Exercise 2.4Z: Repetition to IDFT: Difference between revisions

From LNTwww
Mwiki-lnt (talk | contribs)
m Textersetzung - „*Sollte die Eingabe des Zahlenwertes „0” erforderlich sein, so geben Sie bitte „0.” ein.“ durch „ “
Fix interlanguage link: resolve redirect chain
 
(13 intermediate revisions by 5 users not shown)
Line 1: Line 1:
{{quiz-Header|Buchseite=Beispiele von Nachrichtensystemen/xDSL als Übertragungstechnik
{{quiz-Header|Buchseite=Examples_of_Communication_Systems/xDSL_as_Transmission_Technology


}}
}}


[[File:P_ID1971__Sig_A_5_2.png|right|frame|Fünf Mustersätze zur IDFT]]
[[File:P_ID1971__Sig_A_5_2.png|right|frame|Five sample sets for  $\rm IDFT$]]


Bei der Diskreten Fouriertransformation (DFT) werden aus den Zeitabtastwerten $d(\nu) \ {\rm mit} \  \nu = 0$, ... , $N 1$ die diskreten Spektralkoeffizienten $D(\mu) \ {\rm mit} \  \mu = 0$, ... , $N 1$ wie folgt berechnet:
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:
:$$D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1} d(\nu)\cdot {w}^{\hspace{0.05cm}\nu \hspace{0.03cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$
:$$D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1} d(\nu)\cdot {w}^{\hspace{0.05cm}\nu \hspace{0.03cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$
Hierbei ist mit $w$ der komplexe Drehfaktor abgekürzt, der folgendermaßen definiert ist:
Here  $w$  abbreviates the complex rotation factor, which is defined as follows:
:$$w = {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi /N} = \cos \left( \frac {2 \pi}{N}\right)-{\rm j} \cdot \sin \left( \frac {2 \pi}{N}\right) \hspace{0.05cm}.$$
:$$w = {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi /N} = \cos \left( {2 \pi}/{N}\right)-{\rm j} \cdot \sin \left( {2 \pi}/{N}\right) \hspace{0.05cm}.$$
Somit gilt für die Inverse Diskrete Fouriertransformation (IDFT) als Umkehrfunktion der DFT:
Thus, for the Inverse Discrete Fourier Transform  $\rm (IDFT)$  as an inverse function of  $\rm DFT$:
:$$ d(\nu) = \sum_{\mu = 0 }^{N-1} D(\mu) \cdot {w}^{-\nu \hspace{0.03cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$
:$$ d(\nu) = \sum_{\mu = 0 }^{N-1} D(\mu) \cdot {w}^{-\nu \hspace{0.03cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$
In dieser Aufgabe sollen für verschiedene Beispielfolgen $D(\mu)$ – die in obiger Tabelle mit $\boldsymbol{\rm A}$, ... , $\boldsymbol{\rm E}$ bezeichnet sind – die Zeitkoeffizienten $d(\nu)$ ermittelt werden. Es gilt somit stets $N = 8$.
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  $N = 8$.




Line 18: Line 18:




''Hinweise:''
 
*Die Aufgabe bezieht sich auf die theoretischen Grundlagen des Kapitels [[Signaldarstellung/Diskrete_Fouriertransformation_(DFT)|Diskrete Fouriertransformation]] des Buches „Signaldarstellung” und ist identisch mit der dortigen [[Aufgaben:Aufgabe_5.2:_Inverse_Diskrete_Fouriertransformation|Aufgabe 5.2]].  
 
*Sie können Ihre Lösung mit dem interaktiven Applet [[Applets:Diskrete_Fouriertransformation_(Applet)|Diskrete Fouriertransformation]] überprüfen.
 
*DFT und IDFT spielen auch bei [[Beispiele_von_Nachrichtensystemen/xDSL_als_Übertragungstechnik#DMT.E2.80.93Realisierung_mit_IDFT.2FDFT|DSM/DSL]] eine große Rolle.  
Hints:
*Im entsprechenden Kapitel werden die Spektralkoeffizienten allerdings mit $D_k$ bezeichnet und die Zeitabtastwerte mit $s_l$. Wir bitten Sie, diese Nomenklaturdiskrepanz zu entschuldigen.
*This exercise refers to the theoretical foundations of the chapter  [[Signal_Representation/Discrete_Fourier_Transform_(DFT)|"Discrete Fourier Transform"]]  of the book "Signal Representation" and is identical to the one there  [[Aufgaben:Exercise_5.2:_Inverse_Discrete_Fourier_Transform|"Exercise 5.2"]].  
*Für die beiden Laufvariablen gelten mit dem DFT–Parameter $N = 8$:  $0 \le k \le 7, \hspace{0.2cm}0 \le l \le 7 \hspace{0.05cm}.$
*You can check your solution using the interactive applet  [[Applets:Diskrete_Fouriertransformation_(Applet)|"Discrete Fourier Transform"]] .
*DFT and IDFT also play a major role in  [[Examples_of_Communication_Systems/xDSL_as_Transmission_Technology#DMT_realization_with_IDFT.2FDFT|"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  $N = 8$:   $0 \le k \le 7, \hspace{0.2cm}0 \le l \le 7 \hspace{0.05cm}.$
   
   


Line 30: Line 33:




===Fragebogen===
===Questions===
<quiz display=simple>
<quiz display=simple>




Wie lauten die Zeitkoeffizienten $d(\nu)$ für $D(\mu)$ gemäß Spalte $\boldsymbol{\rm A}$?
What are the time coefficients&nbsp; $d(\nu)$&nbsp; for&nbsp; $D(\mu)$&nbsp; according to column&nbsp; $\boldsymbol{\rm A}$?
|type="{}"}
|type="{}"}
$d(0) \ = \ ${ 1 }  
$d(0) \ = \ ${ 1 }  
Line 40: Line 43:




{Wie lauten die Zeitkoeffizienten $d(\nu)$ für $D(\mu)$ gemäß Spalte $\boldsymbol{\rm B}$?
{What are the time coefficients&nbsp; $d(\nu)$&nbsp; for&nbsp; $D(\mu)$&nbsp; according to column&nbsp; $\boldsymbol{\rm B}$?
|type="{}"}
|type="{}"}
$d(0) \ = \ ${ 1 }  
$d(0) \ = \ ${ 1 }  
$d(1) \ = \ ${ 0.707 3% }
$d(1) \ = \ ${ 0.707 3% }


{Wie lauten die Zeitkoeffizienten $d(\nu)$ für $D(\mu)$ gemäß Spalte $\boldsymbol{\rm C}$?
{What are the time coefficients&nbsp; $d(\nu)$&nbsp; for&nbsp; $D(\mu)$&nbsp; according to column&nbsp; $\boldsymbol{\rm C}$?
|type="{}"}
|type="{}"}
$d(0) \ = \ ${ 1 }  
$d(0) \ = \ ${ 1 }  
Line 51: Line 54:




{Wie lauten die Zeitkoeffizienten $d(\nu)$ für $D(\mu)$ gemäß Spalte $\boldsymbol{\rm D}$?
{What are the time coefficients&nbsp; $d(\nu)$&nbsp; for&nbsp; $D(\mu)$&nbsp; according to column&nbsp; $\boldsymbol{\rm D}$?
|type="{}"}
|type="{}"}
$d(0) \ = \ ${ 1 }  
$d(0) \ = \ ${ 1 }  
Line 57: Line 60:




{Wie lauten die Zeitkoeffizienten $d(\nu)$ für $D(\mu)$ gemäß Spalte $\boldsymbol{\rm E}$?
{What are the time coefficients&nbsp; $d(\nu)$&nbsp; for&nbsp; $D(\mu)$&nbsp; according to column&nbsp; $\boldsymbol{\rm E}$?
|type="{}"}
|type="{}"}
$d(0) \ = \ ${ 2 }  
$d(0) \ = \ ${ 2 }  
Line 65: Line 68:
</quiz>
</quiz>


===Musterlösung===
===Solution===
{{ML-Kopf}}
{{ML-Kopf}}
'''(1)'''&nbsp; Aus der IDFT–Gleichung wird mit $D(\mu) = 0$ für $\mu \neq 0$ für $0 ≤ \nu ≤ 7$:
'''(1)'''&nbsp; From the IDFT equation, $D(\mu) = 0$ for $\mu \neq 0$ is obtained for the time coefficients with indices&nbsp; $0 ≤ \nu ≤ 7$:
:$$d(\nu) = D(0) \cdot w^0 = D(0) \ = \ 1\hspace{0.3cm} \Rightarrow\hspace{0.3cm}\underline{d(0) = d(1) \ = \ 1}.$$
:$$d(\nu) = D(0) \cdot w^0 = D(0) \ = \ 1\hspace{0.3cm} \rightarrow\hspace{0.3cm}\underline{d(0) = d(1) \ = \ 1}.$$
   
   
Dieser Parametersatz beschreibt somit die diskrete Form der Fourierkorrespondenz des Gleichsignals:
This set of parameters thus describes the discrete form of the Fourier correspondence of the DC signal:
:$$x(t) = 1 \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} X(f) = {\delta}(f) \hspace{0.05cm}.$$
:$$x(t) = 1 \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} X(f) = {\delta}(f) \hspace{0.05cm}.$$
   
   


'''(2)'''&nbsp; Hier sind alle Spektralkoeffizienten $0$ außer $D_{1} = D_{7} = 0.5$. Daraus folgt für $0 ≤ \nu ≤ 7$:
 
'''(2)'''&nbsp; Here all spectral coefficients are $0$ except $D_{1} = D_{7} = 0.5$. It follows that for $0 ≤ \nu ≤ 7$:
:$$d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (7\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} \hspace{0.05cm}.$$
:$$d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (7\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} \hspace{0.05cm}.$$
Aufgrund der Periodizität gilt aber auch:
However, due to periodicity, the following is also true:
:$$d(\nu) \ = \ 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{4} \cdot \nu \right)\hspace{0.3cm}
:$$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}}.$$
\Rightarrow  \hspace{0.3cm}\underline{d(0) = 1, \hspace{0.2cm}d(1) = {1}/{\sqrt{2}} \approx 0.707 \hspace{0.05cm}}.$$
Thus, it is the discrete-time equivalent of
Es handelt sich also um das zeitdiskrete Äquivalent zu
:$$x(t) = \cos(2 \pi \cdot f_{\rm A} \cdot t) \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} X(f) = {1}/{2} \cdot {\delta}(f + f_{\rm A}) + {1}/{2} \cdot {\delta}(f - f_{\rm A}) \hspace{0.05cm},$$
:$$x(t) = \cos(2 \pi \cdot f_{\rm A} \cdot t) \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} X(f) = {1}/{2} \cdot {\delta}(f + f_{\rm A}) + {1}/{2} \cdot {\delta}(f - f_{\rm A}) \hspace{0.05cm},$$
wobei $f_{\rm A}$ die kleinste in der DFT darstellbare Frequenz bezeichnet.
where $f_{\rm A}$ denotes the smallest frequency that can be represented in the DFT.




'''(3)'''&nbsp; Gegenüber der Teilaufgabe (2) ist nun die Frequenz doppelt so groß, nämlich $2 \cdot f_{\rm A}$ anstelle von $f_{\rm A}$:
:$$x(t) = \cos(2 \pi \cdot (2f_{\rm A}) \cdot t) \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} X(f) = {1}/{2} \cdot {\delta}(f + 2f_{\rm A}) + {1}/{2}\cdot {\delta}(f - 2f_{\rm A}) \hspace{0.05cm},$$
Damit beschreibt die Folge $〈d(\nu)〉$ zwei Perioden der Cosinusschwingung, und es gilt für $0 ≤ \nu ≤ 7$:
:$$d(\nu) \ = \ 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left({\pi}/{2} \cdot \nu \right)\hspace{0.3cm}
\Rightarrow  \hspace{0.3cm}\underline{d(0) = 1, \hspace{0.2cm}d(1) = 0 }\hspace{0.05cm}.$$


'''(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; Durch eine weitere Verdoppelung der Cosinusfrequenz auf $4f_{\rm A}$ kommt man schließlich zur zeitkontinuierlichen Fourierkorrespondenz
 
'''(4)'''&nbsp; By further doubling the cosine frequency to $4f_{\rm A}$, one finally arrives at the continuous-time Fourier correspondence
:$$d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} \pi \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} \pi \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left(\pi \cdot \nu \right) \hspace{0.05cm}$$
:$$d(\nu) = 0.5 \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} \pi \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} + 0.5 \cdot {\rm e}^{{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} \pi \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \cos \left(\pi \cdot \nu \right) \hspace{0.05cm}$$
und damit zu den Zeitkoeffizienten
and thus to the time coefficients
:$$\underline{d(0) =}d(2) =d(4) =d(6)= +1, \hspace{0.2cm}\underline{d(1)} =d(3) =d(5) =d(7) = -1 \hspace{0.3cm}
:$$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}.$$
\Rightarrow \hspace{0.3cm}\underline{d(0) = +1, \hspace{0.2cm}d(1) = -1 }\hspace{0.05cm}.$$
*Note that the two Dirac functions coincide in the discrete-time representation due to periodicity.  
Zu beachten ist, dass die beiden Diracfunktionen in der zeitdiskreten Darstellung aufgrund der Periodizität zusammenfallen. Das heißt: Die Koeffizienten $D(4) = 0.5$ und $D(-4) = 0.5$ ergeben zusammen $D(4) = 1$.
*That is, &nbsp; The coefficients $D(4) = 0.5$ and $D(-4) = 0.5$ add up to $D(4) = 1$.
 




'''(5)'''&nbsp; Auch die Diskrete Fouriertransformation ist linear. Deshalb ist das Superpositionsprinzip weiterhin anwendbar. Die Koeffizienten $D(\mu)$ aus Spalte $\boldsymbol{\rm E}$ ergeben sich als die Summen der Spalten $\boldsymbol{\rm A}$ und $\boldsymbol{\rm D}$. Deshalb wird aus der alternierenden Folge $〈d(\nu)〉$ entsprechend Teilaufgabe (4) die um 1 nach oben verschobene Folge:
'''(5)'''&nbsp; The Discrete Fourier Transform is also linear. Therefore, the superposition principle is still applicable.  
:$$d(0) =d(2) =d(4) =d(6)= 2, \hspace{0.2cm}d(1) =d(3) =d(5) =d(7) = 0\hspace{0.3cm}
*The coefficients $D(\mu)$ from column $\boldsymbol{\rm E}$ are obtained as the sums of columns $\boldsymbol{\rm A}$ and $\boldsymbol{\rm D}$.  
\Rightarrow  \hspace{0.3cm}\underline{d(0) = 2, \hspace{0.2cm}d(1) =0 }\hspace{0.05cm}. $$
*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}}
Line 107: Line 112:




[[Category:Aufgaben zu Beispiele von Nachrichtensystemen|^2.3 xDSL als Übertragungstechnik
[[Category:Examples of Communication Systems: Exercises|^2.3 xDSL Transmission Technology


^]]
^]]
[[de:Aufgabe 2.4Z: Wiederholung zur IDFT]]

Latest revision as of 17:53, 16 March 2026

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:
$$d(0) =d(2) =d(4) =d(6)= 2, \hspace{0.2cm}d(1) =d(3) =d(5) =d(7) = 0\hspace{0.3cm}\Rightarrow \hspace{0.3cm}\underline{d(0) = 2, \hspace{0.2cm}d(1) =0 }\hspace{0.05cm}. $$