Difference between revisions of "Exercise 2.4Z: Repetition to IDFT"

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{{quiz-Header|Buchseite=Beispiele von Nachrichtensystemen/xDSL als Übertragungstechnik
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{{quiz-Header|Buchseite=Examples_of_Communication_Systems/xDSL_as_Transmission_Technology
  
 
}}
 
}}
  
[[File:|right|frame|]]
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[[File:P_ID1971__Sig_A_5_2.png|right|frame|Five sample sets for&nbsp; $\rm IDFT$]]
  
 +
In the Discrete Fourier Transform&nbsp; $\rm (DFT)$&nbsp; from the time samples&nbsp; $d(\nu) \hspace{0.15cm} {\rm with} \hspace{0.15cm}  \nu = 0$, ... , $N - 1$&nbsp; the discrete spectral coefficients&nbsp; $D(\mu) \hspace{0.15cm} {\rm with} \hspace{0.15cm}  \mu = 0$, ... , $N - 1$&nbsp; calculated as follows:
 +
:$$D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1} d(\nu)\cdot {w}^{\hspace{0.05cm}\nu \hspace{0.03cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$
 +
Here&nbsp; $w$&nbsp; abbreviates the complex rotation factor, which is defined as follows:
 +
:$$w = {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi /N} = \cos \left( {2 \pi}/{N}\right)-{\rm j} \cdot \sin \left( {2 \pi}/{N}\right) \hspace{0.05cm}.$$
 +
Thus, for the Inverse Discrete Fourier Transform&nbsp; $\rm (IDFT)$&nbsp; as an inverse function of&nbsp; $\rm DFT$:
 +
:$$ d(\nu) = \sum_{\mu = 0 }^{N-1} D(\mu) \cdot {w}^{-\nu \hspace{0.03cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$
 +
In this exercise, for different example sequences&nbsp; $D(\mu)$ - which in the above table are denoted by&nbsp; $\boldsymbol{\rm A}$, ... , &nbsp;$\boldsymbol{\rm E}$&nbsp; - the time coefficients&nbsp; $d(\nu)$&nbsp; are determined. Thus, it is always&nbsp; $N = 8$.
  
  
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===Fragebogen===
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 +
 
 +
 
 +
Hints:
 +
*This exercise refers to the theoretical foundations of the chapter&nbsp; [[Signal_Representation/Discrete_Fourier_Transform_(DFT)|"Discrete Fourier Transform"]]&nbsp; of the book "Signal Representation" and is identical to the one there&nbsp; [[Aufgaben:Exercise_5.2:_Inverse_Discrete_Fourier_Transform|"Exercise 5.2"]].
 +
*You can check your solution using the interactive applet&nbsp;  [[Applets:Diskrete_Fouriertransformation_(Applet)|"Discrete Fourier Transform"]]&nbsp;.
 +
*DFT and IDFT also play a major role in&nbsp; [[Examples_of_Communication_Systems/xDSL_as_Transmission_Technology#DMT_realization_with_IDFT.2FDFT|"DSM/DSL"]]&nbsp;.
 +
*In the corresponding chapter, spectral coefficients are denoted by&nbsp; $D_k$&nbsp; and time samples are denoted by&nbsp; $s_l$. We apologize for this nomenclature discrepancy.
 +
*For the two running variables, with the DFT parameter&nbsp; $N = 8$: &nbsp; $0 \le k \le 7, \hspace{0.2cm}0 \le l \le 7 \hspace{0.05cm}.$
 +
 +
 
 +
 
 +
 
 +
 
 +
 
 +
===Questions===
 
<quiz display=simple>
 
<quiz display=simple>
  
  
 +
What are the time coefficients&nbsp; $d(\nu)$&nbsp; for&nbsp; $D(\mu)$&nbsp; according to column&nbsp; $\boldsymbol{\rm A}$?
 +
|type="{}"}
 +
$d(0) \ = \ ${ 1 }
 +
$d(1) \ = \ ${ 1 }
 +
 +
 +
{What are the time coefficients&nbsp; $d(\nu)$&nbsp; for&nbsp; $D(\mu)$&nbsp; according to column&nbsp; $\boldsymbol{\rm B}$?
 +
|type="{}"}
 +
$d(0) \ = \ ${ 1 }
 +
$d(1) \ = \ ${ 0.707 3% }
 +
 +
{What are the time coefficients&nbsp; $d(\nu)$&nbsp; for&nbsp; $D(\mu)$&nbsp; according to column&nbsp; $\boldsymbol{\rm C}$?
 +
|type="{}"}
 +
$d(0) \ = \ ${ 1 }
 +
$d(1) \ = \ ${ 0. }
 +
 +
 +
{What are the time coefficients&nbsp; $d(\nu)$&nbsp; for&nbsp; $D(\mu)$&nbsp; according to column&nbsp; $\boldsymbol{\rm D}$?
 +
|type="{}"}
 +
$d(0) \ = \ ${ 1 }
 +
$d(1) \ = \ ${ -1.01--0.99 }
  
 +
 +
{What are the time coefficients&nbsp; $d(\nu)$&nbsp; for&nbsp; $D(\mu)$&nbsp; according to column&nbsp; $\boldsymbol{\rm E}$?
 +
|type="{}"}
 +
$d(0) \ = \ ${ 2 }
 +
$d(1) \ = \ ${ 0. }
  
  
 
</quiz>
 
</quiz>
  
===Musterlösung===
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===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp;  
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'''(1)'''&nbsp; From the IDFT equation, $D(\mu) = 0$ for $\mu \neq 0$ is obtained for the time coefficients with indices&nbsp; $0 ≤ \nu ≤ 7$:
 +
:$$d(\nu) = D(0) \cdot w^0 = D(0) \ = \ 1\hspace{0.3cm} \rightarrow\hspace{0.3cm}\underline{d(0) = d(1) \ = \ 1}.$$
 +
 +
This set of parameters thus 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)'''&nbsp; Here all spectral coefficients are $0$ except $D_{1} = D_{7} = 0.5$. It follows that for $0 ≤ \nu ≤ 7$:
 +
:$$d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (7\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} \hspace{0.05cm}.$$
 +
However, due to periodicity, the following is also true:
 +
:$$d(\nu) \ = \ 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{4} \cdot \nu \right)\hspace{0.3cm}
 +
\Rightarrow  \hspace{0.3cm}\underline{d(0) = 1, \hspace{0.2cm}d(1) = {1}/{\sqrt{2}} \approx 0.707 \hspace{0.05cm}}.$$
 +
Thus, it is 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)'''&nbsp; Compared to the subtask '''(2)''', the frequency is now twice as large, namely $2 \cdot f_{\rm A}$ 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 $〈d(\nu)〉$ describes two periods of the cosine oscillation, and it holds for $0 ≤ \nu ≤ 7$:
 +
:$$d(\nu) \ = \ 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{2} \cdot \nu \right)\hspace{0.3cm}
 +
\Rightarrow  \hspace{0.3cm}\underline{d(0) = 1, \hspace{0.2cm}d(1) = 0 }\hspace{0.05cm}.$$
 +
 
 +
 
 +
'''(4)'''&nbsp; By further doubling the cosine frequency to $4f_{\rm A}$, 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
 +
:$$d(0) = d(2) =d(4) =d(6)= +1, \hspace{0.2cm}d(1) =d(3) =d(5) =d(7) = -1 \hspace{0.3cm}
 +
\Rightarrow \hspace{0.3cm}\underline{d(0) = +1, \hspace{0.2cm}d(1) = -1 }\hspace{0.05cm}.$$
 +
*Note that the two Dirac functions coincide in the discrete-time representation due to periodicity.
 +
*That is, &nbsp; The coefficients $D(4) = 0.5$ and $D(-4) = 0.5$ add up to $D(4) = 1$.
 +
 
 +
 
 +
 
 +
'''(5)'''&nbsp; The Discrete Fourier Transform is also linear. Therefore, the superposition principle is still applicable.
 +
*The coefficients $D(\mu)$ from column $\boldsymbol{\rm E}$ are obtained as the sums of columns $\boldsymbol{\rm A}$ and $\boldsymbol{\rm D}$.
 +
*Therefore, the alternating sequence $〈d(\nu)〉$ according to subtask '''(4)''' becomes the sequence shifted up by 1:
 +
:$$d(0) =d(2) =d(4) =d(6)= 2, \hspace{0.2cm}d(1) =d(3) =d(5) =d(7) = 0\hspace{0.3cm}
 +
\Rightarrow  \hspace{0.3cm}\underline{d(0) = 2, \hspace{0.2cm}d(1) =0 }\hspace{0.05cm}. $$
  
 
{{ML-Fuß}}
 
{{ML-Fuß}}
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[[Category:Aufgaben zu Beispiele von Nachrichtensystemen|^2.3 xDSL als Übertragungstechnik
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[[Category:Examples of Communication Systems: Exercises|^2.3 xDSL Transmission Technology
  
 
^]]
 
^]]

Latest revision as of 19:22, 7 March 2023

Five sample sets for  $\rm IDFT$

In the Discrete Fourier Transform  $\rm (DFT)$  from the time samples  $d(\nu) \hspace{0.15cm} {\rm with} \hspace{0.15cm} \nu = 0$, ... , $N - 1$  the discrete spectral coefficients  $D(\mu) \hspace{0.15cm} {\rm with} \hspace{0.15cm} \mu = 0$, ... , $N - 1$  calculated as follows:

$$D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1} d(\nu)\cdot {w}^{\hspace{0.05cm}\nu \hspace{0.03cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$

Here  $w$  abbreviates the complex rotation factor, which is defined as follows:

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

Thus, for the Inverse Discrete Fourier Transform  $\rm (IDFT)$  as an inverse function of  $\rm DFT$:

$$ d(\nu) = \sum_{\mu = 0 }^{N-1} D(\mu) \cdot {w}^{-\nu \hspace{0.03cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$

In this exercise, for different example sequences  $D(\mu)$ - which in the above table are denoted by  $\boldsymbol{\rm A}$, ... ,  $\boldsymbol{\rm E}$  - the time coefficients  $d(\nu)$  are determined. Thus, it is always  $N = 8$.





Hints:

  • This exercise refers to the theoretical foundations of the chapter  "Discrete Fourier Transform"  of the book "Signal Representation" and is identical to the one there  "Exercise 5.2".
  • You can check your solution using the interactive applet  "Discrete Fourier Transform" .
  • DFT and IDFT also play a major role in  "DSM/DSL" .
  • In the corresponding chapter, spectral coefficients are denoted by  $D_k$  and time samples are denoted by  $s_l$. We apologize for this nomenclature discrepancy.
  • For the two running variables, with the DFT parameter  $N = 8$:   $0 \le k \le 7, \hspace{0.2cm}0 \le l \le 7 \hspace{0.05cm}.$




Questions

1

What are the time coefficients  $d(\nu)$  for  $D(\mu)$  according to column  $\boldsymbol{\rm A}$?

$d(0) \ = \ $

$d(1) \ = \ $

2

What are the time coefficients  $d(\nu)$  for  $D(\mu)$  according to column  $\boldsymbol{\rm B}$?

$d(0) \ = \ $

$d(1) \ = \ $

3

What are the time coefficients  $d(\nu)$  for  $D(\mu)$  according to column  $\boldsymbol{\rm C}$?

$d(0) \ = \ $

$d(1) \ = \ $

4

What are the time coefficients  $d(\nu)$  for  $D(\mu)$  according to column  $\boldsymbol{\rm D}$?

$d(0) \ = \ $

$d(1) \ = \ $

5

What are the time coefficients  $d(\nu)$  for  $D(\mu)$  according to column  $\boldsymbol{\rm E}$?

$d(0) \ = \ $

$d(1) \ = \ $


Solution

(1)  From the IDFT equation, $D(\mu) = 0$ for $\mu \neq 0$ is obtained for the time coefficients with indices  $0 ≤ \nu ≤ 7$:

$$d(\nu) = D(0) \cdot w^0 = D(0) \ = \ 1\hspace{0.3cm} \rightarrow\hspace{0.3cm}\underline{d(0) = d(1) \ = \ 1}.$$

This set of parameters thus 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)  Here all spectral coefficients are $0$ except $D_{1} = D_{7} = 0.5$. It follows that for $0 ≤ \nu ≤ 7$:

$$d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (7\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} \hspace{0.05cm}.$$

However, due to periodicity, the following is also true:

$$d(\nu) \ = \ 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{4} \cdot \nu \right)\hspace{0.3cm} \Rightarrow \hspace{0.3cm}\underline{d(0) = 1, \hspace{0.2cm}d(1) = {1}/{\sqrt{2}} \approx 0.707 \hspace{0.05cm}}.$$

Thus, it is 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 the subtask (2), the frequency is now twice as large, namely $2 \cdot f_{\rm A}$ 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 $〈d(\nu)〉$ describes two periods of the cosine oscillation, and it holds for $0 ≤ \nu ≤ 7$:

$$d(\nu) \ = \ 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{2} \cdot \nu \right)\hspace{0.3cm} \Rightarrow \hspace{0.3cm}\underline{d(0) = 1, \hspace{0.2cm}d(1) = 0 }\hspace{0.05cm}.$$


(4)  By further doubling the cosine frequency to $4f_{\rm A}$, 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

$$d(0) = d(2) =d(4) =d(6)= +1, \hspace{0.2cm}d(1) =d(3) =d(5) =d(7) = -1 \hspace{0.3cm} \Rightarrow \hspace{0.3cm}\underline{d(0) = +1, \hspace{0.2cm}d(1) = -1 }\hspace{0.05cm}.$$
  • Note that the two Dirac functions coincide in the discrete-time representation due to periodicity.
  • That is,   The coefficients $D(4) = 0.5$ and $D(-4) = 0.5$ add up to $D(4) = 1$.


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

  • The coefficients $D(\mu)$ from column $\boldsymbol{\rm E}$ are obtained as the sums of columns $\boldsymbol{\rm A}$ and $\boldsymbol{\rm D}$.
  • Therefore, the alternating sequence $〈d(\nu)〉$ according to subtask (4) becomes the sequence shifted up by 1:
$$d(0) =d(2) =d(4) =d(6)= 2, \hspace{0.2cm}d(1) =d(3) =d(5) =d(7) = 0\hspace{0.3cm} \Rightarrow \hspace{0.3cm}\underline{d(0) = 2, \hspace{0.2cm}d(1) =0 }\hspace{0.05cm}. $$