Transcript 4th Edition: Chapter 1

CSCD 433
Network Programming
Fall 2012
Lecture 4a
Physical Layer Line Coding
1
Physical Layer Topics
• Physical limits of networks for data
• Encoding data onto signals
2
Physical Layer




Looked at physical media for networks
Many types of wired and wireless connections
All have different capacities and purposes with
regards to network creation
Next, look at some theoretical limits of networks,
encoding schemes for digital modulation and
several multiplexing methods
Data Rate Limits



Important consideration in data communications
is
How fast we can send data, in bits per second,
over a channel?
Data rate depends on three factors:
1. The available bandwidth
2. The number of levels used to represent
signals
3. The quality of the channel (the level of noise)
4
Nyquist Maximum


1924, Henry Nyquist of AT&T developed an
equation for a perfect channel with finite
capacity
His equation expresses
– Maximum data rate for a finite
bandwidth noiseless channel
5
Noiseless Channel: Nyquist Bit
Rate

Defines theoretical maximum bit rate for
Noiseless Channel:

Bit Rate=2 X Bandwidth X log2L
L = number of signal levels

6
Example
Have a noiseless channel
Bandwidth of 3000 Hz transmitting a signal with two
signal levels
The maximum bit rate can be calculated as
Bit Rate = 2  3000  log2 2 = 6000 bps
7
Example
Consider the same noiseless channel
Transmitting a signal with four signal levels
– For each level, we send two bits
The maximum bit rate can be calculated as:
Bit Rate = 2 x 3000 x log2 4 = 12,000 bps
8
Note
Increasing the levels of a signal may
reduce the reliability of the system
9
Claude Shannon
Noisy Channel




Claude Shannon developed mathematical theory
in the 1940's for noisy channels
He used Entropy in his equation, which is the
amount of randomness for a channel
Then, defined the amount of information that a
message could carry
This allowed networks to plan for capacity of
information
10
Noisy Channel: Shannon Capacity

Defines theoretical maximum bit rate for Noisy
Channel:

Capacity=Bandwidth X log2(1+SNR)
11
Example
Consider an extremely noisy channel in which the
value of the signal-to-noise ratio is almost zero
In other words, the noise is so strong that the signal
is faint
For this channel the capacity is calculated as
C = B log2 (1 + SNR) = B log2 (1 + 0)
= B log2 (1) = B  0 = 0
12
Example
We can calculate the theoretical highest bit rate of a
regular telephone line
A telephone line normally has a bandwidth of 4KHz
The signal-to-noise ratio is usually 3162
For this channel the capacity is calculated as
C = B log2 (1 + SNR) = 3000 log2 (1 + 3162)
= 3000 log2 (3163)
C = 3000  11.62 = 34,860 bps
13
Example
We have a channel with a 1 MHz bandwidth
The SNR for this channel is 63,
What is the appropriate bit rate and signal level?
Solution
First, we use the Shannon formula to find our upper
limit
C = B log2 (1 + SNR) = 106 log2 (1 + 63) = 106 log2 (64) = 6 Mbps
Then we use the Nyquist formula to find the
number of signal levels.
6 Mbps = 2  1 MHz  log2 L  L = 8
14
Digital Modulation
15
Digital Modulation


Process of converting between bits and
signals is called digital modulation
Convert voltages into bits


Mostly for wired media
Other schemes regulate the phase or
frequency of a carrier signal

Mostly for wireless media
16
Line Coding Schemes
17
Note
In unipolar encoding, we use only one voltage
level, positive
18
Unipolar Encoding
19
Note
In polar encoding, we use two voltage levels:
positive & negative
20
Polar: NRZ-L and NRZ-I Encoding
21
Note
In NRZ-L, level of voltage determines value
of the bit
In NRZ-I, inversion or lack of inversion
determines value of the bit
22
Polar: RZ Encoding
23
Polar: Manchester Encoding
24
Note
In Manchester and differential Manchester
encoding, the transition
at the middle of the bit is used for
synchronization.
25
Note
In bipolar encoding, we use three levels:
positive, zero, and negative.
26
Bipolar: AMI (Alternative Mark Inversion)
Encoding
27
Summary
28
Summary
• Many types of encoding for sending data over
analog types of lines
• Multiplexing allows sharing
–
More on this later ….
• There are actually limits to how much data can
be sent within a network
• No new assignment yet ...
30