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dsphdl.CICInterpolator

Interpolate signal using CIC filter

Description

The dsphdl.CICInterpolator System object™ interpolates an input signal by using a cascaded integrator-comb (CIC) interpolation filter. CIC interpolation filters are a class of linear phase finite impulse response (FIR) filters consisting of a comb part and an integrator part. The CIC interpolation filter structure consists of N sections of cascaded comb filters, a rate change factor of R, and N sections of cascaded integrators. For more information about CIC interpolation filters, see Algorithms.

The System object supports these combinations of input and output data.

  • Scalar input and scalar output — Support for fixed and variable interpolation rates

  • Scalar input and vector output — Support for fixed interpolation rates only

  • Vector input and vector output — Support for fixed interpolation rates only

The System object provides an architecture suitable for HDL code generation and hardware deployment.

The System object supports real and complex fixed-point inputs.

To filter input data with a CIC interpolator filter, follow these steps:

  1. Create the dsphdl.CICInterpolator object and set its properties.

  2. Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?

Creation

Description

cicIntFilt = dsphdl.CICInterpolator creates a CIC interpolator filter System object, cicIntFilt, with default properties.

example

cicIntFilt = dsphdl.CICInterpolator(Name,Value) creates the filter with properties set using one or more name-value arguments. Enclose each property name in single quotes.

Properties

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Unless otherwise indicated, properties are nontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and the release function unlocks them.

If a property is tunable, you can change its value at any time.

For more information on changing property values, see System Design in MATLAB Using System Objects.

Specify whether the System object operates with a fixed or variable interpolation rate.

  • 'Property' — Use a fixed interpolation rate specified by the InterpolationFactor property.

  • 'Input port' — Use a variable interpolation rate specified by the R input argument.

Note

The System object does not support variable interpolation for these two combinations of input and output:

  • Scalar input and vector output

  • Vector input and vector output

Specify the interpolation factor as an integer from 1 to 2048. The range of available values depends on the type of input and output data. This value gives the rate at which the System object interpolates the input.

Input DataOutput DataInterpolationFactor Valid Values
ScalarScalarInteger from 1 to 2048
ScalarVectorInteger from 1 to 64
VectorVectorInteger from 1 to 64

Note

For vector inputs, select the interpolation factor rate and input vector length such that their multiplication value does not exceed 64.

Dependencies

To enable this property, set the InterpolationSource property to 'Property'.

Specify the upper bound of the range of valid values for the R input argument as an integer from 1 to 2048.

Note

The System object does not support variable interpolation for these two combinations of input and output:

  • Scalar input and vector output

  • Vector input and vector output

Dependencies

To enable this property, set the InterpolationSource property to 'Input port'.

Specify the differential delay of the comb part of the filter as either 1 or 2 cycles.

Specify the number of sections in either the comb part or the integrator part of the System object.

Specify the minimum number of cycles between the valid input samples as 1, factors of R, or multiples of R based on the type of input and output data, where R is the interpolation factor.

Input DataOutput DataMinimum Number of Cycles Between Valid Input Samples
ScalarScalargreater than or equal to R
ScalarVectorfactors less than R
VectorVector1

Dependencies

To enable this property, set the InterpolationSource property to 'Property'.

Set this property to true to compensate for the output gain of the filter.

The latency of the System object changes depending on the type of interpolation you specify, the number of sections, and the value of this property. For more information on the latency of the System object, see Latency.

Choose the data type of the filtered output data.

  • 'Full precision' — The output data type has a word length equal to the input word length plus gain bits.

  • 'Same word length as input' — The output data type has a word length equal to the input word length.

  • 'Minimum section word lengths' — The output data type uses the word length you specify in the OutputWordLength property.

Word length of the output, specified as an integer from 2 to 104.

Dependencies

To enable this property, set the OutputDataType property to 'Minimum section word lengths'.

When you set this property to true, the System object expects a reset input argument.

Usage

Description

[dataOut,validOut] = cicIntFilt(dataIn,validIn) filters and interpolates the input data using a fixed interpolation factor only when validIn is true.

[dataOut,validOut] = cicIntFilt(dataIn,validIn,R) filters the input data using the specified variable interpolation factor R. The InterpolationSource property must be set to 'Input port'.

[dataOut,validOut] = cicIntFilt(dataIn,validIn,reset) filters the input data when reset is false and clears filter internal states when reset is true. The System object expects the reset argument only when you set the ResetInputPort property to true.

[dataOut,validOut] = cicIntFilt(dataIn,validIn,R,reset) filters the input data when reset is false and clears filter internal states when reset is true. The System object expects the reset argument only when you set the ResetInputPort property to true. The InterpolationSource property must be set to 'Input port'.

Input Arguments

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Specify input data as a scalar or a column vector with a length from 1 to 64. The input data must be a signed integer or signed fixed point with a word length less than or equal to 32.

Data Types: int8 | int16 | int32 | fi
Complex Number Support: Yes

Control signal that indicates if the input data is valid. When validIn is 1 (true), the object captures the values from the dataIn argument. When validIn is 0 (false), the object ignores the values from the dataIn argument.

Data Types: logical

Use this argument to dynamically specify the variable interpolation rate during run time.

This value must have the data type fi(0,12,0) and must be an integer in the range from 1 to the MaxInterpolationFactor property value.

Dependencies

To enable this argument, set the InterpolationSource property to 'Input port'.

Data Types: fi(0,12,0)

Control signal that clears internal states. When reset is 1 (true), the object stops the current calculation and clears internal states. When the reset is 0 (false) and the input valid is 1 (true), the object captures data for processing.

For more reset considerations, see the Reset Signal section on the Hardware Control Signals page.

Dependencies

To enable this argument, set the ResetInputPort property to true.

Data Types: logical

Output Arguments

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CIC-interpolated output data, returned as a scalar or a column vector with a length from 1 to 64.

The OutputDataType property sets the data type of this argument.

Data Types: int8 | int16 | int32 | fi
Complex Number Support: Yes

Control signal that indicates if the output data is valid. When validOut is 1 (true), the object returns valid data from the dataOut argument. When validOut is 0 (false), values from the dataOut argument are not valid.

Data Types: logical

Control signal that indicates that the object is ready for new input data sample on the next cycle. When ready is 1 (true), you can specify the data and valid inputs for the next time step. When ready is 0 (false), the object ignores any input data in the next time step.

Data Types: logical

Object Functions

To use an object function, specify the System object as the first input argument. For example, to release system resources of a System object named obj, use this syntax:

release(obj)

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getLatencyLatency of CIC interpolation filter
stepRun System object algorithm
releaseRelease resources and allow changes to System object property values and input characteristics
resetReset internal states of System object

Examples

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This example shows how to use a dsphdl.CICInterpolator System object™ to filter and upsample data. This object supports scalar and vector inputs. In this example, two functions are provided to work with scalar and vector input data separately. You can generate HDL code from these functions.

Generate Frames of Random Input Samples

Set up workspace variables for the object to use. The object supports fixed and variable interpolation rates for scalar inputs and only a fixed interpolation rate for vector inputs. The example runs the HDLCICInterp_maxR8 function when you set the scalar variable to true and runs the HDLCICInterp_vec function when you set the scalar variable to false. For scalar inputs, choose a range of the input varRValue values and set the interpolation factor value R to the maximum expected interpolation factor. For vector inputs, the input data must be a column vector of size 1 to 64 and R must be an integer multiple of the input frame size.

R = 8;             % interpolation factor
M = 1;             % differential delay
N = 3;             % number of sections
scalar = true;     % true for scalar; false for vector
if scalar
    varRValue = [2, 4, 5, 6, 7, 8];
    vecSize = 1;
else
    varRValue = R; %#ok
    fac = (factor(R));
    vecSize = fac(randi(length(fac),1,1));
end

numFrames = length(varRValue);
dataSamples = cell(1,numFrames);
varRtemp = cell(1,numFrames);
framesize = zeros(1,numFrames);
refOutput = [];
WL = 0;            % Word length
FL = 0;            % Fraction length

Generate Reference Output from dsp.CICInterpolator System Object

Generate frames of random input samples and apply the samples to the dsp.CICInterpolator System object. Later in this example, you use the output generated by the System object as reference data for comparison. The System object does not support a variable interpolation rate, so you must create and release the object for each change in interpolation factor value.

totalsamples = 0;
for i = 1:numFrames
    framesize(i) = varRValue(i)*randi([5 20],1,1);
    dataSamples{i} = fi(randn(vecSize,framesize(i)),1,16,8);
    ref_cic = dsp.CICInterpolator('DifferentialDelay',M, ...
                                  'NumSections',N, ...
                                  'InterpolationFactor',varRValue(i));
    refOutput = [refOutput,ref_cic(dataSamples{i}(:)).']; %#ok
    release(ref_cic);
end

Run Function Containing dsphdl.CICInterpolator System Object

Set the properties of the System object to match the input data parameters and run the function for your input type. These functions operate on a stream of data samples rather than a frame. You can generate HDL code from these functions.

The example uses the HDLCICInterp_maxR8 function for a scalar input.

function [dataOut,validOut] = HDLCICInterp_maxR8(dataIn,validIn,R)
%HDLCICInterp_maxR8
% Performs CIC interpolation with an input interpolation factor up to 8. 
% sampleIn is a scalar fixed-point value.
% validIn is a logical scalar value.

  persistent cic8;
  if isempty(cic8)
    cic8 = dsphdl.CICInterpolator('InterpolationSource','Input port', ...
                                'MaxInterpolationFactor',8, ...
                                'DifferentialDelay',1, ...
                                'NumSections',3);
  end    
  [dataOut,validOut] = cic8(dataIn,validIn,R);
end

The example uses the HDLCICInterp_vec function for a vector input.

function [dataOut,validOut] = HDLCICInterp_vec(dataIn,validIn)
%HDLCICInterp_vec
% Performs CIC interpolation with an input vector. 
% sampleIn is a fixed-point vector.
% validIn is a logical scalar value.

  persistent cicVec;
  if isempty(cicVec)
    cicVec = dsphdl.CICInterpolator('InterpolationSource','Property', ...
                                    'InterpolationFactor',8, ...
                                    'DifferentialDelay',1, ...
                                    'NumSections',3);
  end    
  [dataOut,validOut] = cicVec(dataIn,validIn);
end

To flush the remaining data, run the object by inserting the required number of idle cycles after each frame using the latency variable. For more information, see the GainCorrection property.

Initialize the output to a size large enough to accommodate the output data. The final size is smaller than totalsamples due to interpolation.

if scalar
    latency = 3 + N + 9;
    dataOut = zeros(1,totalsamples*R+numFrames*latency);

else
    latency = 3 + (N*(vecSize*R))+ 3*N + 9; %#ok
    dataOut = zeros(vecSize*R,totalsamples+numFrames*latency);
end
validOut = zeros(1,size(dataOut,2));
idx=0;
for ij = 1:numFrames
    if scalar
        dataIn = upsample(dataSamples{ij},R);
        validIn = upsample(true(1,length(dataSamples{ij})),R);
        % scalar input with variable interpolation
        for ii = 1:length(validIn)
            idx = idx+1;
            [dataOut(:,idx),validOut(idx)] = HDLCICInterp_maxR8( ...
                dataIn(ii), ...
                validIn(ii), ...
                fi(varRValue(ij),0,12,0));
        end
        for ii = 1:latency
            idx = idx+1;
            [dataOut(:,idx),validOut(idx)] = HDLCICInterp_maxR8( ...
                fi(0,1,16,8), ...
                false, ...
                fi(varRValue(ij),0,12,0));
        end

    else
        % vector input with fixed interpolation
        for ii = 1:size(dataSamples{ij},2)  %#ok
            idx = idx+1;
            [dataOut(:,idx),validOut(idx)] = HDLCICInterp_vec( ...
                dataSamples{ij}(:,ii), ...
                true);
        end
        for ii = 1:latency
            idx = idx+1;
            [dataOut(:,idx),validOut(idx)] = HDLCICInterp_vec( ...
                fi(zeros(vecSize,1),1,16,8), ...
                false);
        end
    end
end

Compare Function Output with Reference Data

Compare the function results against the output from the dsp.CICInterpolator object.

cicOutput = dataOut(:,validOut==1);
refOutput = refOutput(:);

fprintf('\nCIC Interpolator\n');
difference = (abs(cicOutput(:)-refOutput(1:numel(cicOutput)))>0);
fprintf(['\nTotal number of samples differed between Behavioral ' ...
    'and HDL simulation: %d \n'],sum(difference));
CIC Interpolator

Total number of samples differed between Behavioral and HDL simulation: 2773 

The latency of the dsphdl.CICInterpolator System object™ varies depending on how many integrator and comb sections your filter has, the input vector size, and whether you enable gain correction. Use the getLatency function to find the latency of a particular filter configuration. The latency is the number of cycles between the first valid input and the first valid output, assuming the input is continuously valid.

Create a dsphdl.CICInterpolator System object and request the latency. The default System object filter has two integrator and comb sections, and the gain correction is disabled.

hdlcic = dsphdl.CICInterpolator
hdlcic = 
  dsphdl.CICInterpolator with properties:

    InterpolationSource: 'Property'
    InterpolationFactor: 2
      DifferentialDelay: 1
            NumSections: 2
              NumCycles: 1
         GainCorrection: false

  Show all properties

L_def = getLatency(hdlcic)
L_def = 13

Modify the filter object so it has three integrator and comb sections. Check the resulting change in latency.

hdlcic.NumSections = 3;
L_3sec = getLatency(hdlcic)
L_3sec = 18

Enable the gain correction on the filter object with vector input size 2. Check the resulting change in latency.

hdlcic.GainCorrection = true;
vecSize = 2;
L_wgain = getLatency(hdlcic,vecSize)
L_wgain = 33

Algorithms

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References

[1] Hogenauer, E. “An Economical Class of Digital Filters for Decimation and Interpolation.” IEEE Transactions on Acoustics, Speech, and Signal Processing 29, no. 2 (April 1981): 155–62. https://doi.org/10.1109/TASSP.1981.1163535.

Extended Capabilities

Version History

Introduced in R2022a