Hi all, I am modelling sampling jitter in matlab using sinc interpolation. The input is a vector of 256 complex data(16QAM symbols). Have introduced jitter in sampling time as t=k/Fs+k*err where Fs is sampling frequency, k is the symbol index, t is the new sampling instant. I have assumed ideal sampling when err is 0. But, when err is non zero and the product k*err exceeds Ts/2 (i.e k*err>Ts/2) I found that the interpolated data is not quite accurate, when plotted the input symbols and the interplated symbols do not align. what might be the reason??? Is it due to inaccuracy of sinc interpolation and how could I overcome this problem. Thanks in advance.

# Sinc interpolation

"aamer" <raqeebhyd@yahoo.com> wrote in message news:ztmdnV0iqL5a2uLYnZ2dnUVZ_rqhnZ2d@giganews.com...> Hi all, > > I am modelling sampling jitter in matlab using sinc interpolation. The > input is a vector of 256 complex data(16QAM symbols). Have introduced > jitter in sampling time as > > t=k/Fs+k*err > > where Fs is sampling frequency, k is the symbol index, t is the new > sampling instant. I have assumed ideal sampling when err is 0. But, when > err is non zero and the product k*err exceeds Ts/2 (i.e k*err>Ts/2) I > found that the interpolated data is not quite accurate, when plotted the > input symbols and the interplated symbols do not align. what might be the > reason??? Is it due to inaccuracy of sinc interpolation and how could I > overcome this problem. > > Thanks in advance. > >First, it would be good to better define what you're doing. "Modeling sampling jitter using sinc interpolation" is pretty vague. Are you trying to model an analog system by using nearly perfect sinc interpolation and *then* jittering the sample times thereafter? What do you mean that the input symbols and the interpolated symbols do not align? What is Ts? etc. Fred

aamer wrote:> Hi all, > > I am modelling sampling jitter in matlab using sinc interpolation. The > input is a vector of 256 complex data(16QAM symbols). Have introduced > jitter in sampling time as > > t=k/Fs+k*err > > where Fs is sampling frequency, k is the symbol index, t is the new > sampling instant. I have assumed ideal sampling when err is 0. But, when > err is non zero and the product k*err exceeds Ts/2 (i.e k*err>Ts/2) I > found that the interpolated data is not quite accurate, when plotted the > input symbols and the interplated symbols do not align. what might be the > reason??? Is it due to inaccuracy of sinc interpolation and how could I > overcome this problem.How many samples are you using for each sinc interpolation? A sinc function has infinite extent and significant energy well away from the center lobe. Are you windowing your sinc? IMHO. YMMV. -- rhn A.T nicholson d.0.t C-o-M

aamer wrote:> Hi all, > > I am modelling sampling jitter in matlab using sinc interpolation. The > input is a vector of 256 complex data(16QAM symbols). Have introduced > jitter in sampling time as > > t=k/Fs+k*err > > where Fs is sampling frequency, k is the symbol index, t is the new > sampling instant. I have assumed ideal sampling when err is 0. But, when > err is non zero and the product k*err exceeds Ts/2 (i.e k*err>Ts/2) I > found that the interpolated data is not quite accurate, when plotted the > input symbols and the interplated symbols do not align. what might be the > reason??? Is it due to inaccuracy of sinc interpolation and how could I > overcome this problem. > > Thanks in advance. > >Aamer, What I guess you mean is that you are using bandlimited interpolation of this type: x(t) = sum_-inf^inf x[n] h_s(t-n/F_0) Where h_s is a sinc function and you are randomly perturbing t to simulate the effects of jitter in the ADC? The problem you are running in to, when the jitter (clock phase noise) gets too big, is that aliasing occurs when the sampling interval becomes too long to properly sample all the frequencies in the signal. The solution is to oversample your signal and adjust your simulation parameters to ensure that this does not have an effect on the performance estimate that your simulation is giving you. Are you using the entire sinc(), or are you windowing it or (hopefully not) just truncating it? Do you assume x[n] to be zero for n outside your samples? Is that a valid assumption? Regards Marc Brooker

> > >aamer wrote: >> Hi all, >> >> I am modelling sampling jitter in matlab using sinc interpolation. The >> input is a vector of 256 complex data(16QAM symbols). Have introduced >> jitter in sampling time as >> >> t=k/Fs+k*err >> >> where Fs is sampling frequency, k is the symbol index, t is the new >> sampling instant. I have assumed ideal sampling when err is 0. But,when>> err is non zero and the product k*err exceeds Ts/2 (i.e k*err>Ts/2) I >> found that the interpolated data is not quite accurate, when plottedthe>> input symbols and the interplated symbols do not align. what might bethe>> reason??? Is it due to inaccuracy of sinc interpolation and how couldI>> overcome this problem. >> >> Thanks in advance. >> >> > >Aamer, > >What I guess you mean is that you are using bandlimited interpolation of>this type: > >x(t) = sum_-inf^inf x[n] h_s(t-n/F_0) > >Where h_s is a sinc function and you are randomly perturbing t to >simulate the effects of jitter in the ADC? The problem you are running >in to, when the jitter (clock phase noise) gets too big, is that >aliasing occurs when the sampling interval becomes too long to properly >sample all the frequencies in the signal. > >The solution is to oversample your signal and adjust your simulation >parameters to ensure that this does not have an effect on the >performance estimate that your simulation is giving you. > >Are you using the entire sinc(), or are you windowing it or (hopefully >not) just truncating it? Do you assume x[n] to be zero for n outside >your samples? Is that a valid assumption? > >Regards > >Marc Brooker >Dear all, Thanks for the prompt messages. This is exactly what am I doing. t(k)=k/Fs+k*err n=1:256 y(k)=sum( x(n)* sinc((t(k)-n*Ts)/Ts); where k runs from 1 to 256 Fs=sampling frequency, Ts=sampling time. x(n) is input complex data. t is the sampling instant. y(n) the resultant complex data after sampling. when err is 0, which means no sampling error. I got x(n)=y(n). when err is non zero , the sampling time changes by factor k.....as t(1)=1/Fs+1*err.... t(2)=2/Fs+2*err....so on. the factor k*err increases in each step, which i have assumed as, the jitter in sampling which changes the sampling instant. for k*err< Ts/2 the interpolation has no error. when plotted interpolated data y(n) with line and actual data x(n) with circles....the circles lie exactly on the line. The problem am running into is, when k*err>Ts/2 , the circles(x(n)) do not lie on the line(y(n)).which means, interpolated data is not accurate, and needs to be corrected. why is it so??? and how could I solve this problem??? Thanks in advance Aamer

"aamer" <raqeebhyd@yahoo.com> wrote in message news:962dnRmm9qIOeOLYnZ2dnUVZ_vamnZ2d@giganews.com...> Dear all, > > Thanks for the prompt messages. This is exactly what am I doing. > > t(k)=k/Fs+k*err > n=1:256 > y(k)=sum( x(n)* sinc((t(k)-n*Ts)/Ts); > > where k runs from 1 to 256 Fs=sampling frequency, Ts=sampling time. x(n) > is input complex data. t is the sampling instant. y(n) the resultant > complex data after sampling. > > when err is 0, which means no sampling error. I got x(n)=y(n). > when err is non zero , the sampling time changes by factor k.....as > t(1)=1/Fs+1*err.... > t(2)=2/Fs+2*err....so on. > > the factor k*err increases in each step, which i have assumed as, the > jitter in sampling which changes the sampling instant. > > for k*err< Ts/2 the interpolation has no error. when plotted interpolated > data y(n) with line and actual data x(n) with circles....the circles lie > exactly on the line. > > The problem am running into is, when k*err>Ts/2 , the circles(x(n)) do not > lie on the line(y(n)).which means, interpolated data is not accurate, and > needs to be corrected. why is it so??? and how could I solve this > problem??? > > Thanks in advance > AamerAamer, I still don't quite understand what you're trying to accomplish. Reading the expressions, here is what I see is being done: I guess I will have to assume that Ts = 1/Fs since you haven't said so. So, if correct: t(k)=k/Fs+k*err = k*Ts + k*err Ooops! I don't think you want k*err here, I think you want err(k) instead for jitter. Then: y(k)=sum( x(n)* sinc((k*Ts + k*err - n*Ts)/Ts) k=() > ()? parentheses are messed up so: y(k)=sum{ x(n)* sinc(k*Ts + err(k) - n*Ts)/Ts) } 1) You get a set of equispaced samples x(n). 2) You interpolate those samples with a sinc if err(all k)=0. So far so good. This is equivalent to running the samples through a perfect lowpass filter (to the extent that the sincs are of infinite extent at least). However, when you jitter the time you are also jittering the position of the sincs. I really don't know if that's your objective or not. I wonder if your objective isn't to do this: First, I'm going to assume that k=G*n where G is some large integer so that there are G times the output samples compared to the number of input samples - giving some discrete times on which the jittered output samples can occur. y(k)=sum{x(n)*sinc(-n*Ts)} for all "n" evaluated at time k*Ts/G + err(k) That would simulate jitter of a sequence of samples, lowpassed back to continuous time and then resampled with jitter. If there are no great errors or just poor notation on my part here perhaps this will give you an idea. It would be good to do a couple of things if I may suggest: 1) Get k and n well defined so that one can tell better their relative values. 2) Draw a block diagram so that one can tell better what your objective really is. Here is the block diagram for what I was trying to do above: +-----------+ +-----------+ +-----------+ | | | | | | | Regular | | Sinc | | Sample on| ---->| Samples |--->|Interpolate -->| n*Ts+err(k)---> re-interpolate | on Ts | | on Ts/G | | (jitter) | with sincs? | | | fine grid | | | +-----------+ +-----------+ +-----------+ err(k) is an integer multiple of Ts/G and is much less than Ts. Fred

aamer wrote:> > > Dear all, > > Thanks for the prompt messages. This is exactly what am I doing. > > t(k)=k/Fs+k*err > n=1:256 > y(k)=sum( x(n)* sinc((t(k)-n*Ts)/Ts); > > where k runs from 1 to 256 Fs=sampling frequency, Ts=sampling time. x(n) > is input complex data. t is the sampling instant. y(n) the resultant > complex data after sampling. > > when err is 0, which means no sampling error. I got x(n)=y(n). > when err is non zero , the sampling time changes by factor k.....as > t(1)=1/Fs+1*err.... > t(2)=2/Fs+2*err....so on. > > the factor k*err increases in each step, which i have assumed as, the > jitter in sampling which changes the sampling instant. > > for k*err< Ts/2 the interpolation has no error. when plotted interpolated > data y(n) with line and actual data x(n) with circles....the circles lie > exactly on the line. > > The problem am running into is, when k*err>Ts/2 , the circles(x(n)) do not > lie on the line(y(n)).which means, interpolated data is not accurate, and > needs to be corrected. why is it so??? and how could I solve this > problem??? > > Thanks in advance > Aamer > >Aamer, The problem is caused by aliasing. If you look at the power spectrum of your y(k) and x(n) signals, you should see the problem fairly clearly. Also, I don't understand why you are using k*err. Clock phase noise generally does not behave in this way. Perhaps I am misunderstanding what you are saying, but I think T(n) = n/Fs + err Where err is a random variable with a PDF and spectrum matching that of the phase noise you are simulating. Cheers Marc