wX be the DTFT of ] [ nx Then its inverse is inverse Fourier integral of )( Examples with DTFT are: periodic signals and unit step-functions • )( wX typically
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Using the definition determine the DTFT of the following sequences It it does not exist Solution a) DFT x 3 , x 0 , x 1 , x 2 = DFT x n - 1 4 = w4 The inverse DCT obtained for L = 20, 30, 40 are shown below
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4.1 Chapter 4: Discrete-time Fourier Transform (DTFT)
4.1 DTFT and its Inverse
Forward DTFT: The DTFT is a transformation that maps Discrete-time (DT) signal x[n] into a complex valued
function of the real variable w, namely: wenxwXnjwn ,][)( (4.1)· Note nis a discrete-time instant, but w represent the continuous real-valued frequency as in the
continuous Fourier transform. This is also known as the analysis equation.· In general CwXÎ)(
· },{)()2(ppp-ÎÞ=+wwXnwX is sufficient to describe everything. (4.2) · )(wX is normally called the spectrum of ][nx with: ,:)(|:)(|.|)(|)()( (4.3) · The magnitude spectrum is almost all the time expressed in decibels (dB): |)(|log.20|)(|10wXwXdB= (4.4)Inverse DTFT: Let )(wX be the DTFT of ].[nx Then its inverse is inverse Fourier integral of )(wX in the
interval ).,{pp- =-p p pdwewXnxjwn)(21][ (4.5)This is also called the synthesis equation.
Derivation: Utilizing a special integral: ][2ndwejwnpdp p =ò- we write:4.2 ][.2][][2][}][{)(][nxknkxdwekxdweekxdwewXkkknjwjwn
kjwkjwnpdpp pp pp p Note that since x[n] can be recovered uniquely from its DTFT, they form Fourier Pair: ).(][wXnxÛ Convergence of DTFT: In order DTFT to exist, the series å¥ njwn enx][ must converge. In other words: M Mnjwn MenxwX][)( must converge to a limit )(wX as .¥®M (4.6) Convergence of )(wXm for three difference signal types have to be studied: · Absolutely summable signals: ][nx is absolutely summable iff ¥å<¥ -¥=nnx|][|. In this case, )(wX always exists because: nnjwn njwn nxenxenx|][|||.|][||][| (4.7) · Energy signals: Remember ][nx is an energy signal iff .|][|2¥<庥 -¥=nxnxE We can show that )(wXM converges in the mean-square sense to :)(wX0|)()(|2=ò--
¥®dwwXwXLimMMp
p (4.8) Note that mean-square sense convergence is weaker than the uniform (always) convergence of (4.7). · Power signals: ][nx is a power signal iff ¥<å+=-=¥®NNnNxnxNLimP2|][|121
· In this case, ][nx with a finite power is expected to have infinite energy. But )(wXM may still converge
to )(wX and have DTFT. · Examples with DTFT are: periodic signals and unit step-functions. · )(wX typically contains continuous delta functions in the variable .w4.3 4.2 DTFT Examples
Example 4.1 Find the DTFT of a unit-sample ].[][nnxd=1][][)(0==å=å=-¥
-¥=-j njwn njwn eenenxwXd (4.9) Similarly, the DTFT of a generic unit-sample is given by:0][]}[{00jwn
njwn eennnnDTFT-¥ =å-=-dd (4.10)Example 4.2 Find the DTFT of an arbitrary finite duration discrete pulse signal in the interval: :21NN<
][][2 1 kncnxNNkk-å=-=d
Note: ][nx is absolutely summable and DTFT exists: jwkNNkknjwn
NNkknjwn
NNkkecekncekncwX-
-=å=å-å=å-å=2 12 12 1 }][{]}[{)(dd (4.11)Example 4.3 Find the DTFT of an exponential sequence: .1||][][<=awherenuanxn It is not difficult to see
that this signal is absolutely summable and the DTFT must exist. jw nnjw njwnn njwnnaeaeeaenuawX-¥ -=å=å=å=11 )(.][.)(00 (4.12) Observe the plot of the magnitude spectrum for DTFT and )(wXM for: 8.0=a and },20,10,5,2{DTFTM=¥=4.4 Example 4.4 Gibbs Phenomenon: Significance of the finite size of M in (4.6).
For small
M, the approximation of a pulse by a finite harmonics have significant overshoots and undershoots. But it gets smaller as the number of terms in the summation increases.Example 4.5 Ideal Low-Pass Filter (LPF). Consider a frequency response defined by a DTFT with a form:
<<<=pwwwwwX CC0||1)( (4.13)
4.5 Here any signal with frequency components smaller than
Cw will be untouched, whereas all other frequencies will be forced to zero. Hence, a discrete-time continuous frequency ideal LPF configuration. Through the computation of inverse DTFT we obtain: )(21][pppnwSincwdwenxCCw wjwn C C (4.14) where . )sin()(xx xSincpp= The spectrum and its inverse transform for 2/p=C w has been depicted above.4.3 Properties of DTFT
4.3.1 Real and Imaginary Parts:
][][][njxnxnxIR+= Û )()()(wjXwXwXIR+= (4.15)4.3.2 Even and Odd Parts:
][][][nxnxnxoddev+= Û )()()(wXwXwXoddev+= (4.16a) ][]}[][.{2/1][**nxnxnxnxevev-=-+= Û ][]}[)(.{2/1)(**wXwXwXwXevev-=-+= (4.16b) ][]}[][.{2/1][**nxnxnxnxoddodd--=--= Û)()}()(.{2/1)(**wXwXwXwXoddodd--=--= (4.16c)4.3.3 Real and Imaginary Signals:
If ÂÎ][nx then );()(*-=XwX even symmetry and it implies:4.6 )()(|;)(|)(|wXwXwXwX--Ð=Ð-= (4.17a)
)()();()(wXwXwXwXIIRR--=-= (4.17b) If ÁÎ][nx (purely imaginary) then )()(*wXwX--=; odd symmetry (anti-symmetry.)4.3.4 Linearity:
a. Zero-in zero-out and b. Superposition principle applies: )(.)(.][.][.wXBwXAnyBnxA+Û+ (4.18)4.3.5 Time-Shift (Delay) Property:
)(.][wXeDnxjwD-Û- (4.19)4.3.6 Frequency-Shift (Modulation) Property:
][.][nxewwXnjwCC-Û- (4.20)
Example 4.6 Consider a first-order system:
]1[.][.][10-+=nxKnxKny Then )().()(10wXeKKwYjw-+= and the frequency response: jweKKwXwYjwH-+==.)(/)()(104.3.7 Convolution Property:
)().(][*][wHwXnhnxÛ (4.21)4.3.8 Multiplication Property:
-Û-p p fffpdwYXnynx)().(21][].[ (4.22)4.3.9 Differentiation in Frequency:
][.)(.nxndwwdXjÛ (4.23) 4.74.3.10 Parseval's and Plancherel's Theorems:
dwwXnxnòå=-¥ -¥=p p p22|)(|21|][| (4.24)If][nx and/or ][ny complex then
dwwYwXnynxnò=å-¥ -¥=p p p)().(21][].[** (4.25) Example 4.7 Find the DTFT of a generic discrete-time periodic sequence ].[nx Let us write the Fourier series expansion of a generic periodic signal: =1 00 ][N knjkw keanx where Nwp2 0= )(2.){.){]}[{)(1 0 01 01000å-=å=å==-
=N k knjkwN k kN knjkw kkwwaeDTFTaeaDTFTnxDTFTwXpd (4.26) Therefore, DTFT of a periodic sequence is a set of delta functions placed at multiples of0kw with heights .ka
4.4 DTFT Analysis of Discrete LTI Systems
The input-output relationship of an LTI system is governed by a convolution process: ][*][][nhnxny= where ][nh is the discrete time impulse response of the system. Then the frequency-response is simply the DTFT of :][nh njwn wenhwH,].[)( (4.27)4.8 · If the LTI system is stable then ][nh must be absolutely summable and DTFT exists and is continuous.
· We can recover ][nh from the inverse DTFT: ò ==-p p pdwewHwHIDTFTnhjwn).(21)}({][ (4.28) · We call |)(|wH as the magnitude response and )(wHÐ the phase responseExample 4.8 Let
][.)21 (][nunhn= and ][.)31 (][nunxn=Let us find the output from this system.
1. Via Convolution: å
kknkknukunhnxny][.)21 ].([.)31 (][*][][ ÞNot so easy.2. Via Fast Convolution or DFTF from Example 4.3 or Equation(4.12): jw
ewH--=21 11 )( and jw ewX--=31 11 )( jwjwjwjweeeewHwXwY------ --==31 122113 )21
1).(31
1(1 and the inverse DTFT will result in: ][.)31 (2][.)21 (3][nununynn-=Example 4.9 Causal moving average system:
=1 0 ][1][M k knxMnyIf the input were a unit-impulse: ][][nnxd= then the output would be the discrete-time impulse response: