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Digital Transmission through the AWGN Channel

ECE460 Spring, 2012

Geometric Representation

Orthogonal Basis 1. Orthogonalization (Gram-Schmidt) 2. Pulse Amplitude Modulation a. Baseband b. Bandpass c.

Geometric Representation 3. 2-D Signals a. Baseband b. Bandpass 1) Carrier Phase Modulation (All have same energy) 1) Phase-Shift Keying 2) Two Quadrature Carriers 2) Quadrature Amplitude Modulation 4. Multidimensional a. Orthogonal 1) Baseband 2) Bandpass b. Biorthogonal 1) Baseband 2) Bandpass 2

Geometric Representation

Gram-Schmidt Orthogonalization 1. Begin with first waveform,

s

1 (

t

) with energy ξ 1:  1  1    1 2. Second waveform a. Determine projection,

c

21,

c

21    

s

2 onto ψ 1 b. Subtract projection from

s

2 (t)

d

2 

s

2 

c

21  1 c.

Normalize  2 

d

2  2 where  2    

d

2 2 3. Repeat

c ki

   

s k d k

k

s k

d k

k

k i

 1   1

c ki

i

where 

k

   

d k

2 3

Example 7.1

4

Pulse Amplitude Modulation

Baseband Signals

Binary PAM • • Bit 1 – Amplitude +

A

Bit 0 – Amplitude

- A M-ary

PAM

M

 2

k s m

m

A g m T

   

T

2

s m A

2

m

T A

2

m

g g T

2 M-ary PAM Binary PAM Fixed

R b

 1

T b

k kT

5

u m

Pulse Amplitude Modulation

Bandpass Signals

Baseband Signal

s m

X Bandpass Signal

s m

cos 2   

A g m T

   cos 2 

f t c

m

 1, 2, ... ,

M U m

A m

2

G T

f

f c

 

G T

f

f c

What type of Amplitude Modulation signal does this appear to be?

m

   

u m

2     

A

2

m A

2

m

2

t g T

2   

g T

2 cos 2  2 

c

 

A

2

m

2   

g T

2 cos 4 

c

 6

PAM Signals

Geometric Representation

M-ary PAM waveforms are one-dimensional

s m

s m

m

 1, 2,...,

M

where 

s m

  1 

g g T

g A m

0

T m

 1, 2,...,

M d d d

0

d d

d = Euclidean distance between two points 7

Optimum Receivers

Start with the transmission of any one of the

M-ary

waveforms: g

M

 

s m

,

m

signal  1, 2,...,

M

 g g Transmitted within timeslot 0 Corrupted with AWGN:   

s m T

s m

Demodulator  Sampler 1 , ,..., 2

r N

 Detector

r

s m

Output Decision 1. Demodulators a. Correlation-Type b. Matched-Filter-Type 2. Optimum Detector 3. Special Cases (Demodulation and Detection) a. Carrier-Amplitude Modulated Signals b. Carrier-Phase Modulation Signals c.

Quadrature Amplitude Modulated Signals d. Frequency-Modulated Signals 8

Demodulators

Correlation-Type r k

  0

T

  0

T s m

  0

T

s mk s m

n k

k

  0

T k

 1, 2,...,

N

r

s

m

n

Next, obtain the joint conditional PDF

f

m

   

N

0 1 

N

/ 2 exp  

k N

  1 

r k

s mk

 2 /

N

0     

N

0 1 

N

/ 2 exp 

r s

m

2 /

N

0

m

 1, 2,...,

M

9

Demodulators Matched-Filter Type

Instead of using a bank of correlators to generate {

r

k }, use a bank of

N

linear filters.

The Matched Filter

Key Property: if a signal

s

(

t

) is corrupted by AGWN, the filter with impulse response matched to

s

(

t

) maximizes the output SNR Demodulator 10

Optimum Detector

Decision based on transmitted signal in each signal interval based on the

Maximum a Posterior Probabilities (MAP)

P

 signal

s

m

was transmitted |

r

m

 1, 2,...,

M

m

|

r

 

f m N

  1

f

r

|

s m

  

m

m

s

m

 

m

the denominator is a constant for all

M

, this reduces to

M

s m

 and

D

D

 

m

 

k N

  1 

r k

s mk

 2

m

 2

r s

m

s

m

2 minimum distance detection minimize

C

m

  2

r s s

m

2 maximize (correlation metric) 11

Probability of Error

Binary PAM Baseband Signals

Consider binary PAM baseband signals   1    

s

2 

g T

interval and zero elsewhere. This can be pictured geometrically as  

b

b s

2 0

s

1 Assumption: signals are equally likely and that

s

1 transmitted. Then the received signal is was

r

   1 

b

n

Decision Rule:

r s

1

s

 2 0 The two conditional PDFs for

r

are  |  | 2 1      1

N

0 e 1 

N

0 e   

b

 2 /

N

0 

b

 2 /

N

0 12

Example 7.5.3

Consider the case of binary PAM signals in which two possible       the metrics for the optimum MAP detector when the transmitted signal is corrupted with AWGN.

13

Probability of Error

M-ary PAM Baseband Signals

Recall baseband

M-ary

PAM are geometrically represented in 1 D with signal point values of

s m

 

g A m m

 1, 2,...,

M

And, for symmetric signals about the origin,

A m

  2

m M

m

 1, 2,...,

M

g

Each signal has a different energies. The average is  

av P av

    1

M

g M

g m M

  1 

m m M

  1  2

m

cos  1  2  1  

M M

av T

2 3  1  3 

g

 

M

2 3  1  

M

g T

 2 14

Demodulation and Detection

Carrier-Amplitude Modulated Signals

Demodulation of bandpass digital PAM signal Received Signal

r

(

t

) Oscillator Transmitted Signal:

u m

  

A g m T

Received Signal:    wh er e    cos 2  cos 2 

T

     

f t c

 0    2 

T

 0 Crosscorrelation  0

T

A m

A m

Optimum Detector o r   2 

g

 0

T g T

2 

g

2 

n

cos 2  2 

c

m m

 

r

s m

2

r s m

 2 

s m

2   0

T T

15

Two-Dimensional Signal Waveforms

Baseband Signals •

Are these orthogonal?

Calculate ξ.

Find basis functions of (b).

16

Problem 7.22

In an additive white Gaussian noise channel with noise power-

N

spectral density of , two equiprobable messages are transmitted by 1 2    

At T

0, , 0

T

otherwise

s

2    

A

0, 1 

t T

  ' 0

T

otherwise 1. Determine the structure of the optimal receiver 2. Determine the probability of error.

17

Two-Dimensional Bandpass Signals

Carrier-Phase Modulation 1. Given M-two-dimensional signal waveforms

s m

  ,

u m

s m

cos 2   0

T m

 1, 2,...,

M

2. Constrain bandpass waveforms to have same energy 

m

T

0 

u m

2 

T

0 

s m

2   1 2 

s T

0 

s

2

m

m

cos 2  2   1 2

c

T

0 

s

2

m

cos 4 

c

 18

Demodulation and Detection

Carrier-Phase Modulated Signals

The received signal:   [

u m

   

T

t n t

      [

T

     wher e

m

 0,1,...

M

 1 Giving basis vectors as  1   2

g g T

cos 2 

f t c

 2   2 

g g T

sin 2 

f t c

Outputs of correlators:

r

  

s m

n

s

cos 2  

n c

, 

s

sin 2    

n s

 19

Two-Dimensional Bandpass Signals

Quadrature Amplitude Modulation

u m

T

     

T

sin 2  

m

 1, 2,...,

M

20

Multidimensional Signal Waveforms

Orthogonal

Multidimensional means multiple basis vectors • •

Baseband Signals

Overlapping (Hadamard Sequence) Non-Overlapping o Pulse Position Mod.

(PPM)

s m

A g T

t

 

m

 1  where

m

m

 1, 2,...,  1 

M

 21

Multidimensional Signal Waveforms

Orthogonal

Bandpass Signals

As before, we can create bandpass signals by simply multiplying a baseband signal by a sinusoid:

u m

s m

   

f t c

 0

T

Carrier-frequency modulation: Frequency-Shift Keying (FSK)

u m

 2 

b T

   2 

m

f t

m

 0,1,...,

M

, 0

T

mn

   1

s T

u m

0 sin 2  2  

m

m

 

n

n

 

f T

f T

22

Multidimensional Signal Waveforms

Biorthogonal

Baseband

Begin with

M

/2

s

1

s

2 orthogonal vectors in

N

=

M

/2     0, 

s

, 0, 0,..., 0 

s

, 0,..., 0   dimensions.

s

M

/ 2   0, 0, 0,..., 

s

 Then append their negatives

s

M

 1 2 

s

, 0, 0,..., 0 

s

M

  0, 0, 0,...,  

s

Bandpass

As before, multiply the baseband signals by a sinusoid.

23

Multidimensional Signal Waveforms

Simplex

Subtract the average of M orthogonal waveforms

s

m

s m

s

 

T

0 

s

m

 1

M k M

  1

s k

2

dt

  1  1

M

s

In geometric form (e.g., vector)

s

 

m

s

m

 1

M k M

  1

s

k

Where the mean-signal vector is

s

 1

M k M

  1

s

k

Has the effect of moving the origin to reducing the energy per symbol 

s

 

s

m

2

s

s

m

s

  1  1

M

2 

s

24

Demodulation and Detection

Carrier-Amplitude Modulated Signals

Demodulation of bandpass digital PAM signal Received Signal

r

(

t

) Oscillator Transmitted Signal:

u m

  

A g m T

Received Signal:    wh er e    cos 2  cos 2 

T

     

f t c

 0    2 

T

 0 Crosscorrelation  0

T

A m

A m

Optimum Detector o r   2 

g

 0

T g T

2 

g

2 

n

cos 2  2 

c

m m

 

r

s m

2

r s m

 2 

s m

2   0

T T

25