Calculating the DTFT of 1: Challenges and Solutions

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    Dtft
In a book on digital signal processing, I would expect that the Dirac delta would be the only one of interest. So if the author uses ##\delta()## without comment, I think it is reasonable to assume that it is the Dirac delta.
  • #1
zhaniko93
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Hello.
I'm stuck on calculating DTFT of 1.
DTFT formula is:
5E%7Bjw%7D%29%20%3D%20%5Csum_%7Bn%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7D%20x%28n%29e%5E%7B-jnw%7D.gif

so dtft of 1 is:
gif.latex?%5Csum_%7Bn%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7De%5E%7B-jnw%7D.gif

1) in case of w=1, sum becomes:
gif.latex?%5Csum_%7Bn%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7D%201.gif

doesn't it diverge?
2) in case of w no 1, how the hell should that sum be calculated?
thanks
 
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  • #2
[itex]\displaystyle \sum_{n=-\infty}^{\infty} e^{-jn \omega} = \sum_{n=0}^{\infty} e^{-jn \omega} + \sum_{n=-\infty}^{0} e^{-jn \omega} -1 = \sum_{n=0}^{\infty} e^{-jn \omega} + \sum_{n=0}^{\infty} e^{jn \omega} - 1[/itex].

When the sum converges, each of the last two sums is a geometric series that you should be able to calculate. The intermediate steps may not be neat, but the final answer is very simple.

However, there are values of [itex]\omega[/itex] for which the series is not convergent. You mention [itex]\omega=1[/itex]. I'm guessing you actually mean [itex]\omega=0[/itex] or [itex]e^{j \omega} = 1[/itex]. But this is not the only one!
 
Last edited:
  • #3
Thanks krome.
yes I meant w = 0, so for w=0 the sum doesn't converge. but I found such formula for DFTF of 1:
%202%5Cpi%5Csum_%7Bk%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7D%20%5Cdelta%20%28w%20-%202%5Cpi%20k%29.gif


so for w=0 (and other 2πk numbers) I get 2π. did i get something wrong?
also, for w not equal to 2πk, I get 0, so that sum must converge to 0.
can you give any hint how should I calculate that sum? should I use eulers formula?
 
  • #4
zhaniko93 said:
so for w=0 (and other 2πk numbers) I get 2π. did i get something wrong?

You don't actually get [itex]2 \pi[/itex] at [itex]\omega = 0[/itex], you get infinity. You get [itex]2 \pi \delta ( \omega )[/itex] evaluated at [itex]\omega = 0[/itex], which is infinite or not well-defined at least.

zhaniko93 said:
also, for w not equal to 2πk, I get 0, so that sum must converge to 0.

Yes, that is correct. You should get 0 when [itex]\omega \neq 2 \pi k[/itex] for any integer [itex]k[/itex].

zhaniko93 said:
can you give any hint how should I calculate that sum? should I use eulers formula?

Do you know how to calculate the geometric series [itex]1+x+x^2+x^3+...[/itex]? If you do, then you should be able to calculate the two sums I wrote down. Except you run into a problem if [itex]x= 1[/itex]. That's precisely where you get infinity. Otherwise, you should get [itex]0[/itex].

So for me, the logic goes as follows:
(1) [itex]X( \omega ) = 0[/itex] except when [itex]\omega = 2 \pi k[/itex] for some integer [itex]k[/itex]. Therefore, [itex]\displaystyle X( \omega ) = \sum_{k= - \infty}^{\infty} c_k \delta ( \omega - 2 \pi k )[/itex] for some set of constants [itex]c_k[/itex].

(2) To determine [itex]c_k[/itex], use the inverse DTFT: [itex]\displaystyle x(n) = \frac{1}{2 \pi} \int_{2 \pi k -\pi}^{2 \pi k + \pi} d \omega \, X( \omega ) e^{jn \omega}[/itex]. I've picked a particular region of integration of length [itex]2 \pi[/itex] here. [itex]X( \omega )[/itex] has periodicity [itex]2 \pi[/itex]. You are free to pick any full period over which to perform the integral. Plug in [itex]\displaystyle X( \omega ) = \sum_{m=- \infty}^{\infty} c_m \delta ( \omega - 2 \pi m )[/itex] and interchange the sum and the integral (check out Fubini's or Tonelli's theorems for more rigor regarding such interchanges; this is a mixed case between sums and integrals). You should get [itex]\displaystyle x(n) = \frac{c_k}{2 \pi}[/itex], which shows that [itex]c_k = 2 \pi[/itex] for all [itex]k[/itex] since [itex]x(n) = 1[/itex] for all [itex]n[/itex].
 
  • #5
zhaniko93 said:
Hello.
I'm stuck on calculating DTFT of 1.
DTFT formula is:
5E%7Bjw%7D%29%20%3D%20%5Csum_%7Bn%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7D%20x%28n%29e%5E%7B-jnw%7D.gif

so dtft of 1 is:
gif.latex?%5Csum_%7Bn%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7De%5E%7B-jnw%7D.gif

1) in case of w=1, sum becomes:
gif.latex?%5Csum_%7Bn%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7D%201.gif

doesn't it diverge?
2) in case of w no 1, how the hell should that sum be calculated?
thanks
In fact, the series
$$\sum_{n=-\infty}^{\infty} e^{-in\omega}$$
diverges for all ##\omega##, because the terms do not converge to zero. Therefore the Fourier transform as defined by the sum does not exist. Your solution involving Dirac delta functions is a solution in the sense of distributions. It takes quite a lot more mathematical horsepower to make it rigorous. The nonrigorous method is to work in reverse: use the integral formula for the inverse Fourier transform to calculate the inverse DTFT of
$$2\pi \sum_{k=-\infty}^{\infty}\delta(\omega - 2\pi k)$$
and show that the result is identically 1. Then in order to conclude that the DTFT of 1 is the indicated sum of Dirac delta functions, you need to employ the fact (if it is indeed a fact) that the DTFT and inverse DTFT are inverses of each other when working with distributions.
 
  • #6
I don't quite understand, my book said that [itex]\delta(n)[/itex] is 1 for n = 0 and 0 otherwise. now you're saying that it is infinity? is it dirac delta or kroneker delta? how should I distinguish? (on wikipedia, [itex]\delta[n][/itex] is kroneker and [itex]\delta(w)[/itex] is dirak, so I should distinguish by [] and (), but in my book both are (). My book is Schaum's Outlines of Digital Signal Processing
 
  • #7
It is definitely the Dirac delta in this case. Any [] vs. () notation is nonstandard. It is very common to use ##\delta()## for either the Dirac or the Kronecker delta. If there is any chance of confusion, a careful author should indicate which one is intended.
 

1. What is the DTFT and why is it important in signal processing?

The DTFT (Discrete-Time Fourier Transform) is a mathematical tool used in signal processing to convert a discrete-time signal into its frequency domain representation. It is important because it allows us to analyze and understand the frequency content of a signal, which is crucial in many applications such as audio and image processing.

2. How do you calculate the DTFT of a signal?

To calculate the DTFT of a signal, we use the formula: X(e) = Σx[n]e-jωn, where X(e) is the DTFT of the signal x[n] and ω is the frequency variable. This formula can be evaluated using numerical methods or by using software such as MATLAB.

3. What are the challenges in calculating the DTFT?

One of the main challenges in calculating the DTFT is that it requires an infinite number of calculations, which can be computationally intensive. This can also lead to issues with numerical stability and accuracy. Additionally, some signals may not have a DTFT, making it impossible to calculate.

4. How can we overcome the challenges in calculating the DTFT?

One solution to the challenge of infinite calculations is to use a discretized version of the DTFT, known as the DFT (Discrete Fourier Transform), which only requires a finite number of calculations. To address issues with numerical stability, techniques such as windowing and zero-padding can be used. If a signal does not have a DTFT, alternative methods such as the Laplace transform or the Z-transform can be used.

5. What are some common applications of the DTFT?

The DTFT has many applications in signal processing, including spectral analysis, filtering, and system identification. It is also used in fields such as telecommunications, audio and image processing, and control systems. It is an essential tool for understanding and manipulating signals in the frequency domain.

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