Chapter 9: Random Signals and Noise A. Bruce Carlson Paul ...
Transcript of Chapter 9: Random Signals and Noise A. Bruce Carlson Paul ...
Communication Systems, 5e
Chapter 9: Random Signals and Noise
A. Bruce CarlsonPaul B. Crilly
© 2010 The McGraw-Hill Companies
Chapter 9: Random Signals and Noise
• Random processes• Random signals• Noise• Baseband signal transmission with noise• Baseband pulse transmission with noise
© 2010 The McGraw-Hill Companies
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Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
Waveforms in an ensemble v(t,s)Figure 9.1-1
Ensemble
Example 9.1-1 Random Phase
• The phase at which a cosine wave is received is assumed to be a uniformly distributed random variable.
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tftv c2cos
20,21
p
dptftvE c2cos
2
0
222
0 21
212cos
21 deedtftvE jtfjjtfj
ccc
2
0
22
21
21
jee
jeetvE
jtfj
jtfj cc
0111121
21 22 tfjtfj cc ee
jtvE
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Random Digital Signal
• A random digital waveform based on a rectangular pulse train with bit/symbol period D– The time delay is a continuous RV uniformly distributed between 0
and the pulse width D– The amplitude of the kth pulse is a discrete RV with zero mean and
a known variance. – The amplitudes during different intervals are independent (i.i.d)
k
dk D
DkDTtrectatv 2
DT0,D1Tp dd
kjfor,0aaE
aE,0aE
kj
22nn
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Random Digital Signal
• Autocorrelation
• Power Spectral Density
DD,D
1tvtvER 2vv
DfcsinDtvtvEwS 22vv
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(a) Sample function; (b) Autocorrelation; (c) Power spectrum: Figure 9.2-3
Random Telegraph: Example 9.2-1
• Poisson distribution– Average number of
transitions per unit time– DC component
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Random Telegraph: Example 9.2-1
• Poisson distribution
t2exp14
AR2
vv
4A
4A
4A2
2exp14
A02exp14
A
tvEtvE
2222
vv
222
vv
222vv
f4
A
f14
AfS2
2
2
vv
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Modulation
• Typical receivers have a random phase offset in the carrier. Uniform distribution.
tf2costvtz c
20,21p
cvvzz f2cosR21R
cvvcvvzz ffSffS41fS
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Thermal Noise Power
• Noise produced by the random motion of charge particles in conducting media
• Modeled as additive white Gaussian noise (AWGN)
– Where κ is Boltzmann’s constant– T is absolute temperature in degrees Kelvin– B is the bandwidth in Hertz
BTN
HzK/dBW6.228
refIEEEK290T0
21e00.429023e38.1TN 00
Hz/dBm174Hz/dBW204N0
Thermal Resistance Noise
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(a) Thevenin Voltage Model
(b) Norton Current Model
Resistor Models with Noise PSD
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Noise Approximation
• Uniform Noise Spectral Density– Resistor description (Thevenin Model)
• Available Power from the “noise source”– Source output power into a matched load
TR2fGvv
ssout vR2
Rv
R4
vR1
2v
RvP
2s
2s
2sout
sout
2
N2T
R4TR2
R4fGfG 0vv
ss
2
NR 0ss
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System Noise
• Since the noise power spectrum is uniform, a systems noise power is the product of the noise power and the integral of the filter power.
20NN
2NN fH
2NfSfHfS
00
0
20
20NN dffHNdffH
2N0R
Noise Equivalent Bandwidth
• If we want the total noise power after the filter, we can integrate the PSD for all frequencies or use the Filtered noise autocorrelation function at zero.– Both of these approaches may be difficult– Could we generate a more simple “noise equivalent
bandwidth for filters” that is rectangular?
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17
EQNEQNPowerDCelrect BHBGaindffHdffH
2_
0
2
mod_0
2 0
EQNPowerDCelrect B
frectGainfH2_mod_
20
2
EQN0H
dffHB
2Power_DC 0HGain
Noise Equivalent Bandwidth
0
2020NN dffH2
2NdffH
2N0R
• When filtering, it is convenient to think of band-limited noise, where the filter is a rect function with bandwidth BEQN
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Noise Equivalent Bandwidth
• Low pass filter 0Hgain_coherent
• For a unity gain filter – assumed when computing receiver input noise power
EQN0EQN0
NNN BNB22
N0RP
2Power_DC 0HGain
20
2
EQN0H
dffHB
0
2EQN dffHB
EQN02
EQN20
NNN BN0HB0H22
N0RP
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Analog baseband transmission system with noise: Figure 9.4-2
Signal Plus Noise
• Additive Gaussian White Noise
ttf2costAtx c
ttfLtAtx cR 2cos tnttf
LtAts c 2cos
thtnttfLtAteD c
2cosPr
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Signal-to-Noise Ratio
• Comparing the desired signal power to the undesired noise power.
• To compare signal and noise power, we must assume a filtering operations
tntxty c
TransmittingAntenna
ReceivingAntenna
RF Communication Channel
Noise
Linear Filtering
NonlinearDistortion
Atten-uation
tn
txc
tntxty c
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Signal-to-Noise Ratio
• Equivalent receiver input signal and noise (ER)
• Equivalent destination signal and noise (D) or pre-demodulation (PreD)
thtntxty RD
tntxty ERERR
tntxty eDeDeD PrPrPr
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Signal-to-Noise Ratio
• Equivalent receiver input SNR (ER)
EQN
R
EQN
ER
ER
ERR BN
SBNtxE
tnEtxESNR
00
2
2
2
• Equivalent destination SNR EQN
D
D
D
eD
eDR BN
SNS
tnEtxESNR
02
Pr
2Pr
can be used to represent receiver noise figure contributions
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Increase in SNR with filtering
• If a filter matched to the input signal is applied, the noise power would be reduced to the smallest equivalent noise bandwidth that is allowed.– Filter to minimize noise power– Importance of the IF filter in a super-het receiver!
• Front-end filtering goals – a dilemna– Minimize signal power loss (wider bandwidth)– Minimize filter equivalent noise bandwidth
(narrower bandwidths)– A trade-off must be made!
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Typical Transmission Requirements
Signal Type Freq. Range SNR (dB)Intelligible Voice 500 Hz to 2 kHz 5-10Telephone Quality 200 Hz to 3.2 kHz 25-35AM Broadcast Audio 100 Hz to 5 kHz 40-50High-fidelity Audio 20 Hz to 20 kHz 55-65Video 60 Hz to 4.2 MHz 45-55Spectrum Analyzer 100 kHz-1.8 GHz 65-75
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Pulse Measurement in Noise 2
0
Let received pulse be a rectangle with amplitude, duration, energy, and zero mean noise with PSD ( ) / 2
LPF 1/ 2
At the filter output ( ) ( )where
p
N
a a A
A
A E AG f N
B B
y t A n t Aamplitude
2 20
22 0 0Lower bound on error variance
2 2
Achieving lower bound
A N
Ap
error
n N B
N N AE
matched filter
AAy
Note: y is a random variable with a variance and pdf
Time-position Error Caused By Noise
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• Time error rv relation
• Time error variance
222
br
tt tnAtEE
At
tnr
b
t
Time-position Error Caused By Noise (2)
• Time Position Error Variance
• Matching the transmit bandwidth
• Therefore
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Nr
br
tt BNAttn
AtEE
0
2222
pEA 2
Tr Bt
21
NTTp
Nt BBfor
BEBN
,4 2
02
NTTTp
t BBforBA
NBE
N
,44 2
002
tmeasureedge tt