01_introduction.ppt

Sep 06, 2005 CS477: Analog and Digital Communications 1
Introduction
Analog and Digital Communications
Autumn 2005-2006

Communications
 Communications = Information transfer
 This course is about communications
 Limited to information in electrical form
 We will not consider delivering newspapers
 We will primarily cover information transfer
at systems level
 We will not deal [too much] with circuits, chips,
signal processing, microprocessors, protocols,
and networks

What exactly is information?
 Information is a word that is too generic
for our purposes
 We will use the word message
 A physical manifestation of information
 What do communication systems have
to do with messages?
 Communication systems are responsible for
producing an “acceptable” replica of
message at the destination

Is Signal = Message?
 Just like information, signal is also a generic
word
 Derived directly from information
 Scientists and Engineers use signal to denote
information in electrical form
 We will use signal and message interchangeably

Can we classify signals?
 Messages or signals can be classified:
 Analog
 A physical quantity that varies with “time”, usually in a
smooth or continuous fashion
 Fidelity describes how close is the received signal to the
original signal. Fidelity defines acceptability
 Digital
 An ordered sequence of symbols selected from a finite set of
discrete elements
 When digital signals are sent through a communication
system, degree of accuracy within a given time defines the
acceptability

Examples
 Analog Signals
 Values are taken from an
infinite set
 Digital Signals
 Values are taken from a
discrete set
 Binary Signals
 Digital signals with just
two discrete values
t
t
t
1
0 0 0
1 1
0

Elements of Communication Systems
 Transmitter
 Modulation
 Coding
 Channel
 Attenuation
 Noise
 Distortion
 Interference
 Receiver
 Detection (Demodulation+Decoding)
 Filtering (Equalization)

Elements of Communication Systems
 Encoder: Message  Message Signal or bits
 Transmitter: Message signal  Transmitted signal
 Channel: Introduces noise, distortion, interference
 Receiver: Received Signal  Message Signal
 Decoder: Message Signal  Original Message
Example: Microphone ---------------> Speaker
Text
Images
Video
Audio
)
(t
s )
(
ˆ t
s
)
(
ˆ
...
ˆ
ˆ
2
1
t
m
b
b
)
(
...
2
1
t
m
b
b
Source
Encoder
Source
Decoder
Channel Receiver
Transmitter

What do we cover in CS477?
 Modulation converts message signal or bits into amplitude,
phase, or frequency of a sinusoidal carrier (Am, FM, QPSK)
 Modulation may make the transmitted signal robust to channel
impairments
 Channel introduces noise, distortion, and interference
 Demodulator tries to mitigate the channel impairments
Analog or Digital
“Modulator”
)
(t
s )
(
)
(
ˆ t
n
t
s  )
(
ˆ
...
ˆ
ˆ
2
1
t
m
b
b
)
(
...
2
1
t
m
b
b
Transmitter Channel
h(t) +
Analog or Digital
“Demodulator”
Receiver
n(t)
What do we cover in CS477?
Analog or Digital
“Modulator”
Transmitter

Fundamental Limitations
 If practical implementation is not a concern and we
don’t worry about feasibility, is there something else
that limits acceptable communications?
 Bandwidth
 Channel must be able to allow signal to pass through
 Channels usually have limited bandwidth
 Can we reduce signal bandwidth? Do “something” at source
 Noise
 Can we reduce it?
 Can we reduce its effects?
 Do something at the transmitter and receiver
 Signal to Noise Ratio

Block Diagram
(Modulator)
Analog
or Digital
Demodulator
m(t) s(t)
h(t)
n(t)
m
ê(t)
Transmitter Channel Receiver

Performance Criterion
 How a “good” communication system can be
differentiated from a “sloppy” one?
 For analog communications
 How close is to ? Fidelity!
 SNR is typically used as a performance metric
 For digital communications
 Data rate and probability of error
 No channel impairments, no error
 With noise, error probability depends upon data
rate, signal and noise powers, modulation scheme
m
ê(t) m(t)

Limits on data rates
 Shannon obtained formulas that provide
fundamental limits on data rates (1948)
 Without channel impairments, an
infinite data rate is achievable with
probability of error approaching zero
 For bandlimited AWGN channels, the
“capacity” of a channel is:
C = B log(1+ SNR)

01_introduction.ppt

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