vector. If the decimation
factor, `m`

equals 1, the length of
`tracek`

equals the frame size (number of rows) of
the input signal, given by size(`x`

,1). If
`m`

> 1, the length of `tracek`

equals the ratio of input frame size and the decimation factor,
`m`

.

For details on how this parameter is calculated, see Algorithms.

**Data Types: **`double`

## Algorithms

### Minimum Mean Squared Error (mmse)

The `msepred`

function computes the minimum mean-squared error
(mmse) using the following equation:

$$mmse=\frac{{\displaystyle \sum _{i=1}^{N}\mathrm{var}({d}_{i}}-W({x}_{i}))}{N}$$

where,

*N*–– Number of frames in the input signal,`x`

.*d*_{i}––*i*th frame (column) of the desired signal.*x*_{i}––*i*th frame (column) of the input signal.*W(x*–– Output of the Wiener filter._{i})`var`

–– Variance

### Excess Mean Squared Error (emse)

The `msepred`

function computes the steady-state excess mean
squared error using the following equations:

$$\begin{array}{l}emse=K\lambda ,\\ K=\frac{B}{I(L)-A},\\ B={\mu}^{2}.mmse.\lambda \text{'},\\ A=I(L)-2\mu Lam+{\mu}^{2}(La{m}^{2}(kurt+2)+\lambda \lambda \text{'}).\end{array}$$

where,

*K*–– Final values of transformed coefficient variances.*λ*–– Column vector containing the eigenvalues of the input autocorrelation matrix.*λ'*–– Transpose of*λ*.*B*–– MSE analysis driving term.*L*–– Length of the FIR adaptive filter, given by`lmsFilt.Length`

.*I(L)*––*L*-by-*L*identity matrix.*A*–– MSE analysis transition matrix.*μ*–– Step size given by`lmsFilt.StepSize`

.*Lam*–– Diagonal matrix containing the eigenvalues.*kurt*–– Average kurtosis value of eigenvector-filtered signals.

### Coefficient Vector Means (meanw)

The `msepred`

function computes each element of the sequence of
coefficient vector means using the following equations:

$$\begin{array}{l}meanw=meanw.T.D,\\ T=I(L)-\mu R,\\ D=\mu P.\end{array}$$

where,

*meanw*–– The initial value of*meanw*is given by`lmsFilt.InitialConditions`

.*T*–– Transition matrix for mean coefficient analysis.*I(L)*––*L*-by-*L*identity matrix.*μ*–– Step size given by`lmsFilt.StepSize`

.*R*–– Input autocorrelation matrix of size*L*-by-*L*.*D*–– Driving term for mean coefficient analysis.*P*–– Cross correlation vector of size 1-by-*L*.*kurt*–– Average kurtosis value of eigenvector-filtered signals.

### Mean Squared Errors (mse)

The `msepred`

function computes each element of the sequence of
the mean squared errors using the following equations:

$$\begin{array}{l}mse=mmse+dk.\lambda ,\\ dk=dk\circ diagA+mse.D,\\ diagA=(1-2\mu \lambda +{\mu}^{2}(kurt+2){\lambda}^{\circ 2})\text{'},\\ D={\mu}^{2}\lambda \text{'}.\end{array}$$

where,

*mmse*–– Minimum mean squared error.*dk*–– Diagonal entries of coefficient covariance matrix. The initial value of*dk*is given by $$dk={((meanw-Wopt)Q)}^{\circ 2}$$.*meanw*–– Coefficient vector means given by`lmsFilt.InitialConditions`

.*Wopt*–– Optimal Wiener filter coefficients.*Q*––*L*-by-*L*matrix whose columns are the eigenvectors of the input autocorrelation matrix,*R*, so that*R**Q*=*Q**Lam*.*Lam*is the diagonal matrix containing the corresponding eigen values.*diagA*–– Portion of MSE analysis transition matrix.*dk*◦*diagA*–– Hadamard or entrywise product of*dk*and*diagA*.*λ*–– Column vector containing the eigenvalues of the input autocorrelation matrix,*R*.*μ*–– Step size given by`lmsFilt.StepSize`

.*kurt*–– Average kurtosis value of eigenvector filtered signals.*λ*^{◦2}–– Hadamard or entrywise power of the column vector containing the eigenvalues.*D*–– Driving term for MSE analysis.

### Total Coefficient Error Powers (tracek)

The `msepred`

function computes each element of the sequence of
the total coefficient error powers. These values are given by the trace of the
coefficient covariance matrix. The diagonal entries of the coefficient covariance
matrix are given by *dk* in the following equations:

$$\begin{array}{l}mse=mmse+dk.\lambda ,\\ dk=dk\circ diagA+mse.D,\\ diagA=(1-2\mu \lambda +{\mu}^{2}(kurt+2){\lambda}^{\circ 2})\text{'},\\ D={\mu}^{2}\lambda \text{'}.\end{array}$$

The trace of the coefficient covariance matrix is given by the following equation:

$$tracek=sum(dk).$$

## References

[1] Hayes, M.H. *Statistical Digital Signal Processing and
Modeling.* New York: John Wiley & Sons, 1996.

## See Also

### Functions

### Objects

**Introduced in R2012a**

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