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Exercise 5.2: Inverse Discrete Fourier Transform

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Five different sets for the spectral coefficients  D(μ)

With the Discrete Fourier Transform  (DFT), the following are obtained

  • from the  N  time coefficients  d(ν)   ⇒   samples of the continuous-time signal  x(t)
  • the  N  spectral range coefficients  D(μ)


are calculated. With  ν=0, ... , N – 1  and  \mu = 0, ... , N – 1  holds:

D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1} d(\nu)\cdot {w}^{\hspace{0.05cm}\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.

Here  w  denotes the complex rotation factor:

w = {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi /N} = \cos \left( {2 \pi}/{N}\right)-{\rm j} \cdot \sin \left( {2 \pi}/{N}\right) \hspace{0.05cm}.

For the Inverse Discrete Fourier Transform  (IDFT)   ⇒   „inverse function” of the DFT, the following applies accordingly:


d(\nu) = \sum_{\mu = 0 }^{N-1} D(\mu) \cdot {w}^{-\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.

In this task, the time coefficients  d(\nu)  are to be determined for various example sequences (which are labelled  \rm A, ... ,  \rm E in the table above)  D(\mu)  ermittelt werden. Thus,  N = 8 always applies.





Hints:


Questions

1

What are the time coefficients  d(\nu)  for the  D(\mu)–values of column  \rm A?

d(0)\ = \

d(1)\ = \

2

What are the time coefficients  d(ν)  for the  D(\mu)–values of column  \rm B?

d(0)\ = \

d(1)\ = \

3

What are the time coefficients  d(ν)  for the  D(\mu)–values of column  \rm C?

d(0)\ = \

d(1)\ = \

4

What are the time coefficients  d(ν)  for the  D(\mu)–values of column  \rm D?

d(0)\ = \

d(1)\ = \

5

What are the time coefficients  d(ν)  for the  D(\mu)–values of column  \rm E?

d(0)\ = \

d(1)\ = \


Solution

(1)  From the IDFT equation, with  D(\mu) = 0  for  \mu \ne 0:

d(\nu) = D(0) \cdot w^0 = D(0) =1\hspace{0.5cm}(0 \le \nu \le 7)\ \hspace{0.5cm} \Rightarrow\hspace{0.5cm}\hspace{0.15 cm}\underline{d(0) = d(1) = 1}.
  • This set of parameters describes the discrete form of the Fourier correspondence of the DC signal:
x(t) = 1 \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} X(f) = {\delta}(f) \hspace{0.05cm}.


(2)  All spectral coefficients are zero except  D_1 = D_7 = 0.5. It follows for  0 ≤ ν ≤ 7:

d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} \hspace{0.05cm}.
  • However, due to periodicity, also holds:
d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{4} \cdot \nu \right) \hspace{0.3cm} \Rightarrow \hspace{0.3cm}\hspace{0.15 cm}\underline{d(0) = 1}, \hspace{0.2cm}\hspace{0.15 cm}\underline{d(1) = {1}/{\sqrt{2}} \approx 0.707} \hspace{0.05cm}.
  • It is therefore the discrete-time equivalent of
x(t) = \cos(2 \pi \cdot f_{\rm A} \cdot t) \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} X(f) = {1}/{2} \cdot {\delta}(f + f_{\rm A}) + {1}/{2} \cdot {\delta}(f - f_{\rm A}) \hspace{0.05cm},
where  f_{\rm A}  denotes the smallest frequency that can be represented in the DFT.


(3)  Compared to subtask  (2) , the oscillation frequency is now twice as large, namely  2 f_{\rm A}  instead of  f_{\rm A}:

x(t) = \cos(2 \pi \cdot (2f_{\rm A}) \cdot t) \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} X(f) = {1}/{2} \cdot {\delta}(f + 2f_{\rm A}) + {1}/{2} \cdot {\delta}(f - 2f_{\rm A}) \hspace{0.05cm},
  • Thus the sequence  \langle \hspace{0.1cm}d(ν)\hspace{0.1cm}\rangle   describes two periods of the cosine oscillation, and it holds for  0 ≤ ν ≤ 7:
d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{2} \cdot \nu \right)\hspace{0.3cm} \Rightarrow \hspace{0.3cm}\hspace{0.15 cm}\underline{d(0) = 1, \hspace{0.2cm}d(1) = 0} \hspace{0.05cm}.


(4)  By further doubling the cosine frequency to  4 f_{\rm A}  one finally arrives at the continuous-time Fourier correspondence

d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} \pi \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} \pi \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left(\pi \cdot \nu \right) \hspace{0.05cm}
and thus to the time coefficients
d(0) =d(2) =d(4) =d(6) \hspace{0.15 cm}\underline{= +1}, \hspace{0.2cm}d(1) =d(3) =d(5) =d(7) \hspace{0.15 cm}\underline{= -1} \hspace{0.05cm}.
  • Note that here the two Dirac functions coincide in the discrete-time representation due to periodicity.
  • The coefficients  D (+4) = 0.5  and  D (-4) = 0.5  together give  D (4) = 1.


(5)  The Discrete Fourier Transform is also linear. Therefore, the superposition principle is still applicable:

  • The coefficients  D(\mu )  from column  \rm E  result as the sums of columns  \rm A  and  \rm D.
  • Therefore, the alternating sequence  \langle \hspace{0.1cm}d(ν) \hspace{0.1cm}\rangle   becomes the sequence shifted up by  1  according to subtask  (4) :
\hspace{0.15 cm}\underline{d(0) =d(2) =d(4) =d(6)= 2}, \hspace{0.2cm}\hspace{0.15 cm}\underline{d(1) =d(3) =d(5) =d(7) = 0} \hspace{0.05cm}.