september 9, 2004 ee 615 lecture 2 review of stochastic processes random variables dsp, digital comm...
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![Page 1: September 9, 2004 EE 615 Lecture 2 Review of Stochastic Processes Random Variables DSP, Digital Comm Dr. Uf Tureli Department of Electrical and Computer](https://reader034.vdocuments.site/reader034/viewer/2022051620/56649f225503460f94c3acd7/html5/thumbnails/1.jpg)
September 9, 2004
EE 615 Lecture 2 Review of Stochastic Processes Random Variables DSP, Digital Comm
Dr. Uf TureliDepartment of Electrical and Computer Engineering Stevens Institute of TechnologyHoboken NJ 07030
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September 9, 2004
Stochastic Processes Fundamentals Random Variables A mapping between a discrete or a
random event and a real number. (not a variable!)
Ensemble Average Average or Expected value behavior
of a random variable.
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September 9, 2004
Continous Random Variables Distribution function FX (a) of RV X is:
Probability density function fX(a)
fX(a) > 0
)Pr()( XFX
)()(
XX Ff
1)()()(
XXX FFf
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September 9, 2004
Discrete Random Variables and Probability
Random variable X assumes a value as a function from outcomes of a process which can not be determined in advance.
Sample space S of a random variable is the set of all possible values of the variable X.
: set of all outcomes and divide it into elementary events, or states
1)(}{
x
xp 0)(1 xp
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September 9, 2004
Expectation, Variance and Deviation
The moments of a random variable define important
characteristics of random variables:
The first moment is the expectation E[X]=<X>:
Note: The expectation has a misleading name and is not always the value we
expect to see most. In the case of the number on a dice the expectation is 3.5
which is not a value we will see at all!. The expectation is as a weighted average.
The variance is defined by Var[x] = <x2> - <x>2 = M2 - M12.
The standard deviation = Var[x]1/2 evaluates the “spread
factor”or x in relation to the mean.
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September 9, 2004
Ensemble Average Mean:
Continuous
Discrete
Variance
)( E XfXX
222 XX
k
kk XX )Pr(
Xx fXXX 222 E
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September 9, 2004
Correlation & Covariance Crosscorrelation
Covariance
If <X> or <Y> equal zero, correlation equals covariance
*XYEXYr
YXYYcXY** XYEXX-E
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September 9, 2004
Random Process X, Y need not be separate events X,Y can be samples of process
observed at different instants t_1, t_2 )()(tE),( 2
*121 tXXttRX
*2211
*21 )()()(t(E),( tXtXXtXttCX
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September 9, 2004
Independence vs. Uncorrelatedness R.V.s X, Y independent if
Uncorrelated (Weaker condition), when
R.V. X, Y uncorrelated if covariance is zero.
Independent R.V. always uncorrelated. Uncorrelated R.V. may not be independent!
)()(),( YXXY fff
*** EEE YXYXXYrXY
*** XYEXX-E YXYYcXY
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September 9, 2004
Random Processes Random process is a rule for assigning
every outcome of a probabilistic event to a function
Random process is an indexed sequence of R.V.s
R.P. is stationary in strict sense, if all statistics are time invariant
Wide Sense stationary if first and second order statistics are time invariant.
),( tX
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September 9, 2004
WSS Process Properties <X>=constant, For Gaussian process, WSS implies
strict stationarity For WSS:
XX RkkR ),(
2)(E)0(
allfor ),0()(
)()(
tXR
RR
RR
X
XX
XX
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September 9, 2004
MOdulation/DEModulation Modulation: Converting digital data into an
analog signal. Demodulation: Converting an analog signal
into digital data
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September 9, 2004
DIGITAL SIGNAL DISCRETE WAVEFORM TWO DISCRETE STATES:
1-BIT & 0-BIT ON / OFF PULSE
DATA COMMUNICATION USES MODEM TO TRANSLATE
ANALOG TO DIGITAL, DIGITAL TO ANALOG
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September 9, 2004
Digital Comm over Fading Channels Comm Theory 609: Design/
Performance of Digital Comm. In Additive White Gaussian noise
New: Linear Filter Channel with AWGN
Traditional Soln: Equalization Question: How should signals be
designed for complex channels?
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September 9, 2004
Statistical Characterization of Channels
Digital Comm. Proakis, 4th Edition, Ch.14 pp.800
Notice channel has time varying impulse response!
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September 9, 2004
Propagation Models Channel model provides reliable base
in system design and research For simulations and design, simple
model preferred In transmitter/receiver (transceiver)
design, not accurate but typical and worst case models most relevant
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September 9, 2004
Major Channel Effects Propagation Loss is attenuation,
also called path loss Time Dispersion: multiple
reflections due to obstacles leading to multipaths
Doppler Effects: Time variant nature due to mobility of objects in an environment
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September 9, 2004
Propogation Loss Free space propagation:
Loss (dB):S(d)=S_0+10a log_10 (d)+b, where a and b depend on operating frequency, environment, obstructed or direct line of sight, around 5 GHz, a=3.75, b=-6.5, such that for distances 10-50m, S=80-100 dB!
2
4
dGG
P
PRT
T
R
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September 9, 2004
Noise White Noise Interference: Narrowband Interference
KelvinJxkkN /1038.1, WT 230
Microwave Emission
Frequency Hopping
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September 9, 2004
Multipath Propagation Natural
obstacles, buildings, furnitures, etc.
Each path:delay, attenuation, phase shift
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September 9, 2004
Terminology Static Channel Impulse response k:path index, a_k:path gain,
theta_k:path phase shift, tau_k:path delay
1
0
)()(N
kk
jk teath k
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September 9, 2004
Power Delay Profile (PDP) PDP
RMS Delay Spread
)()(1
0
2
N
kkk tatP
1
0
2
1
0
2
1
0
2
1
0
22
2
,
)(
)(
,
)(
)(
N
kk
N
kkk
N
kk
N
kkk
RMS
RMS
a
a
a
a
dttP
dtttP
dttP
dttPt
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September 9, 2004
Coherence Bandwidth Autocorrelation of channel frequency response For class of channels with exponential delay profile, autocorrelation can be computed as a statistical expectation
Coherence BW:
dfffHfHfR )()()( *
fj
ffHfHfRRMS
21
1)()(E *
2
1
)0(
)(
cohBf
A
R
fR
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September 9, 2004
Flat vs Frequency Selective Fading For channel with exponential delay
spread
If BW > B_coh: Frequency selective fading
If BW < B_coh: Flat fading
)2/(1 RMScohB
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September 9, 2004
Effect of channel Transmitted signal fc:carrier frequency, j=sqrt(-1) Received signal
tfj
l
cetsts2
)( Re)(
dtsttx
ttsttx nn
n
)(),()(
)()()(
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September 9, 2004
Time Variant ChannelsCorrelation:
tR
dttththtR )()()( *
Coherence Time:
2
1
)0(
)(
cohTt
A
R
tR
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September 9, 2004
Doppler Spectra Doppler
Spectrum:
T_coh ~ 1/ f_d
dtetRfP ftjh
2)()(
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September 9, 2004
Example: OFDM Modulation
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September 9, 2004
Multicarrier Modulation DFT/FFT to generate subcarriers Real representation:
Tttttts
Ttttttfjdts
ss
sssTi
c
N
NNi
s
s
i
,,0)(
,)))((2exp(Re)( 5.012/
12/2/
Tttttts
Ttttttfjdts
ss
sssTi
c
N
NNi
s
s
i
,,0)(
,)))((2exp()(12/
12/2/
Complex:
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September 9, 2004
Demodulation
Tddtttjd
Ttttttfjdttj
s
s
s
s
s
i
s
s
i
s
s
Njs
Tt
t
Tji
N
NNi
sssTi
c
N
NNis
Tt
t
Ti
2/
12/
12/2/
12/
12/2/
))(2exp(
,)))((2exp())(2exp(
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September 9, 2004
IFFT for modulation N point transform N^2 operations
(complexity grows quadratically) NlogN complexity in the FFT/IFFT
(slightly faster than linear) Radix-4 butterfly
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September 9, 2004
FFT Implementation Decimation in Time
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September 9, 2004
Multicarrier System -Wireless Complex Transmission
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September 9, 2004
Wireline, Baseband Transmission
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September 9, 2004
Decimation Decimation in Time, vs Frequency
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September 9, 2004
Scalability-repetetive structure Partial FFT, if you use a subset of
transmitted carriers
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September 9, 2004
Cyclic Extension Transmission in frequency domain
(FFT) DFT properties
Signal and channel linearly convolved
Prefix and postfix extension
}{}{}{
}{}{}*{
nnnn
nnnn
hxDFTdDFThdDFT
hFTdFThdFT
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September 9, 2004
Cyclic Prefix Make the convolution linear Filtering:
Cylic Prefix and Removal makes linear convolution into Circular convolution
)()()(*)()( knxkhnhnxny
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September 9, 2004
Time/ Frequency Domain -Processing Why not equalize in frequency
domain? Stu Schwartz (Princeton) Hikmet Sari (France Telekom)
(w/cylic prefix) Falconer (Carleton)