Exercise 5.2: Inverse Discrete Fourier Transform
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:
- This task belongs to the chapter Discrete Fourier Transformation (DFT).
- The topic dealt with here is also dealt with in the interactive applet Discrete Fourier Transform and Inverse.
Questions
Solution
- 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}.