Tele3113 wk1tue
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Transcript of Tele3113 wk1tue
p. 1
TELE3113 Analogue & Digital Communications
Review of Fourier Transform
p. 2
Signal Representation
A: Amplitude
f : Frequency (Hz) (ω=2πf)
φ : Phase (radian or degrees)
s(t) = A sin(2π fo t +φo ) or A sin(ωo t +φo )
Time (seconds)
Period (seconds)
Time-domain: waveform
Frequency-domain: spectrum
Frequency (Hz)fo
S(f)
p. 3
Energy and Power of Signals
For an arbitrary signal f(t), the total energy normalized to unit resistance is defined as
joules, )(lim 2 dttfET
TT ∫−∞→
∆
=
and the average power normalized to unit resistance is defined as
, watts )(21lim 2 dttfT
PT
TT ∫−∞→
∆
=
• Note: if 0 < E < ∞ (finite) P = 0.• When will 0 < P < ∞ happen?
p. 4
ttfTtf allfor )()( 0 =+ (*)
where the constant T0 is the period.
The smallest value of T0 such that equation (*) is satisfied is referred to as the fundamental period, and is hereafter simply referred to as the period.
Any signal not satisfying equation (*) is called aperiodic.
A signal f(t) is periodic if and only if
Periodic Signal
p. 5
Deterministic signal can be modeled as a completely specified function of time.
Example)cos()( 0 θ+ω= tAtf
Random signal cannot be completely specified as a function of time and must be modeled probabilistically.
Deterministic & Random Signals
p. 6
Mathematically, a system is a rule used for assigning a function g(t)(the output) to a function f(t) (the input); that is,
where h{•} is the rule or we call the impulse response.
For two systems connected in cascade, the output of the first system forms the input to second, thus forming a new overall system:
System
h(t)
g(t) = h{ f(t) }
f(t) g(t)
g(t) = h2 { h1 [ f(t) ] } = h{ f(t) }
p. 7
If a system is linear then superposition applies; that is, if
then
where a1, a2 are constants. A system is linear if it satisfiesEq. (*); any system not meeting these requirement is nonlinear.
h{ a1 f1(t) + a2 f2(t) } = a1 g1(t) + a2 g2(t) (*)
Linear System
g1(t) = h{ f1(t) }, and g2(t) = h{ f2(t) }
p. 8
A system is time-invariant if a time shift in the input resultsin a corresponding time shift in the output so that
.any for )}({)( 000 tttfhttg −=−
The output of a time-invariant system depends on time differences and not on absolute values of time.
Any system not meeting this requirement is said to be time-varying.
Time-Invariant and Time-Varying
p. 9
A periodic function of time s(t) with a fundamental period of T0 can be represented as an infinite sum of sinusoidal waveforms. Such summation, a Fourier series, may be written as:
∑∑∞
=
∞
=
π+
π+=
1 01 00 ,2sin2cos)(
nn
nn T
ntBTntAAts
where the average value of s(t), A0 is given by
∫−= 2
0
20
,)(1
00
T
T dttsT
A
while
.2sin)(2 20
20
00∫−
π=
T
T dtTntts
TBn
∫−
π= 2
0
20
,2cos)(2
00
T
T dtTntts
TAn
and
(1)
(2)
(3)
(4)
Fourier Series
p. 10
An alternative form of representing the Fourier series is
∑∞
=
φ−
π+=
1 00
2cos)(n
nn TntCCts
where ,00 AC =
,22nnn BAC +=
.tan 1
n
nn A
B−=φ
The Fourier series of a periodic function is thus seen to consist of a summation of harmonics of a fundamental frequency f0 = 1/T0.
The coefficients Cn are called spectral amplitudes, which represent the amplitude of the spectral component Cn cos(2πnf0t − φn) at frequency nf0.
(5)
(6)
(7)
(8)
Fourier Series
p. 11
The exponential form of the Fourier series is used extensively in communication theory. This form is given by
∑∞
−∞=
π
=n
nTntj
eSts ,)( 02
where
∫−
−= 2
0
20
0
2
)(1
0
T
TTntj
dtetsT
S nπ
Note that Sn and S−n are complex conjugate of one another, that is
.*nn SS −=
These are related to the Cn by
,00 CS = .2
njnn eCS φ−=
(9)
(10)
(11)
(12)
Fourier Series
p. 12
Amplitude Spectra (Line Spectra)
Note that except S0 = C0, each spectral line in Fig. (a) at frequency fis replaced by the two spectral lines in Fig. (b), each with half amplitude, one at frequency f and one at frequency - f.
Fourier Series
0 fo 2fo 3fo 4fo 5fo 6fo (n-1) fo nfo
CnFig.(a)
|Sn|
-nfo -(n-1)fo ••• - 6fo0-5fo -4fo -3fo -2fo -fo 0 fo 2fo 3fo 4fo 5fo 6fo ••• (n-1) fo nfo
••• •••
Fig.(b)
p. 13
Consider a unitary square wave defined by
<<−<<
= 150 ,15.00 ,1
)(t.t
tx
and periodically extended outside this interval. The average value is zero, so
.00 =A
Recall that
( )
( ) ( )
( ) ( )
0
22sin
22sin2
2cos22cos2
2cos)(2
2cos)(2
1
5.0
5.0
0
1
5.0
5.0
0
1
0
00
20
20
=
ππ−
ππ=
π−+π=
π=
π=
∫∫∫
∫−
nnt
nnt
dtntdtnt
dtnttx
dtTnttx
TA
T
Tn
Thus all An coefficients are zero.
The Bn coefficients are given by
( )
( ) ( )
( ) ( )
( )π−π
=
ππ+
ππ−=
π−+π=
π=
π=
∫∫∫
∫−
nn
nnt
nnt
dtntdtnt
dtnttx
dtTnttx
TB
T
Tn
cos12
22cos
22cos2
2sin22sin2
2sin)(2
2sin)(2
1
5.0
5.0
0
1
5.0
5.0
0
1
0
00
20
20
which results in
π=even is ,0
odd is ,4
n
nnBn
Fourier Series : Example
p. 14
The Fourier series of a square wave of unitary amplitude with odd symmetry is therefore
Fourier Series : Example
)10sin516sin
312(sin4)( K+π+π+π
π= ttttx
1st term 1st + 2nd terms 1st + 2nd + 3rd terms
Sum up to the 6th term
p. 15
Representation of an Aperiodic Function
)()(lim tftfTT=
∞→(13)
Consider an aperiodic function f(t)
To represent this function as a sum of exponential functions overthe entire interval (-∞, ∞), we construct a new periodic functionfT(t) with period T.
By letting T→∞,
Fourier Transform
p. 16
The new function fT(t) can be represented by an exponential Fourier series, which is written as
∑∞
−∞=
ω=n
tjnnT eFtf ,)( 0
where
∫−
ω−=2/
2/0)(1 T
T
tjnTn dtetf
TF
and ./20 Tπ=ω
(14)
(15)
Fourier Transform
p. 17
For the sake of clear presentation, we set
,0ω=ω∆
nn ,)( nn TFF∆
=ω
Thus, Eq.(14) and (15) become
∑∞
−∞=
ωω=n
tjnT
neFT
tf ,)(1)(
∫−
ω−=ω2/
2/.)()(
T
T
tjTn dtetfF n
The spacing between adjacent lines in the line stream of fT(t)is
./2 Tπ=ω∆
(16)
(17)
(18)
(19)
Fourier Transform
p. 18
Using this relation for T, we get
∑∞
−∞=
ω
πω∆
ω=n
tjnT
neFtf .2
)()(
As T becomes very large, ∆ω becomes smaller and the spectrumbecomes denser.
In the limit T → ∞, the discrete lines in the spectrum of fT(t) mergeand the frequency spectrum becomes continuous.
Therefore,∑
∞
−∞=
ω
∞→∞→ω∆ω
π=
n
tjnTTT
neFtf )(21lim)(lim
becomes ∫∞
∞−
ω ωωπ
= deFtf tj)(21)(
(20)
(21)
(22)
Fourier Transform
p. 19
In a similar way, Eq. (18) becomes
.)()( ∫∞
∞−
ω−=ω dtetfF tj
Eq. (22) and (23) are commonly referred to as the Fourier transform pair.
(23)
Fourier Transform
Inverse Fourier Transform
∫∞
∞−
−= dtetfF tjωω )()(
∫∞
∞−
ω ωωπ
= deFtf tj)(21)(
Fourier Transform
p. 20
F(ω): The spectral density function of f(t).
Fig. 3.2
2/)2/sin()2/(Sa
ωω
=ω
A unit gate function Its spectral density graph
Spectral Density Function
p. 21
The energy delivered to a 1-ohm resistor is
∫∫∞
∞−
∞
∞−== .)()()( *2 dttftfdttfE
Using Eq. (22) in (24), we get
.)()(21
)()(21
)(21)(
*
*
*
∫
∫ ∫
∫ ∫
∞
∞−
∞
∞−
∞
∞−
ω−
∞
∞−
∞
∞−
ω−
ωωωπ
=
ω
ω
π=
ωω
π=
dFF
ddtetfF
dtdeFtfE
tj
tj
Parseval’s Theorem:
∫∫∞
∞−
∞
∞−ωω
π= .)(
21)( 22 dFdttf
(24)
(25)
(26)
Parseval’s Theorem
∫∞
∞−
ω ωωπ
= deFtf tj)(21)(
p. 22
The unit impulse function satisfies
,1)( =δ∫∞
∞−dxx
≠=∞
=.0 0,0
)(xx
xδ
Using the integral properties of the impulse function, the Fourier transform of a unit impulse, δ(t), is
{ } .1)()( 0 ==δ=δℑ ∫∞
∞−
ω− jtj edtett
If the impulse is time-shifted, we have
{ } .)()( 000
tjtj edtetttt ω−∞
∞−
ω− =−δ=−δℑ ∫
(27)
(28)
(29)
(30)
Fourier Transform: Impulse Function
p. 23
{ }
,21
)(21)(
0
001
tj
tj
e
de
ω±
∞
∞−
ω−
π=
ωωωδπ
=ωωδℑ ∫ mm
The spectral density of will be concentrated at ±ω0.tje 0ω±
Taking the Fourier transform of both sides, we have
{ } { }tje 0
21)( 0
1 ω±− ℑπ
=ωωδℑℑ m
which gives{ } )(2 0
0 ωωπδω m=ℑ ± tje
(31)
(32)
(33)
Fourier Transform: Complex Exponential Function
p. 24
The sinusoidal signals and can be written in terms ofthe complex exponentials.
t0cosω t0sin ω
Their Fourier transforms are given by
{ } { }),()(
cos
00
21
21
000
ω+ωπδ+ω−ωπδ=
+ℑ=ωℑ ω−ω tjtj eet
(34)
{ } { }.)()(
sin
00
21
21
000
j
eet tjj
tjj
ω+ωπδ−ω−ωπδ=
−ℑ=ωℑ ω−ω
(35)
Fourier Transform: Sinusoidal Function
p. 25
(37)
We can express a function f(t) that is periodic with period T by itsexponential Fourier series
∑∞
−∞=
ω=n
tjnnT eFtf 0)( where ω0 = 2π/T.
Taking the Fourier transform, we have
(36)
{ }
{ }
∑
∑
∑
∞
−∞=
∞
−∞=
ω
∞
−∞=
ω
ω−ωδπ=
ℑ=
ℑ=ℑ
nn
n
tjnn
n
tjnnT
nF
eF
eFtf
).(2
)(
0
0
0 e.g.
Line spectrum of f(t) with period T
Its spectral density graph
Its Fourier transformA unit gate function
Fourier Transform: Periodic Functions
p. 26
Time and Spectral Density Functions
p. 27
Selected Fourier Transform Pairs
p. 28
Linearity (Superposition)
)()()()( 22112211 ω+ω↔+ FaFatfatfa
Complex Conjugate)()( ** ω−↔ Ftf
Scaling
ω
↔a
Fa
atf 1)( for .0≠a
Time Shifting (Delay)0
0 )()( tjeFttf ωω −↔−
Frequency Shifting (Modulation)
)()( 00 ω−ω↔ω Fetf tj
Differentiation
)()()( ωω↔ Fjtfdtd nn
n
Duality).(2)( ω−π↔ ftF
Multiplication
)()()()( 2121 ω∗ω↔ FFtftf
Convolution
)()()()( 2121 ωω↔∗ FFtftf
Properties of Fourier Transform
p. 29
Scaling
ω
↔a
Fa
atf 1)( for .0≠a
Duality ).(2)( ω−π↔ ftF
Properties of Fourier Transform
p. 30
Frequency Shifting (Modulation)
)()( 00 ω−ω↔ω Fetf tj
Properties of Fourier Transform