Carlos Moreno wrote: ...> > My point is that, the white noise as I defined it, as a > mathematical abstraction, has properties that can be > rigorously defined according to the laws of mathematics. > > The way I see it, the power of the white noise that I > described *can not* be infinite. In fact, calculating > the variance is a trivial exercise: E{x^2} = integral > from -1 to 1 of (1/2) x^2 dx. That gives you the average > power (assuming a normalized 1-ohm resistor, etc. etc.), > which is definitely not infinite. > > Yes, white noise is a mathematical abstraction; the > disagreement is on the mathematical properties of this > abstraction.Why does it surprise you that the calculated properties of impossible abstractions are themselves not possible? White noise as you defined is is characterized as volts per root Hz. (Or watts per Hz.) It is a useful abstraction where the actual bandwidth is limited, even though it implies infinite power for infinite bandwidth. That doesn't bother me because neither infinity can be realized.> You mention also the delta. As an example, if someone > tells you that the value of the delta at t = 0 is 1?? > You will undoubtedly tell them that they're wrong!!You would do well to doubt. I might, however, ask 1 what?> It doesn't matter that a delta can not exist in practice; > the mathematical properties that define a delta are > specific and rigurous.Well, it behooves us to be precise. What to you mean by "value" above? Certainly nor width or height. Some would say "area", others "strength".> Same thing (IMO) about white noise. We talk about > *Gaussian* white noise with zero mean and variance > sigma^2. Well, if the variance is sigma^2, then the > average power is not infinite!Are you sure that sigma^2 represents power here? There is no limit to the magnitude of true Gaussian noise, even though the frequency of a particular amplitude's being exceeded decreases rapidly with amplitude. If there is just one infinite-amplitude event in a year, the average power is rather large, to say the least. It doesn't do to say that impossible conditions are OK because that are merely mathematical constructs, and then to reject the conclusions mathematically constructed from them. Jerry -- Engineering is the art of making what you want from things you can get. �����������������������������������������������������������������������

# Rigorous definition of the Spectral Density of a random signal?

Started by ●October 25, 2003

Reply by ●October 31, 20032003-10-31

Reply by ●October 31, 20032003-10-31

Carlos Moreno <moreno_at_mochima_dot_com@x.xxx> wrote in message news:<iwkob.13706$RG1.441838@wagner.videotron.net>...> Same thing (IMO) about white noise. We talk about > *Gaussian* white noise with zero mean and variance > sigma^2. Well, if the variance is sigma^2, then the > average power is not infinite!Carlos: **You** may talk about Gaussian white noise with zero mean and variance sigma^2, but in the context of continuous-time signals, this does not make sense. In most systems, the thermal noise at the output of a filter can be modeled as a Gaussian random process whose PSD (that word you hate!) is proportional to |H(w)|^2. We can derive this result using the standard general theory of second-order (i.e. finite power) random processes by **pretending** that the filter input is a Gaussian random process with PSD = constant for all w. This hypothetical process is called Gaussian white noise, and generalized Fourier theory (which allows the notion of Fourier transforms of **power** signals) allows us to pretend that the input process has autocorrelation that is a delta function. But, it does not make sense to talk about the variance of white noise: it is a meaningless concept. For continuous-time signals, the only meaning to be ascribed to white noise -- Gaussian or not -- is that it is a hypothetical process defined by the property that when it is passed through a filter with transfer function H(w), it produces a random process with PSD that is proportional to |H(w)|^2. We cannot talk of its properties except in terms of what we can observe, and any observation necessarily implies some filtering. For example, we cannot talk of the properties of the random *variable* X(5), say, and claim that it has zero mean or is Gaussian with some specified variance (possibly infinite), because we cannot sample the process at t = 5 instantaneously, even though we often pretend in DSP circles that we can and do get an instantaneous sample. This pretence works because the fact that an actual sampler switch will stay closed for a small nonzero period of time does not matter verymuch in typical DSP applications: the signal being sampled has typically been filtered anyway. But, to apply this notion of instantaneous sampling to a white noise process just leads to the same sort of dilemmas that you are stuck with. For **discrete-time** random processes, the concept of Gaussian white noise is a sequence of independent zero-mean Gaussian random variables with fixed finite variance sigma^2. The power here is indeed finite (and equal to sigma^2), but bear in mind if we think of this discrete-time process as having been obtained from a continuous-time process via sampling, then the continuous-time white noise has, implicitly or explicitly, been filtered before sampling and thus has finite variance. Now, you can say that you are very mathematical and rigorous and don't care a whit about practical notions, and have given an explicit construction of a continuous-time random process with finite variance such that X(t) and X(t') are independent if t and t' are two different real numbers. Now, why don't you figure out what a typical sample function (or realization) of this process is, whether such a realization can actually be exhibited (remember that instantaneous changes in voltage will require that infinite currents instantaneously change the charges in various capacitors in the circuit!), and whether second-order random process theory can be applied to this process that you have described? Since you claim that you are not trolling or seeking help on homework, maybe you should be asking your instructor these questions too? --Dilip Sarwate

Reply by ●October 31, 20032003-10-31

Jerry Avins wrote:> Why does it surprise you that the calculated properties of impossible > abstractions are themselves not possible?Because mathematical abstractions follow mathematical rules and axioms, not the possibility or impossibility of such abstraction to exist in nature. The great majority of mathematical constructs are abstractions that are not possible in nature (I would almost dare to say *all* mathematical abstractions). The simplest and most ubiqutous abstraction: the notion of "continuous" values. It doesn't seem to exist in nature; yet we do use differential equations and differential calculus to obtain *real* results (that do approximate the way physical systems behave). (and let's not get started with complex numbers!! Just because complex numbers do not exist in nature, would you dismiss as invalid all of the Fourier analysis theory or anything that someone derives based on complex numbers properties?) What I'm trying to say (which applies to the white noise discussion) is: if we set up a mathematical abstraction with certain properties (properties that do not contradict other more fundamental mathematical axioms), then anything that we derive from it will have to be consistent with that and other mathematical axioms! White noise should not be an exception! So white noise can not exist in nature? So what? Complex numbers don't either, and the delta function doesn't either, and yet they don't lead to contradiction *in mathematical terms* when deriving properties of them. See, that's my point: what I'm seeing when thinking of PSD as density of power is a contradiction *in mathematical terms*, not a contradiction with physical properties or terms.>> Same thing (IMO) about white noise. We talk about >> *Gaussian* white noise with zero mean and variance >> sigma^2. Well, if the variance is sigma^2, then the >> average power is not infinite! > > Are you sure that sigma^2 represents power here?It represents "mean power" or "average" power, of course I'm sure! At least it has to!> There is no limit to > the magnitude of true Gaussian noiseI know. And it doesn't matter: the average is still finite! The expected value of x^2, where x is a random variable with gaussian distribution, is the variance of it.> If there is just one infinite-amplitude event in a year, the average > power is rather large, to say the least.Actually, there is never an event with infinite-amplitude. At any time, the value of a Gaussian variable is an *actual number*. There is no such thing as a function, or a random variable, taking "infinite-value".> It doesn't do to say that impossible conditions are OK because that are > merely mathematical constructsHmmm, I know that we may be shifting to the "philosophical grounds" here, but I have to disagree with that. As I said, virtually all mathematical constructs are indeed impossible to achieve in nature. Many of them are things that we use on a daily basis in DSP, and in general in signal analysis and the like (e.g., the delta, complex numbers, differential equations and differential calculus in general, integrals from 0 to infinity, or from -infinity to infinity). BTW, notice that I'm not saying that impossible conditions are ok: I'm saying that mathematical constructs representing impossible conditions may be ok -- as long as they're ok from the point of view of the mathematical rules that we use to deal with them. Applying those mathematical constructs to represent physical things, that's a different thing, and I do agree with what you mention about dealing with "band limited" versions of white noise, etc. Carlos --

Reply by ●October 31, 20032003-10-31

Carlos Moreno wrote: [...] Maybe I'm wrong, but I think the sigma^2 refers to the Gaussian variable, while the PDS refers to the Gaussian process, i.e. a sequence of Gaussian variables. So the process has infinite "power", while the variable itself has statistical power = sigma^2. Does it fit to you? bye, -- Piergiorgio Sartor

Reply by ●October 31, 20032003-10-31

Carlos Moreno wrote:> Jerry Avins wrote: > >> Why does it surprise you that the calculated properties of impossible >> abstractions are themselves not possible? > > > Because mathematical abstractions follow mathematical rules > and axioms, not the possibility or impossibility of such > abstraction to exist in nature.This is silly. I can prove that if the moon is made of green cheese, then you are your own grandmother. In a proof, any false premise can lead to any conclusion, true or false. Just so, a non-realizable abstraction can lead to a non-realizable property. That's OK. What's not OK is to claim that it really is realizable, or that a non-realizable conclusion invalidated the argument. In fact, all it shows is that the abstraction was extended outside it's useful domain. Just as functions have regions of convergence, abstractions have regions of applicability. Be happy!>... Jerry -- Engineering is the art of making what you want from things you can get. �����������������������������������������������������������������������

Reply by ●October 31, 20032003-10-31

Piergiorgio Sartor wrote:> Carlos Moreno wrote: > > [...] > > Maybe I'm wrong, but I think the sigma^2 refers > to the Gaussian variable, while the PDS refers > to the Gaussian process, i.e. a sequence of > Gaussian variables. > > So the process has infinite "power", while the > variable itself has statistical power = sigma^2. > > Does it fit to you?I'm not sure... The first impression is to say "no it doesn't", but I'm not quite sure that I'm not missing some subtlety. I know I may be repeating myself, but applying your concepts to the random process that I described before (x(t) is independent of the value of x at any time other than t, and has uniform distribution in [-1,1]. Any possible realization of such process must have an average power less than one. In fact, for any possible realization of such process, the power at any given time (as an "instantaneous" measure) must be less than 1 (since the magnitude of the signal can not be greater than one). Also, the variance of the signal at any given time (i.e., the variance with respect to all possible realizations of the process) is also less than one. So, how could a concept that leads to "infinite power" fit in here? Notice that this example emphasizes a bit more the impossibility of having infinite power, but it is indeed equivalent to the example of Gaussian white noise. Carlos --

Reply by ●October 31, 20032003-10-31

Dilip V. Sarwate wrote:> [...] > --Dilip SarwateI guess I can only thank you for a detailed and careful explanation of the terms causing my confusion... (in fact, I'll have to re-read it carefully :-)) I was in the process of writing a long and detailed message in response to yours, but to be honest, *I* myself read the message and it *really* started to sound like I'm trolling... :-( My conclusion is that I will definitely have to figure out a way to reconcile ideas that in my mind are contradictory. At some point during this discussion I though about the possibility that this is indeed one of those "mathematical paradoxes" (something like the notion that there is exactly the same number of points in the interval (0,1) as in the interval (0,infinity), given that I can define a one-to-one, bijective function that maps (0,1) to (0,infinity). This paradox is "explained" by the fact that the real numbers is not a countable set. (I think -- maybe mathematicians out there will scream at me telling me that I have no clue of what I'm saying :-)) See, the thing is that I'm not able to see if this PSD thing with white noise is one of those paradoxes, and if so, where would the origin of that paradox be. Oh well, I guess I bothered you guys enough, so I'll stop. Thanks to all that participated!! Cheers, Carlos --

Reply by ●October 31, 20032003-10-31

Carlos Moreno wrote: ...> I know I may be repeating myself, but applying your > concepts to the random process that I described > before (x(t) is independent of the value of x at > any time other than t, and has uniform distribution > in [-1,1].Then it isn't bandlimited, so you can't draw legitimate conclusions from the samples. ... Jerry -- Engineering is the art of making what you want from things you can get. �����������������������������������������������������������������������

Reply by ●November 1, 20032003-11-01

Carlos Moreno wrote:> I know I may be repeating myself, but applying your > concepts to the random process that I described > before (x(t) is independent of the value of x at > any time other than t, and has uniform distribution > in [-1,1].OK...> Any possible realization of such process must have > an average power less than one. In fact, for any > possible realization of such process, the power at > any given time (as an "instantaneous" measure) must > be less than 1 (since the magnitude of the signal > can not be greater than one).I do not get this. A possibile realization is: ..., 0, 0, 1, 0, 0... which has quite a lot of spectral energy, I would say.> Also, the variance of the signal at any given time > (i.e., the variance with respect to all possible > realizations of the process) is also less than one.That signal does not have any variance, the random variable has variance.> So, how could a concept that leads to "infinite > power" fit in here? Notice that this example > emphasizes a bit more the impossibility of having > infinite power, but it is indeed equivalent to the > example of Gaussian white noise.Statistical power, but I do not see the point. In my view the process you described has infinite power, why not? bye, -- piergiorgio

Reply by ●November 1, 20032003-11-01

I know I said I would leave you guys alone and let the thread drop... But now you're answering my other post, and it feels kind of rude to let you talking alone... Piergiorgio Sartor wrote:> Carlos Moreno wrote: > >> I know I may be repeating myself, but applying your >> concepts to the random process that I described >> before (x(t) is independent of the value of x at >> any time other than t, and has uniform distribution >> in [-1,1].>>> Any possible realization of such process must have >> an average power less than one. In fact, for any >> possible realization of such process, the power at >> any given time (as an "instantaneous" measure) must >> be less than 1 (since the magnitude of the signal >> can not be greater than one). > > I do not get this. > A possibile realization is: > > ..., 0, 0, 1, 0, 0...??? How can that be a possible realization? The process I described is continuous-time. But regardless, I don't understand what you mean with "a lot of spectral energy". I'm talking about signals representing voltage, and thus, the power I'm referring to is electric power, which, as an instantaneous measure, at time t, is equal to x(t)^2 (divided by the value of the resistor to which it is applied, but let's assume a normalized 1 ohm resistor) If we talk about the average power of one particular realization (let's call it the "time average power", to avoid confusion with any other parameter), then that would be given by: T / 1 | TAP = lim --- | x(t)^2 dt T->oo 2T | / -T That integral is less-than-or-equal to an integral with the same limits and a function that is >= x(t)^2 for all t. So, such function could be f(t) = 1. If you plug f(t) = 1 (replacing x(t)^2) in the above formula, you obtain that the average power (time average power) is 1. Thus, the average power of the signal (the particular realization of the process I described) is less-than-or-equal than one. If all possible realizations have a time average power less than one, then the ensemble average power (i.e., the average over all possible realizations of the time average power) must also be less than or equal than one. I'm using basic definition and basic properties of integrals, and I reach a conclusion that contradicts the fact that the white noise is found to have infinite power, if calculated from the fact that the constant-valued PSD represents density of power. I feel so frustrated that I haven't been able to communicate what's in my mind!! And that is a fact, because I am seeing a contradiction that no-one else sees, and no-one has been able to make me understand why there isn't a contradiction, or why such contradiction is to be expected (no, I'm still not buying the "since white noise is impossible to achieve in nature..." -- again, the way I see it, the contradiction arises in purely abstract mathematical terms, using the rules and axioms of mathematics, which apply to mathematical constructs).> In my view the process you described has infinite > power, why not?Well, the way I see it, because of the above... Maybe I'm still using the wrong terminology? Or maybe I'm confusion some terms or properties?? I'm really hoping that someone will be able and willing to make me understand what's happening!! (so that I can leave you guys alone AND sleep peacefully at the same time :-)) -- I'm serious!! I'm having nightmares about this!!! :-((( Cheers, Carlos --