Design a DAC sinx/x Corrector

• Matlab
• Tutorials

This post provides a Matlab function that designs linear-phase FIR sinx/x correctors.  It includes a table of fixed-point sinx/x corrector coefficients for different DAC frequency ranges.

A sinx/x corrector is a digital (or analog) filter used to compensate for the sinx/x roll-off inherent in the digital to analog conversion process.  In DSP math, we treat the digital signal applied to the DAC is a sequence of impulses.  These are converted by the DAC into contiguous pulses of length T_s, where T_s is the DAC sample time.  The result is that the frequency response of the DAC is not flat; it is rather the Fourier transform of a pulse of length T_s [1, 2]:

$$ H(f)= T_s \frac{sin\left(\pi f T_s \right)}{\pi f T_s} $$

H(f) is called the sinx/x or sinc function.  The dB magnitude of H(f) is plotted at the top of Figure 1, were we have ignored the scale factor T_s.  The usable range of the DAC output spectrum is normally limited to frequencies less than f_s/2.  As shown in the middle plot of Figure 1, the sinc function rolls off about 3.9 dB over 0 to f_s/2.

This article is available in PDF format for easy printing.

Whether compensation of the sinc roll-off is used depends on the particular application.  If compensation is required, we need a function with response that is the inverse of the sinc, as shown in the bottom plot in Figure 1.  We’ll call this the goal response.  Note that the corrector response need only match the goal response up to the maximum frequency in the DAC spectrum.  It is helpful if the maximum frequency is not too close to f_s/2 -- this eases the requirements on the corrector and DAC reconstruction filter. The DAC reconstruction filter is an analog lowpass filter that attenuates the images of the sampled output of the DAC.

In a DSP system, the sinx/x corrector is typically placed just before the DAC input, as shown in Figure 2.

Figure 1.Top:  sinc function dB-magnitude response, f = 0 to 3f_s.

Middle:  sinc function dB-magnitude response, f = 0 to f_s/2.

Bottom:  Inverse sinc response, f = 0 to f_s/2.

Figure 2.  sinx/x corrector in a DSP system.

The sinx/x corrector Matlab function is provided in the Appendix.The function call is:

    b= sinc_corr(ntaps,fmax,fs)

ntaps = desired number of taps

fmax = maximum frequency of DAC output signal, Hz ( fmax < .499*f_s).

fs = DAC sample frequency, Hz

b = vector of sinx/x corrector coefficients

For best results, ntaps should be odd.

Example 1

Let’s design a corrector with ntaps = 5 and fmax = 0.3*f_s.The Matlab commands are:

    ntaps= 5;
    fmax= 0.3;
    fs= 1;
    b= sinc_corr(ntaps,fmax,fs)

The output is:

    b = 0.0104  -0.0765  1.1331  -0.0765  0.0104

The frequency response of these coefficients is plotted in the top of Figure 3, along with the goal response (see Appendix for the code that computes the responses).  The vertical red line is at f = fmax.  The overall response of the DAC plus sinx/x corrector is shown in the bottom of Figure 3.  The ripple of the overall response is less than +/- .015 dB.

Note that the gain of the sinx/x corrector is 1.164= 1.32 dB at f= fmax.  Thus, at the corrector input, the peak-to-peak signal level of a sinewave at fmax should be backed-off 1.32 dB from full-scale to avoid clipping.  A simple way to achieve this is to incorporate a gain of 1/1.164 = 0.859 (or less) into the corrector.  For example, incorporating a gain of ¾ would work.

Example 2

In this example, we keep ntaps = 5, but extend fmax to 0.375*f_s.  The resulting coefficients are:

    b = 0.0164  -0.0872  1.1451  -0.0872  0.0164

The frequency response is plotted in the top of Figure 4, along with the goal response.  The overall response of the DAC plus sinx/x corrector is shown in the bottom of Figure 4.  The ripple of the overall response is roughly +/- .05 dB.

The gain of the sinx/x corrector is   1.269= 2.07 dB at fmax.

Figure 3.  sinx/x corrector with fmax= 0.3*fs and ntaps= 5.

Top:  Goal response and sinx/x corrector response.

Bottom:  Overall response of corrector plus DAC.

Figure 4.  sinx/x corrector with fmax= 0.375*fs and ntaps= 5.

Top:  Goal response and sinx/x corrector response.

Bottom:  Overall response of corrector plus DAC.

Fixed-Point Implementations

The function sinc_corr generates floating point coefficients.  We can convert them to a decimal representation of fixed-point using:

    b_fix= round(b*k)/k;

where b are the floating-point coefficients and k is 2 raised to an integer power.  When quantized with k= 512, the coefficients of Example 1 become b_fix = [5 -39 580 -39 5]/512, and they produce the overall response shown in Figure 5.

Table 1 lists coefficients and other filter parameters for three fixed-point sinx/x correctors.  The coefficients’ maximum number of signed digits is listed for Canonic-Signed-Digit (CSD) implementations [3].

Figure 5.  sinx/x corrector with fmax= 0.3*fs and ntaps= 5.

Overall response of corrector plus DAC.

blue = floating-point coefficients. green= fixed-point coefficients with k= 512.

References

1.  Smith, Steven W., The Scientist and Engineer’s Guide to Digital Signal Processing, California Technical Publishing, 1997, Ch 3 (p 44 – 48)  http://www.dspguide.com/

2.  http://www.ee.cuhk.edu.hk/~wkma/engg2310/handouts/2-fourier.pdf

3. Robertson, Neil, “Canonic Signed Digit (CSD) Representation of Integers”, Feb, 2017  https://www.dsprelated.com/showarticle/1030.php

Appendix    Matlab Function sinc_corr

This is a Matlab function to find the coefficients of a linear-phase FIR sinx/x corrector.  It uses the Matlab firls function for least-squares FIR design.  Note: the number of taps (ntaps) should be odd for best results.

    % sinc_corr.m  7/20/18  Neil Robertson
    % function synthesizes FIR DAC sinx/x corrector
    % ntaps       number of FIR taps.  Should be odd.
    % fmax     Hz maximum frequency to be corrected.  Require fmax < .499*fs
    % fs       Hz  DAC sample frequency
    % b            vector of FIR coefficients        
    function b= sinc_corr(ntaps,fmax,fs);
    if fmax >= .499*fs
        error('fmax must be less than .499*fs')
    end
    %    
    npts= 8;                % number of freq points for goal function
    k= 0:npts-1;
    f= k*fmax/(npts-1);                          % Hz
    hsinc= sin(pi*f/fs)./(pi*f/fs + eps);        % sinx/x response
    hsinc(1)= 1;
    h_goal= 1./hsinc;                            % goal function
    %    
    % least-squares FIR design
    ff(1:2:2*npts-1)= 2*f/fs;                  % f/fnyquist  vector of freq pairs
    ff(2:2:2*npts)= 2*f/fs + .001;
    a(1:2:2*npts-1)= h_goal;                   % vector of amplitude goal pairs
    a(2:2:2*npts)= h_goal;
    %    
    b= firls(ntaps-1,ff,a);                    % corrector coeffs

.

Here is the Matlab code for generating the plots in Figures 3 or 4:

    b= sinc_corr(ntaps,fmax,fs);
    [hc,f]= freqz(b,1,256,fs);
    Hc= 20*log10(abs(hc));                      % dB sinc corrector freq response
    hsinc= sin(pi*f/fs)./(pi*f/fs + eps);
    hsinc(1)= 1;
    Hsinc= 20*log10(abs(hsinc));                % dB DAC freq response
    Htotal= Hc + Hsinc;                         % dB Overall freq response
    %    
    subplot(211),plot(f,-Hsinc,f,Hc,[fmax fmax],[-1 5],'r'),grid
    axis([0 fs/2 -1 5])
    xlabel('f/fs'),ylabel('dB')
    text(.41,3.5,'goal')
    subplot(212),plot(f,Htotal,[fmax fmax],[-1 1],'r'),grid
    axis([0 fs/2 -.1 .1])
    xlabel('f/fs'),ylabel('dB')

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