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1 Chapter 3 Digital Communication Fundamentals for Cognitive Radio Cognitive Radio Communications and Networks: Principles and PracticeBy A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

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Chapter 3. Digital Communication Fundamentals for Cognitive Radio. “ Cognitive Radio Communications and Networks: Principles and Practice ” By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009). 1. Outline. Fundamental of Data Transmission Digital Modulation Techniques - PowerPoint PPT Presentation

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Page 1: Chapter 3

1

Chapter 3

Digital Communication Fundamentals for Cognitive Radio

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

Page 2: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

2

Outline

Fundamental of Data Transmission Digital Modulation Techniques Probability of Bit Error Multicarrier Modulation Multicarrier Equlization Intersymbol Interference Pulse Shaping

Page 3: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

3

Data Transmission

Anatomy of a wireless Digital Comm Sys

Page 4: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

4

Data Transmission

Fundamental Limits Shannon–Hartley theorem

S is the total received signal power over the bandwidth (in case of a modulated signal, often denoted C, i.e. modulated carrier), measured in watt or volt; N is the total noise or interference power over the bandwidth, measured in watt or volt; and S/N is the signal-to-noise ratio (SNR) or the carrier-to-noise ratio (CNR) of the communication signal to the Gaussian noise interference expressed as a linear power ratio (not as logarithmic decibels).

Page 5: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

5

Data Transmission

Source of Transmission Error

The linear filter channel with additive noise

Page 6: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

6

Data Transmission

Additive White Gaussian Noise (AWGN) White noise assumption in most situations Gaussian pdf

Page 7: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

7

Data Transmission

Additive White Gaussian Noise (AWGN)

Histogram of AWGN superimposed on the probability density function for a Gaussian random variable of N(0,0.25)

Power spectral density of AWGN with N(0,0.25)

Page 8: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

8

Data Transmission

Nearest neighbor detection

AWGN N(0,0.01) AWGN N(0,0.25)

Page 9: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

9

Digital Modulation Techniques

Representation of Signals Euclidean Distance between Signals Decision Rule Power Efficiency M-ary Phase Shift Keying M-ary Quadrature Amplitude

Modulation

Page 10: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

10

Representation of Signals

Page 11: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

11

Euclidean Distance between Signals For any two signals and , the

Euclidean Distance between them is defined as

Vector form

)(tsi )(ts j

dttstsd jiij22 )()(

jiij ssd

Page 12: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

12

Decision Rule

Nearest neighbor rule: Determine is if is the closest to

rewritten as

waveform representation

r

is

ijsrsr ji ,

is

r

ijsrsr ji ,

T

j

T

i dttstrdttstr00

)()()()(

Page 13: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

13

Power Efficiency

Measures the largest minimum signal distance achieved given lowest transmit power Energy per symbol

Energy per bit

dttsPEM

ii

i

s

1

0

2 )(

M

EE sb

2log

Page 14: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

14

Power Efficiency

Power efficiency

bP E

d 2min

Page 15: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

15

M-ary Phase Shift Keying

1,,1,0),2

cos()(

Mmm

ttAts ci

Page 16: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

16

M-ary Quadrature Amplitude Modulation

Page 17: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

17

M-ary Quadrature Amplitude Modulation

Page 18: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

18

M-ary Quadrature Amplitude Modulation Receiver

Simple receiver structure makes M-QAM popular in digital communication systems.

Page 19: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

19

Probability of Bit Error

Also known as bit error rate Starting from the modulation scheme

with two signal waveforms, and Assuming the noise term has a

Gaussian distribution

)(1 ts )(2 ts

);0(~)( Ntn

Page 20: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

20

Derivation of BER

Assuming was transmitted, then an error event occurs if

where

)(1 ts

dttstrdttstrTT

)()()()( 2010

)()()( 1 tntstr

Page 21: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

21

Derivation of BER

P(error)=

is the noise density is the correlation between and

Q-function defines the area under the tail of a Gaussian pdf

x

y dyexQ 2/2

2

1)(

0

12

N

EQ

0N

12 )(1 ts )(2 ts

Page 22: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

22

Derivation of BER

When ,

and therefore

0

1221

2

2

N

EEQ

21 ss EE

2min1221 2 dEE

0

2min

2N

dQPe

Page 23: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

23

Upper Bound on BER

At least one error occurs

Therefore

j

m

j

m

12

1141312

m

j

ij

e N

dQP

2 0

2

2

Page 24: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

24

Lower Bound on BER

The smaller , the more likely an error happens.

Therefore

0

2min

2N

dQPe

2ijd

Page 25: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

25

Multicarrier modulation

Advantages:•Transmission agility•High datarate•Immunity to non-flat fading response.•“divide-and-conquer” channel distortion•Adaptive bit loading

Page 26: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

26

Multicarrier modulation

Disadvantages:•Sensitive to narrowband noise•Sensitive to amplitude clipping•Sensitive to timing jitter, delay

Page 27: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

27

Multicarrier Modulation

Transmitter

Receiver

Page 28: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

28

Comparison between single carrier and multicarrier tt

Page 29: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

29

OFDM

Orthogonal Frequency Division Multiplexing

Page 30: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

30

FB-OFDM

Orthogonal Frequency Division Multiplexing with Filterbank

FB-MC subcarrier spectrum employing a seuqre-root raised cosine prototype lowpass filter with a rolloff of 0.25

Page 31: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

31

OFDM

Transmitter

Receiver

Page 32: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

32

Multicarrier Equalization

Interference in multicarrier systems

Page 33: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

33

Distortion Reduction

Channel coding•Add redundancy to improve recovering

Page 34: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

34

Distortion Reduction

Channel equalization

Before equalization

After equalization

Page 35: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

35

Distortion Reduction

Channel equalization•Pre-equalization requires a feedback path from the receiver

Page 36: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

36

Intersymbol Interference

Signal being distorted by the temporal spreading and resulting overlap of individual symbol pulses

Simplified block diagram for a wireless channel

Page 37: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

37

Intersymbol Interference

RC-LPF filter response of combined modulation pulses when T=1, A=1

Page 38: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

38

Intersymbol Interference

Peak Interference/Distortion•When all symbols before and after have destruct effect on the symbol on cursor

Page 39: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

39

Pulse Shaping

Nyquist Pulse Shaping Theory•Time domain condition for zero ISI

No ISI if sampling at t=kT

Page 40: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

40

Nyquist Pulse Shaping Theory

Nyquist-I Pulse (W=1/(2T))

Page 41: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

41

Nyquist Pulse Shaping Theory

Nyquist-II Pulse (W>1/(2T))

Page 42: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

42

Nyquist Pulse Shaping Theory

Frequency domain No ISI criterion

Tf

T

kfHfH

TfCfH

N

Nkeq

eq

2

1),()(

2

1,)(

Page 43: Chapter 3

“Cognitive Radio Communications and Networks: Principles and Practice”By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009)

43

Chapter 3 Summary Digital communication theory at the

core of cognitive radio operations Mathematical tools necessary for

enabling advanced features Various modulation schemes can be

used for transmission under different constraints and expectations

Bit Error Rate is primarily used to define performance