Hello, It feels like this will be a standard DSP result that I should be able to dig up, but I'm not sure where to look, so here goes! If we have a continuous-time function, h(t) (assuming real and "sufficiently well-behaved" for now), then define the autocorrelation as: r(t) = int { h(tau) h(tau - t) d tau } Let's also define the discrete-time sampled sequence, h[n] = h(nT), where T is the sampling period, and the discrete-time autocorrelation: r[n] = sum { h[k] h[k-n] } My question is, what are the conditions under which r[n] = r(nT) for all n? It's trivially true when h(t) is a Nyquist filter. I believe it's also true if h(t) is bandlimited appropriately for the sampling rate. However, is there a more general condition? Note: this is not a "homework" question! For context, I'm considering the whitened-matched-filter (WMF) receiver model in e.g. "Digital Communications" by Proakis, and trying to understand when (if ever) the WMF and a ZF equaliser cancel. Regards, -- Oli

# When does sampled autocorrelation = autocorrelation of sampled sequence?

Started by ●February 28, 2009

Reply by ●February 28, 20092009-02-28

Oli Charlesworth <catch@olifilth.co.uk> wrote:>If we have a continuous-time function, h(t) (assuming real and >"sufficiently well-behaved" for now), then define the autocorrelation as: > >r(t) = int { h(tau) h(tau - t) d tau } > >Let's also define the discrete-time sampled sequence, h[n] = h(nT), >where T is the sampling period, and the discrete-time autocorrelation: > >r[n] = sum { h[k] h[k-n] } > >My question is, what are the conditions under which r[n] = r(nT) for all >n? It's trivially true when h(t) is a Nyquist filter. I believe it's >also true if h(t) is bandlimited appropriately for the sampling rate. >However, is there a more general condition?I don't think so. A loose argument would be that if the signal does not satisfy the Nyquist criterion, after sampling higher-frequency components will have aliased into the baseband and the spectrum will change, hence the autocorrelation function will change. But there could be exceptions to this argument if the signal is sufficiently exotic. Steve

Reply by ●February 28, 20092009-02-28

Steve Pope wrote:> Oli Charlesworth <catch@olifilth.co.uk> wrote: > >> If we have a continuous-time function, h(t) (assuming real and >> "sufficiently well-behaved" for now), then define the autocorrelation as: >> >> r(t) = int { h(tau) h(tau - t) d tau } >> >> Let's also define the discrete-time sampled sequence, h[n] = h(nT), >> where T is the sampling period, and the discrete-time autocorrelation: >> >> r[n] = sum { h[k] h[k-n] } >> >> My question is, what are the conditions under which r[n] = r(nT) for all >> n? It's trivially true when h(t) is a Nyquist filter. I believe it's >> also true if h(t) is bandlimited appropriately for the sampling rate. >> However, is there a more general condition? > > I don't think so. A loose argument would be that if the > signal does not satisfy the Nyquist criterion, after sampling > higher-frequency components will have aliased into the baseband > and the spectrum will change, hence the autocorrelation function > will change. But there could be exceptions to this argument if > the signal is sufficiently exotic.I think that a Nyquist filter (e.g. raised-cosine) is an exception. In general, a Nyquist filter doesn't have to be bandlimited, it just has to satisfy the ISI criterion (i.e. h(nT) = 0, n =/= 0). -- Oli

Reply by ●February 28, 20092009-02-28

>Hello, > >It feels like this will be a standard DSP result that I should be able >to dig up, but I'm not sure where to look, so here goes! > >If we have a continuous-time function, h(t) (assuming real and >"sufficiently well-behaved" for now), then define the autocorrelationas:> >r(t) = int { h(tau) h(tau - t) d tau } > >Let's also define the discrete-time sampled sequence, h[n] = h(nT), >where T is the sampling period, and the discrete-time autocorrelation: > >r[n] = sum { h[k] h[k-n] } > >My question is, what are the conditions under which r[n] = r(nT) for all>n? It's trivially true when h(t) is a Nyquist filter. I believe it's >also true if h(t) is bandlimited appropriately for the sampling rate. >However, is there a more general condition?Oli, I believe the condition is that the signal be sampled at twice the Nyquist rate. I'll let you have the fun of justifying this yourself, though. :-) Emre

Reply by ●March 1, 20092009-03-01

On 1 Mrz., 04:56, "emre" <egu...@ece.neu.edu> wrote:> >Hello, > > >It feels like this will be a standard DSP result that I should be able > >to dig up, but I'm not sure where to look, so here goes! > > >If we have a continuous-time function, h(t) (assuming real and > >"sufficiently well-behaved" for now), then define the autocorrelation > as: > > >r(t) = int { h(tau) h(tau - t) d tau } > > >Let's also define the discrete-time sampled sequence, h[n] = h(nT), > >where T is the sampling period, and the discrete-time autocorrelation: > > >r[n] = sum { h[k] h[k-n] } > > >My question is, what are the conditions under which r[n] = r(nT) for all > >n? �It's trivially true when h(t) is a Nyquist filter. �I believe it's > >also true if h(t) is bandlimited appropriately for the sampling rate. > >However, is there a more general condition? > > Oli, > > I believe the condition is that the signal be sampled at twice the Nyquist > rate. �I'll let you have the fun of justifying this yourself, though. �:-)The condition you suggest is sufficient but not necessary. To see this, try the following DSP Riddle: Find a function h(t) defined on continuous-time that is not bandlimited but such that Oli's property holds. :-) Regards, Andor

Reply by ●March 1, 20092009-03-01

Andor wrote:> On 1 Mrz., 04:56, "emre" <egu...@ece.neu.edu> wrote: >>> Hello, >>> It feels like this will be a standard DSP result that I should be able >>> to dig up, but I'm not sure where to look, so here goes! >>> If we have a continuous-time function, h(t) (assuming real and >>> "sufficiently well-behaved" for now), then define the autocorrelation >> as: >> >>> r(t) = int { h(tau) h(tau - t) d tau } >>> Let's also define the discrete-time sampled sequence, h[n] = h(nT), >>> where T is the sampling period, and the discrete-time autocorrelation: >>> r[n] = sum { h[k] h[k-n] } >>> My question is, what are the conditions under which r[n] = r(nT) for all >>> n? It's trivially true when h(t) is a Nyquist filter. I believe it's >>> also true if h(t) is bandlimited appropriately for the sampling rate. >>> However, is there a more general condition? >> Oli, >> >> I believe the condition is that the signal be sampled at twice the Nyquist >> rate. I'll let you have the fun of justifying this yourself, though. :-) > > The condition you suggest is sufficient but not necessary. To see > this, try the following DSP Riddle: > > Find a function h(t) defined on continuous-time that is not > bandlimited but such that Oli's property holds.I believe h(t) = rect(t/T) is such a function! So, is it possible to find a *necessary* condition? -- Oli

Reply by ●March 1, 20092009-03-01

>> Oli, >> >> I believe the condition is that the signal be sampled at twice theNyquis=>t >> rate. =A0I'll let you have the fun of justifying this yourself, though.=>=A0:-) > >The condition you suggest is sufficient but not necessary. To see >this, try the following DSP Riddle: > >Find a function h(t) defined on continuous-time that is not >bandlimited but such that Oli's property holds. > >:-) > >Regards, >AndorThanks, Andor. Indeed, I meant "the sufficient condition". However, I should point that this is what you need to satisfy for unknown deterministic signals. I think in that case it becomes "the necessary and coefficient condition". Please correct me if I am wrong. Emre

Reply by ●March 2, 20092009-03-02

>Thanks, Andor. Indeed, I meant "the sufficient condition". However, I >should point that this is what you need to satisfy for unknown >deterministic signals. I think in that case it becomes "the necessaryand>coefficient condition". Please correct me if I am wrong. > >EmreI meant necessary and *sufficient* condition here, not coefficient condition. :-) Recapping my conjecture: For unknown (deterministic) bandlimited signals, the necessary and sufficient condition for Oli's property to hold is that the signal be sampled at twice the Nyquist rate. Emre

Reply by ●March 2, 20092009-03-02

> Recapping my conjecture: � > � � For unknown (deterministic) bandlimited signals, the necessary and > sufficient condition for Oli's property to hold is that the signal be > sampled at twice the Nyquist rate.I dont think that is true. If the signal is periodic for instance, we can devise a scheme that samples *below* the Nyquist based on the signal's periodicity.

Reply by ●March 2, 20092009-03-02

> Recapping my conjecture: > For unknown (deterministic) bandlimited signals, the necessary and > sufficient condition for Oli's property to hold is that the signal be > sampled at twice the Nyquist rate.I dont think thats 100% true. If the signal is narrowband and periodic for instance, we can devise a scheme that samples *below* the Nyquist based on the signal's periodicity.