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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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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)

“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

“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

“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).

“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

“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

“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 onthe probability density function for aGaussian random variable of N(0,0.25)

Power spectral density of AWGNwith N(0,0.25)

“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)

“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

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

10

Representation of Signals

“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

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

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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

)()()()(

“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

is

1

0

2 )(

MEE s

b2log

“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

“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),2cos()(

Mmm

ttAts ci

“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

“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

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

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M-ary Quadrature Amplitude Modulation Receiver

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

“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

“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

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

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Derivation of BER

P(error)=

is the noise densityis the correlation between and

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

x

y dyexQ 2/2

21)(

0

12

NEQ

0N

12 )(1 ts )(2 ts

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

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Derivation of BER

When ,

and therefore

0

1221

22

NEEQ

21 ss EE

2min1221 2 dEE

0

2min

2NdQPe

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

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Upper Bound on BER

At least one error occurs

Therefore

j

m

j

m

12

1141312

m

j

ije N

dQP

2 0

2

2

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

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Lower Bound on BER

The smaller , the more likely an error happens.

Therefore

0

2min

2NdQPe

2ijd

“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

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

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Multicarrier modulation

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

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

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Multicarrier Modulation

Transmitter

Receiver

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

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Comparison between single carrier and multicarrier tt

“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

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

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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

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

31

OFDM

Transmitter

Receiver

“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

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

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Distortion Reduction

Channel coding•Add redundancy to improve recovering

“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

“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

“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

“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 pulseswhen T=1, A=1

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

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Intersymbol Interference

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

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

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Pulse Shaping

Nyquist Pulse Shaping Theory•Time domain condition for zero ISI

No ISI if sampling at t=kT

“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))

“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))

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

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Nyquist Pulse Shaping Theory

Frequency domain No ISI criterion

Tf

TkfHfH

TfCfH

N

Nkeq

eq

21),()(

21,)(

“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