l1 introduction(1)

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L1: Introduction

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Course Information

• Instructor:

Sang

W.

Kim – [email protected]

–

– 294‐2726

• Lecture Time: MW 5:10‐6:25

• Class Room: 204 Marston

• Office Hours: Th 2‐4

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• Prerequisite:

– EE422: Communication S stems II or e uivalent

• Class Homepage:

– , , , . posted to website

• Textbook:

– J.Proakis and M.Salehi, Digital Communications, 5th ed.,

McGraw‐Hill, 2008

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• References:

– . ar, g a ommun ca ons: un amen a s an

Applications, 2nd ed. Prentice Hall, 2001.

– ‐. . ,

Fading Channels, 2nd ed., John Wiley, 2005.

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•

• Optimum Receiver Principles

•

• Channel Capacity

• Fading Channels

• Spread‐Spectrum Communications

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•

you quick feedback. Each part of a problem will be

worth 2 points:

– 2/2 ‐ correct answer

– 1/2 ‐ wrong answer but meaningful attempt

– 0/2 ‐ no meaningful attempt of problem

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’

•

the assignment is due will be accepted with a ½

credit penalty. After 24 hours homework will NOT be

accepted.

– If you will be out of town, you must make arrangements to

ge me e ass gnmen e ore e ue a e.

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•

• Open book open note

•

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• ‐

• The project can be a literature survey, analysis, and/or

simulation on to ics related to this course.

– A literature survey means summarizing and comparing the m

ain ideas, concepts, and results presented by other authors.

• The final written report (no more than 10 pages) is due

December 9 in class.

• More details to come.

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•

ra ng

• Exam I: 30%

•

• Project: 20%

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• Block Dia ram of Di ital Communication S stems

k

, , , - js

{ }k b ( )r t

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• Source Encoder

– Removes redundancy from the source data – Iowa State University ISU

– mage co ng , ,… , peec co ng ,… , etc.

– Reverses the operation of the source decoder

– ISU Iowa State University or something else

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• Modulator – Maps a set of symbols into a set of distinguishable

waveforms such that the receiver can determine the

transmitted

symbol. – bk=i Si(t), i=1,2, … , M

– w :

What is the best waveform to choose?

How does this decision affect the bandwidth

requirements?

Error probability performance?

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• Channel

– Medium by which signal propagates from transmitter &

rece ver

– Examples: cable, fiber, air.

• Additive white Gaussian noise channel satellite dee s ace

channel)

• Inter‐symbol interference channel (telephone channel)

•

• Multiple access interference (multiple transmitters send at the

same frequency at the same time, jamming)

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• Demodulator

– Processes channel output and produces an estimate of

transmitted message

– E.g. MAP, ML, …

• Channel Encoder and Decoder

–

– Provides error detection and correction

– e.g. Hamming code, Convolution code, Turbo code,

LDPC code

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• Power or Energy

– The more power available the more reliable

communication is possible

–

power

• Data Rate

– For a given power as the data rate increases, less

reliable communication

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’

•

– Amount of frequency spectrum

• Probability of error

– Performance measure

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Groundworks for modern communication

theor• Wiener Approach

– Early 1940’s

– Classified publication reported in 1949 as an unclassified

MIT Press ublication

– Minimize MSE:)(ˆ)(2

0dt t xt x

T

t x)(t r ^

t x

– Wiener o timum filter

)(t n

Most suited to analog communication systems where

the issue is to preserve the waveform

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• North

A roach – 1943 – RCA report

“Analysis of the factors which determine signal‐to‐

” .

Report PTR‐6C, RCA Labs, June 1943

– Maximize the peak signal‐to‐noise ratio

)(

)(

1

0

t s

t s

)(t r )(^

t s j)(t s j

– Matched filter

)(1t s M

)(t n

Well suited to digital communication systems where the

issue is to distinguish which one among a number of possible discrete signals was transmitted.

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• Claude Shannon Approach

– 1948 – Bell System Technical Journal, “A mathematical

theory of communications”

– Introduced the notion of entropy as a measure of

• For any given SNR, the error probability can be made

as small as possible by using a suitable code provided

that the data rate is no larger than the capacity.

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– Does not tell us how to represent the message (coding) to

achieve the channel capacity, but merely that codes exist.

– Wisdom in the BC (Before Claude) era : Make SNR as large

as ossible. Most of the noise in the receiver is the thermal

noise in the receiver front end. Some NASA receiver front

ends

were

cooled

in

liquid

helium

to

reduce

this

noise.

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+

•

– BPSK in AWGN: ) / 2( 0 N E Q p s

1 1

p

p

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Fundamental Tradeoffs

–

– bandwidth W [Hz] –

– noise power N0 [W/Hz]

–

is possible provided

)1(log0

2W N

W R

in a itive w ite Gaussian noise c anne .

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Com lexit and dela are essentiall infinite.

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Claude E. Shannon

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