exercise.1 8 pam – ber/ser monte carlo simulationsignals – matlab exercise 0 y1 y2 signal...
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Exercise.1 8 PAM – BER/SER Monte Carlo Simulation
- Simulate a 8 level PAM communication system and calculate bit and symbol error ratios (BER/SER).- Plot the calculated and simulated SER and BER curves. - Plot the theoretical SER and BER curves. - Calculate the average energy.- Plot the constellation and histogram of samples.
Uniform random number generator
Gray-encodesymbols
Detector+
GaussianNoise
Compare symbols(I = I_hat) ?
Compare bits
Symbol Error Ratio
Bit Error Ratio
X Y
S_hat
Block Diagram
Exercise.1 8 PAM – BER/SER Monte Carlo Simulation
S
Modulator
I
Gray-decoderI_hat
Gray encoding
0 1 3 2 6 7 5 4
000 001 011 010 110 111 101 100
8 PAM System – Signal constellation
-7d -5d -3d -1d 0 1d 3d 5d 7d
Symbols
Bits in symbol
Amplitude
d : scaling
Gray encoding: Slide 65 (Lecture_3_2007_HypothesisTesting_SignalSpace.pdf)
Average transmitted signal energy
∑ ∫=
=
M
k
T
kav dtttsM
E1
2
0
)()(1 φ
S0(t) S1(t) S2(t) S3(t)
S4(t) S5(t) S6(t) S7(t)
T
E7− T
E5− T
E3−T
E−
T
ET
E3 T
E5T
E7
8 PAM System – Derivation of standard deviation
T
T
t
t
( ) 222228
1
2
0
2121135728
1)()(
8
1dEEdtttsE
k
T
kav ==+++=
= ∑ ∫
=
φ
T
1
)(functionBasis tφ
Tt
T T T
3log2_
avavbitav
E
M
EE ==
2
2
2
2
00
_
2
7
23
21
3 σσdd
N
E
N
ESNR avbitav
bit =⋅
===
bitSNR
d
2
7 2
=σ
Average bit SNR
Transmitted average energy per bit
Standard deviation used in the simulation
8 PAM System – Derivation of standard deviation
EbNoNo
bE
N
bE
N
bitavE
bitSNR ====
00
_
Note that some books use different notation
8 PAM System – Theoretical symbol error probability
SEP = (2*(M-1)/M)*qfunc(sqrt((6*log2(M)/(M^2-1))*SNR_bit_abs));
M = 8
SEP: Slide 63 (Lecture_3_2007_HypothesisTesting_SignalSpace.pdf)
0 2 4 6 8 10 12 14 1610
-4
10-3
10-2
10-1
100
Symbol error probabilities. 8 PAM
Eb/No
Sym
bol E
rror
Pro
babi
lity
theoretical
simulation
[db]
BEP = (1/log2M) SEPapproximation for low BEP :
8 PAM System – Theoretical bit error probability
BEP: Slide 68 (Lecture_3_2007_HypothesisTesting_SignalSpace.pdf)
0 2 4 6 8 10 12 14 1610
-4
10-3
10-2
10-1
100
Bit error probabilities. 8 PAM
Eb/No
Bit
Err
or P
roba
bilit
ytheoretical
simulation
[db]
Histogram of symbols
-4 -2 0 2 4 6 8 10 12 140
200
400
600
800
1000
1200
1400
-Download the file from the course web page and open with an editor
STEP 1 : transform SNR [dB] to SNR [abs]
STEP 2: complete standard deviation for 8 PAM
STEP 3: generate N integer random numbers in the interval [0,7]and verify the distribution with a histogram
8 PAM System – Matlab exercise
STEP 4 : Add noise to symbols, use randn()
STEP 5: Calculate the symbol error ratio
STEP 6: Calculate the bit error ratio
STEP 7: Repeat the simulation for different SNR
STEP 8: Plot using the code in the file. Observe:- symbol error probabilities and compare to theoretical results- bit error probabilities and compare to theoretical results- constellation- histogram of the samples
STEP 9: Observe the average energy
STEP 10:Optional. Modulate using pammod() from the Communications Toolbox
8 PAM System – Matlab
Exercise 2 BPSK / QPSK – BER/SER Simulation
-Simulate a communication system using BPSK/QPSK modulation and calculate bit and symbol error ratios.
- Set BPSK modulation.- Plot the calculated and simulated SER and BER curves. - Plot the theoretical SER and BER curves. - Change modulation to QPSK- Plot the calculated and simulated SER and BER curves. - Plot the theoretical SER and BER curves. - Introduce a rotation in the channel and equalize the channel.
Exercise 2 BPSK / QPSK – BER/SER Simulation
Uniform random number generator
Gray-encodesymbols
Detector+
GaussianNoise
Compare symbols(I = I_hat) ?
Compare bits
Symbol Error Probability
Bit Error Probability
X YS_hatS
Modulator
I
Gray-decoderI_hat
Block Diagram
savbitav EE __ =
220
_
0
_
22 σσEE
N
E
N
E
No
EbSNR savbitav
bit =====
Transmitted average energy per bit
SNR bit
bitSNR
E
2=→ σ
Symbol Error Probability = Bit Error Probability in BPSK (1bit=1symbol)
( )bitb SNRQP 2=
Theoretical Bit Error Probability for BPSK
BPSK
( )bits SNRQP 2=
2log_
2
__
savsavbitav
E
M
EE ==
220
_
0
_
4222 σσEE
N
E
N
E
No
EbSNR savbitav
bit =⋅
====
Transmitted average energy per bit
SNR bit
bitSNR
E
4=→ σ
Approximation of Theoretical Symbol Error Probability for M >= 4
⋅⋅=M
SNRQP bits
πsin22
( )bitb SNRQP 2=
Theoretical Bit Error Probability for BPSK = QPSK
QPSK
4=M
-Download the file from the course web page and open with an editor
STEP 1 : Set modulation to BPSK
STEP 2: Gray-encode symbols, use bin2gray()
STEP 3: Demodulate PSK by using pskdemod()
STEP 4: Plot using the code in the file. Observe:- symbol error probabilities and compare to theoretical results- bit error probabilities and compare to theoretical results
STEP 5: Change modulation to QPSK
STEP 6: Introduce a rotation in the channel
STEP 7: Equalize the channel
BPSK/QPSK – Matlab exercise
STEP 8 : Plot using the code in the file. Observe:- The symbols rotated- The symbols rotated + noise- The symbols after the equalizer
BPSK/QPSK – Matlab exercise
0 1 2 3 4 5 6 7 8 9 1010
-6
10-5
10-4
10-3
10-2
10-1
100
Symbol/Bit error probabilities. BPSK
Eb/No
Bit
Err
or P
roba
bilit
y
SER theoretical
SER simulationBER theoretical
BER simulation
-1.5 -1 -0.5 0 0.5 1 1.5-1.5
-1
-0.5
0
0.5
1
1.5Rotated Signal Constellation with Noise
real
imag
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2-2
-1.5
-1
-0.5
0
0.5
1
1.5Signal Constellation after Equalization
real
imag
0 1 2 3 4 5 6 7 8 9 1010
-6
10-5
10-4
10-3
10-2
10-1
100
Eb/No
Bit
Err
or P
roba
bilit
y
Symbol/Bit error probabilities. QPSK
SER theoretical
SER simulationBER theoretical
BER simulation
BPSK QPSK
QPSK QPSK
[db][db]
Exercise 3 System using Biorthogonal Signals
-Simulate a communication system using biorthogonal waveforms and calculate bit and symbol error ratios.
-Plot the calculated and simulated SER and BER curves. -Modify the simulator to use orthogonal waveforms.-Plot the calculated and simulated SER and BER curves.
Exercise 3 System using Biorthogonal Signals
T/2
S0(t)
T
E2
T
E2−
t T/2
S1(t)
T
E2
T
E2−
T t
T/2
S2(t)
T
E2
T
E2−
t
T/2
S3(t)
T
E2
T
E2−
T
t
101
000
311
210
S0(t)
S1(t)
S2(t)
S3(t)
[ 1 0]
[ 0 1]
[-1 0]
[ 0 -1]
0
1
3
2
Symbols
Signal constellation
System using Biorthogonal Signals
220
_
0
_
4222 σσEE
N
E
N
E
No
EbSNR savbitav
bit =⋅
====
� Transmitted average energy per bit
� Average transmitted signal energy
EE sav =_
� SNR bit
NoEb
E
/4=→ σ
2log_
2
__
savsavbitav
E
M
EE ==
Y10
1
3
2
Y2
System using Biorthogonal Signals - Detector
Possible approach to make decisions
if (y1 > abs(y2), S = 0;elseif (y2 > abs(y1), S = 1;elseif (y1 <-abs(y2), S = 3;else S = 2;
y1
y2 Received symbol
Decision Areas
Signal constellation
Biorthogonal Signals – Matlab exercise
SER / BER
0 1 2 3 4 5 6 7 8 9 1010
-5
10-4
10-3
10-2
10-1
100
Eb/No
Sym
bol/B
it E
rror
Pro
babi
lity
Symbol and bit error probabilities. Biorthogonal waveforms M = 4
SER
BER
Task : Download the file from the course web page and open with an editorThis code simulates biorthogonal signals and is ready to run.Compare the Symbol and Bit error probabilities obtained here to the results obtained in the QPSK exercise.
[db]
.Orthogonal Signals – Matlab exercise
Y10
Y2
Signal constellation
Task : The code provided simulates biorthogonal signals.Modify the code to simulate the orthogonal signals shown below (M = 2).
[ 1 0]
[ 0 1]T/2
S0(t)
T
E2
T
E2−
t T/2
S1(t)
T
E2
T
E2−
T t
S0(t)
S1(t)
STEP 1: Set M = 2
STEP 2: Change standard deviation
STEP 3: Set modulation
STEP 4: Set the decision areas
STEP 5: Observe symbol and bit error rates
STEP 6: Plot theoretical bit error probabilities,for orthogonal and biorthogonal cases.Observe 3dB difference.
0 2 4 6 8 10 1210
-8
10-7
10-6
10-5
10-4
10-3
10-2
10-1
100
Symbol and bit error probabilities.
Eb/No
Sym
bol/B
it E
rror
Pro
babi
lity
SER
BERBER Theoretical Orthogonal
BER Theoretical Biorthogonal
.Orthogonal Signals – Matlab exercise
[db]
Consider a four-phase PSK represented by the following equivalent low pass signal:
Exercise 4 Power Spectrum Density
( ) ( )n
n
u t I g t nT= −∑
where nI takes one of the four possible values: ( )1 12 j± ± with equal probability.
a. Determine and sketch the power spectrum density of( )u t when:
( ) 0
0
A t Tg t
otherwise
≤ ≤=
b. Repeat a. when
( ) sin 0
0
tA t T
g t T
otherwise
π ≤ ≤ =
c. Compare the spectra obtained in a. and b. in terms of the 3dB bandwidthand the bandwidth to the first spectral zero.
( ) ( )sinj fT
ftG f AT e
ftπ
π
π−=
Exercise 4
In PAM the coefficients are real but in PSK this is not the case since the signal space isnI
not uni-dimensional.
( ) 0
0
A t Tg t
otherwise
≤ ≤=
FT
( ) ( )( ) ( ) ( )
( )2 22
2 2 22 2
sin sinuu
ft ftG f A T f A T
ft ft
π π
π π= ⇒Φ =
The power spectrum density is calculated as:
First Zeroat 1/T
-5 -4 -3 -2 -1 0 1 2 3 4 510
-6
10-5
10-4
10-3
10-2
10-1
100
Spectrum for Rectangular pulse
Normalized frequency fTb
Nor
mal
ized
Pow
er s
pect
rum
den
sity
a).
fT
fTATfG
ππ )sin(
)( =
2)(
1)( fG
Tfuu =Φ
Exercise 4
( ) sin 0
0
tA t T
g t T
otherwise
π ≤ ≤ =
FT ( ) ( )2 2
cos2
1 4j Tf
TfATG f e
T fπ
π
π−= ⋅
−
b).
The power spectrum density is calculated:
( ) ( ) ( )( )
2 22
22 2
cos1 2
1 4uu
TfAf G f T
T TT f
π Φ = =
−
First Zero at 1.5/T
-5 -4 -3 -2 -1 0 1 2 3 4 510
-5
10-4
10-3
10-2
10-1
100
Spectrum for Sinusoid pulse
Normalized frequency fTb
Nor
mal
ized
Pow
er s
pect
rum
den
sity
Exercise 4
c).
The power spectrum for the rectangular pulse has narrower mainlobe but higher sidelobes
-5 -4 -3 -2 -1 0 1 2 3 4 5
10-4
10-3
10-2
10-1
100
Spectrum for Rectangular and sinusoid pulse
Normalized frequency fTb
Nor
mal
ized
Pow
er s
pect
rum
den
sity
RectangularSinusoid The sinusoid pulse demands 50% more bandwidth
compared to the rectangular pulse
( )( )
( )( )
23
32
3
2
322 2
sin 1 0.44rectangular:
2
cos 1 0.59sinusoid :
21 4
db
dB
db
dB
f Tf
Tf T
T ff
TT f
π
π
π
= ⇒ =
= ⇒ =−
Exercise 4
• To determine the power spectrum density we used the Fourier transform of a proposed signal.• But the Fourier transform is available only for deterministic signals.• With random message signals we can not find the Fourier transform.• Nevertheless we can determine the autocorrelation function of these signals from their statistical information.• Then we find the power spectrum density from the Fourier transform of the autocorrelation function:
{ })()( τψℑ=Φ fuu
Where ψ(τ) is the autocorrelation function
dttutu )()()( *∫∞
∞−
+= ττψ
In Matlab we use xcorr(x) (crosscorrelation of vector x with itself)
Exercise 4
TASK : Simulate a communication system for the given problem usingMatlab and determine the Power Spectrum Density when g(t)
is a rectangular and sinusoidal pulse.