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# William Stallings Data and Computer Communications_10_

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```									      Data and Computer
Communications

Chapter 3
Data Transmission

1
Physical Layer
Source node                                   Destination node

Application                                   Application

Presentation                                  Presentation

Session                                       Session

transport               Intermediate node     transport
Packets
Network                    Network            Network
Frames

Physical       Bits       Physical            Physical

Signals
2
Physical / Data Link Layer Interface

NL

HDR
DLL
Frame

ACK
PL
HDR

Transmitted Bits

3
Physical Layer

Communications and Information Theory are topics
of whole courses
We‟ll cover some theoretical basics regarding
communications over a physical channel
We discover that there are physical limitations to
communications over a given channel
We‟ll cover some fundamental theorems

4
Terminology (1)
Transmitter
Medium
Guided medium
e.g. twisted pair, optical fiber
Unguided medium
e.g. air, water, vacuum

5
Terminology (2)
No intermediate devices
Point-to-point
Multi-point
More than two devices share the link

6
Terminology (3)
Simplex
One direction (but in Europe means half duplex)
e.g. Television
Half duplex
Either direction, but only one way at a time
Full duplex
Both directions at the same time
e.g. telephone

7
Electromagnetic Signals
Function of time
Analog (varies smoothly over time)
Digital (constant level over time, followed by a
change to another level)
Function of frequency
Spectrum (range of frequencies)
Bandwidth (width of the spectrum)

8
Frequency, Spectrum and
Bandwidth
Time domain concepts
Continuous signal
Varies in a smooth way over time
Discrete signal
Maintains a constant level then changes to another constant
level
Periodic signal
Pattern repeated over time
Aperiodic signal
Pattern not repeated over time

9
Periodic Signal Characteristics
Amplitude (A): signal value, measured in volts
Frequency (f ): repetition rate, cycles per second or
Hertz
Period (T): amount of time it takes for one repetition,
T=1/f
Phase (Φ): relative position in time, measured in

10
Analog Signaling
represented by sine waves
amplitude (volts)

1 cycle

phase
difference
time
(sec)

frequency (hertz)
= cycles per second
11
Digital Signaling
represented by square waves or pulses
amplitude (volts)

1 cycle

time
(sec)

frequency (hertz)
= cycles per second
12
Continuous & Discrete Signals

13
Periodic
Signals

14
Sine Wave
Peak Amplitude (A)
maximum strength of signal
volts
Frequency (f)
Rate of change of signal
Hertz (Hz) or cycles per second
Period = time for one repetition (T)
T = 1/f
Phase ()
Relative position in time

15
Varying Sine Waves

Sin2πt                 0.5Sin2πt

Phase Shift in

Sin4πt
or

Phase Shift in
seconds 16
Wavelength ()
Distance occupied by one cycle
Distance between two points of corresponding
phase in two consecutive cycles

Assuming signal velocity in space is equal to v
 = vT or
f = v
Here, V=c = 3*108 ms-1 (speed of light in free space)

17
Frequency Domain Concepts
A Signal is usually made up of many frequencies
Components are sine waves
It Can be shown (Fourier analysis) that any
signal is made up of component sine waves
One can plot frequency domain functions

18
Frequency
Components
(a) Sin(2πft)

(b) (1/3)Sin(2π(3f)t)

19
(c) (4/π)[Sin(2πft)+(1/3)Sin(2π(3f)t)]
Frequency
Domain
Note: For square waves,
only odd harmonics exist
(plus the fundamental
component of course).

Figure a is discrete because          (a) Frequency domain function for s(t)=(4/π)[Sin(2πft)+(1/3)Sin(2π(3f)t)]
the time domain function is
periodic. Figure b is
continuous because the time
domain function is aperiodic.

See Figure 3.16 Page
103. Note that s(f) is
of the form

20
(b) Frequency domain function for a single square pulse s(t)=1 for -X/2<t<X/2
Communications Basics
 Represent a signal as a single-valued function of time,
g(t), to model behavior of a signal (may be voltage,
current or other change)
 Jean-Baptiste Fourier showed we can represent a
periodic signal (given some conditions) as the sum of a
possibly infinite number of sines and cosines

Period = T
S
g(t) = (1/2)c + n=1 an sin(2pnft) + S bn cos(2pnft)
n=1
f = 1/T is fundamental frequency
a & b coefficients are the amplitude of the nth harmonic
This is a Fourier Series

21
Original
Time ->   Harmonic spectrum

more
harmonics
the signal
reproduces
the original
more closely

22
Signal Transmission

No transmission facility can transmit signals
without losing some power
Usually this attenuation is frequency
dependent so the signal becomes distorted
Generally signal is completely attenuated
above some max frequency (due to medium
characteristics or intentional filtering)
The signal is bandwidth limited

23
Signal Transmission

Time T necessary to transmit a character depends on
coding method and signalling speed
Signaling speed = number of times per second the
signal changes value and is measured in baud
Note that baud rate is not necessarily the same as
the bit rate
By limiting the bandwidth of the signal we also limit
the data rate even if a channel is perfect
Overcome this by encoding schemes

24
Spectrum & Bandwidth
Spectrum
range of frequencies contained in signal
Absolute bandwidth
width of spectrum
Effective bandwidth
Often just bandwidth
Narrow band of frequencies containing most of the
energy
DC Component
Component of zero frequency

25
Signal with DC Component

(a) s(t)=1+(4/π)[Sin(2πft)+(1/3)Sin(2π(3f)t)]

26
Data Rate and Bandwidth
Any transmission system has a limited band of
frequencies
This limits the data rate that can be carried

See Figure 3.8 Page 79

27
Bandwidth
Width of the spectrum of frequencies that can
be transmitted
if spectrum=300 to 3400Hz, bandwidth=3100Hz
Greater bandwidth leads to greater costs
Analog measured in Hertz, digital measured in
baud

28
BPS vs. Baud
BPS=bits per second
Baud=# of signal changes per second
Each signal change can represent more than
one bit, through variations on amplitude,
frequency, and/or phase

29
Analog and Digital Data
Transmission
Data
Entities that convey meaning
Signals
Electric or electromagnetic representations of data
Transmission
Communication of data by propagation and
processing of signals

30
Data
Analog
Continuous values within some interval
e.g. sound, video
Digital
Discrete values
e.g. text, integers

31
Acoustic Spectrum (Analog)

32
Signals
Means by which data are propagated
Analog
Continuously variable
Various media
wire, fiber optic, space
Speech bandwidth 100Hz to 7kHz
Telephone bandwidth 300Hz to 3400Hz
Video bandwidth 4MHz
Digital
Use two DC components

33
Digital Text Signaling
Transmission of electronic pulses representing
the binary digits 1 and 0
How do we represent letters, numbers,
characters in binary form?
Earliest example: Morse code (dots and dashes)
Most common current form: ASCII

34
ASCII Character Codes
Use 8 bits of data (1 byte) to transmit one
character
8 binary bits has 256 possible outcomes (0 to
255)
Represents alphanumeric characters, as well as
“special” characters

35
Digital Image Signaling
 Pixelization and binary representation

Code:   00000000
00111100
01110110
01111110
01111000
01111110
00111100
00000000

36
Data and Signals
Usually use digital signals for digital data and
analog signals for analog data
Can use analog signal to carry digital data
Modem
Can use digital signal to carry analog data
Compact Disc audio

37
Why Study Analog?
Telephone system is primarily analog rather
than digital (designed to carry voice signals)
Low-cost, transmission medium (present almost
at all places at all times
If we can convert digital information (1s and 0s)
to analog form (audible tone), it can be
transmitted inexpensively

38
Voice Signals
Easily converted from sound frequencies
(measured in loudness/db) to electromagnetic
frequencies, measured in voltage
Human voice has frequency components
ranging from 20Hz to 20kHz
For practical purposes, the telephone system
has a narrower bandwidth than human voice,
from 300 to 3400Hz

39
Analog Signals Carrying Analog
and Digital Data

40
Digital Signals Carrying Analog
and Digital Data

41
Analog Transmission
Analog signal transmitted without regard to
content
May be analog or digital data
Attenuated over distance
Use amplifiers to boost signal
Also amplifies noise

42
Digital Transmission
Concerned with content
Integrity endangered by noise, attenuation etc.
Repeaters used
Extracts bit pattern
Retransmits
Attenuation is overcome
Noise is not amplified

43
Transmission
 Digital technology
Low cost LSI/VLSI technology
 Data integrity
Longer distances over lower quality lines
 Capacity utilization
High degree of multiplexing easier with digital
techniques
 Security & Privacy
Encryption
 Integration
Can treat analog and digital data similarly
44
Transmission Media
the physical path between transmitter and
design factors
bandwidth
attenuation: weakening of signal over distances
interference

45
Impairments and Capacity
Impairments exist in all forms of data
transmission
Analog signal impairments result in random
modifications that impair signal quality
Digital signal impairments result in bit errors (1s
and 0s transposed)

46
Transmission Impairments
Signal received may differ from signal
transmitted
Analog - degradation of signal quality
Digital - bit errors
Caused by
Attenuation and attenuation distortion
Delay distortion
Noise

47
Transmission Impairments
Attenuation
loss of signal strength over distance
Attenuation Distortion
different losses at different frequencies
Delay Distortion
different speeds for different frequencies
Noise

48
Attenuation

P1 watts        P2 watts

Attenuation              10 log10 (P1/P2) dB

Amplification            10 log10 (P2/P1) dB

49
Attenuation
Signal strength falls off with distance
Depends on medium
must be enough to be detected
must be sufficiently higher than noise to be received
without error
Attenuation is an increasing function of
frequency

50
Delay Distortion
Only in guided media
Propagation velocity varies with frequency

51
Noise (1)
Types of Noise:
Thermal
Due to thermal excitement of electrons
Uniformly distributed, cannot be eliminated
White noise
Intermodulation
Signals that are the sum and difference of original
frequencies sharing a medium

52
Noise (2)
Crosstalk
A signal from one line is picked up by another
 NEXT (near-end crosstalk     )
interference in a wire at the transmitting end of a signal
sent on a different wire

 FEXT (far-end crosstalk)
interference in a wire at the receiving end of a signal
sent on a different wire

Impulse
Irregular pulses or spikes
e.g. External electromagnetic interference
Short duration
High amplitude
Less predictable                                            53
Noise

 Effect
distorts a transmitted signal
attenuates a transmitted signal

 signal-to-noise ratio to quantify noise

S/Ndb =   10 log S          S= average signal power
N
N= noise power

54
Effect of noise

Signal

Noise
Logic
Threshold                                    Signal+Noise

Sampling times
0 1   1   1 1 0   0    0   0 1   Data Received
0 1   0   1 1 0   0    1   0 1   Original data

Bit error
55
Channel Capacity
Data rate
In bits per second
Rate at which data can be communicated
Bandwidth
In cycles per second of Hertz
Constrained by transmitter and medium

56
Maximum Data Rate

In 1920s Nyquist (of the Nyquist Theorem)
developed an equation for the maximum data rate of
a noiseless channel
For low pass filtered signal of bandwidth B
Sampling at exactly 2B samples per sec allows
reconstruction of the signal
More samples are useless since the frequencies
above B are filtered out

C=Capacity=max data rate = 2B log2 M bits/sec
for M discrete levels

57
Nyquist theorem

“ In a perfectly noiseless channel, if f is the
maxmimum frequency the medium can transmit, the
receiver can completely reconstruct a signal by
sampling it 2*f times per second”

Nyquist, 1920

58
Nyquist formula

B = bandwidth
C=      2B log2 M             M = number of discrete signal levels
Theoretical capacity for Noiseless channel

Example: Channel capacity calculation for voice bandwidth (~3100 Hz):

M          Max data rate (C)
2           6200 bps
4          12400 bps
8          18600 bps
16          24800 bps

59
Shannon’s Law
In the „40s Shannon (of Shannon‟s Law) extended the
equation to a channel subject to thermodynamic
(thermal) noise
Thermal noise measured by ratio of signal (S) power to
noise (N) power (signal-to-noise ratio - S/N)
But represented as: 10 log10 S/N
These units are called decibels (dB)
Now, for a channel with signal to noise of S/N
Capacity=C=max bits/sec = B log2 (1 + S/N)
Here, C=Theoretical Maximum capacity with noise

Note: Only much lower rates are achieved since the equation
assumes zero impulse noise and no attenuation and delay
distortion.                                                60
Bit rate and Baud rate
 Bit rate   number of bits that are transmitted in a second

 Baud rate     number of line signal changes (variations) per second

If a modem transmits 1 bit for every signal
change
bit rate = baud rate

If a signal change represents 2 or more or n
bits
bit rate = baud rate *n

61

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