1. CSCD 433/533 Network Programming
Spring 2018
Lecture 3
Physical Layer Line Coding 1 1

2. Physical Layer Topics Motivation for studying Physical Layer
Definitions of terms
Analog vs Digital
Characteristics of physical media
Wireless
Reading: Tanenbaum, Chapter 2 2

3. Motivation Why study the physical layer?
Need to know basic data transmission concepts
Understand physical layer to understand media influence on network performance
Answer Questions such as:
What transmission speed is possible with various media?
Where and how are errors introduced?

4. Physical Layer - Purpose
Transmit information across a distance
Source
Transmit bits from one point to another
Encode bits onto a signal
Destination
Receive signals, interpret or extract bits
What is a Signal?
1. Mechanism used to carry information over
time or distance
2. Sign or gesture giving information
3. Sequence of electrical or optical impulses or
waves

5. Signals Examples Physical gesture, wave, hand signal Morse code
Sound: vary tone, loudness or duration
Flags
Smoke
Electical voltages

6. Transmission 1. Action of conveying electrical or optical
signals from 1 point to 1 or more other
points in space
2. Process of sending information from 1 point
to another
What do you need for a Transmission System ?
Medium for signal transfer
Method to transform signal to appropriate form
Way to transmit the signal
Way to remove, receive or detect the signal

7. Transmission Not Perfect
Along the way, signals are subject to less than favorable conditions
Distance affects the signal
Loss of signal strength with distance
Transmission through medium, loss strength
Resistance of medium, creates heat
Recall what that is called?
Attenuation
Noise affects the signal
Line noise obscures the signal
Can make it impossible to send information

8. Line Noise
Defined
Unwanted electrical or electromagnetic energy that degrades quality of signals and data
External noise from appliances, electrical transformers, atmosphere
Communications engineers are constantly striving to develop better ways to deal with noise

9. Bandwidth What is bandwidth? More than one definition?
Network bandwidth Defined:
Bandwidth is same as data transfer rate,
Amount of data that can be carried from one point to another in a given time period
Network bandwidth is usually expressed in bits per second (bps)

10. Bandwidth (2) What is bandwidth? More than one definition?
Signal Processing Bandwidth defined
Bandwidth is range of frequencies carried by a channel
Difference between highest- frequency signal component and the lowest-frequency signal component
Bandwidth is measured in hertz (cycles per second)

11. Bandwidth

12. What is Attenuation?
Attenuation is …
- Reduction of signal strength during transmission
- Attenuation is gradual loss in intensity of any kind of
flux through a medium
Ex. Reduction in signal strength from length of phone line
Sunlight is attenuated by dark glasses, and
X-rays are attenuated by lead.

13. Attenuation Continued
- Attenuation is measured in decibels
- Decibel (dB) is used to measure sound, but
also widely used in electronics, signals and communication
Decibel (dB) measures relative strengths of two signals or a signal
at two different points
- The lower the Attenuation the stronger the received signal
Note: Decibel is negative if a signal is attenuated and positive if a signal is amplified.
Attenuation can be measured by:
dB = 10 log10 (P2 /P1)
where P1 and P2 are the powers of a signal at points 1 and 2, respectively.

14. Attenuation 14 14

15. Digital vs Analog

16. Analog and Digital Both data and signals that represent them can
take either analog or digital form.
Digital signal has discrete values, not continuous
Example of Digital data or signal
Example of Digital data or signal?
0's and 1's stored in computer as a number

17. Analog and Digital
Both data and signals that represent them can take either analog or digital form.
Analog has continuous values, not discrete
Example of Analog Signal
What might be an example of an Analog signal?
Human voice.
Analog wave is created in the air

18. Analog vs. Digital Signals
1. Limited to finite number of values
2. Has meaning only at discrete points in
time
Examples: Text, bits, integers

19. Analog vs. Digital Signals
Analog Signal
1. Signal that is an analog of the quantity
being represented
2. Continuous range of values
3. Also continuous in time, always valued
Examples: Sound, vision, music, original TV signal

20. Analog vs. Digital

21. Analog Signals
An analog signal is continuous has infinite number of values in a range
Primary shortcoming of analog signals is difficulty to separate noise from original waveform
An example is a sine wave which can be specified by three characteristics:
tsin (2  f t + p)‏
A: amplitude or height
f : frequency
p phase

22. Sine Waves Characteristics
Amplitude, height (intensity) of wave
The amplitude is a variable characterizing a sinusoidal oscillation.
It gives the deflection of a physical quantity from its neutral position (zero point) up to a positive or negative value.
Frequency, number of waves that pass in a single second and is measured in Hertz (cycles/second) (wavelength, the length of the wave from crest to crest, is related to frequency)
Phase is a third characteristic
Describes point in wave’s cycle at which a wave begins and is measured in degrees

23. A Carrier Wave

24. Sine Wave

25. Wavelength and Frequency
How is the wavelength related to frequency?
Wavelength and frequency of light are closely related Higher the frequency, shorter the wavelength
Equation that relates wavelength and frequency for electromagnetic waves is:
λν=c where λ is the wavelength, ν is the frequency and c is the speed of light.

26. Analog vs. Digital Transmission
Analog transmission: all details must be reproduced accurately
Distortion
Attenuation
Sent
Received
Digital transmission: only discrete levels need to be reproduced
Received
Sent
Distortion
Attenuation
Receiver: Was original pulse positive or negative?

27. Digital Signal Bit rate = 1 bit / secondT 2T 3T 4T 5T 6T 1
Bit rate = 1 bit / second
For a given communications medium
How do we increase transmission speed?
How do we achieve reliable communications?
Are there limits to speed and reliability?

28. Channel Noise affects Reliability
signal
noise
signal + noise
High
SNR
virtually error-free
Low
SNR
error-prone
SNR (dB) = 10 log10 (Ave Signal Power/ Ave Noise Power)
SNR – Signal to Noise Ratio, measures signal strength with addition of noise

29. Data Rate Limits Important Concern in Data Communications
How fast can we send data, in bits per second, over a channel?
Plus, what bandwidth is needed to send bits?
Also, must consider errors ...
Data rate depends on three factors:
1. Available bandwidth
2. Number of levels used to represent
signals
3. Quality of the channel (level of noise)

30. Data Rate Limits
Turns out that there are two formulas establish theoretical limits of data rates
Formula for a noiseless channel – Nyquist Maximum
Uses bandwidth and signal encodings, levels
Formula for a noisy channel – Shannon Capacity
Uses characteristics of channel such as bandwidth but accounts for Signal to Noise ratio (SNR)

31. 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
Noiseless means no measurement for errors

32. Noiseless Channel: Nyquist Bit Rate
Defines theoretical maximum bit rate for
Noiseless Channel
Bit Rate = 2 X Bandwidth X log2 L
L = number of signal levels

33. Signal Levels of Digital Signals
Digital Signals can be encoded for optimal transmission
For example,
1 can be encoded as positive voltage and
0 as zero voltage
Simplest way to encode a digital signal
Limits us to sending 1 bit per level
But, digital signals can have more than two levels
In this case, we can send more than 1 bit for each level 33 33

34. Ex. Two digital signals: Two signal levels and Four signal levels34 34

35. Look at some examples ….

36. Example 1 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
log2(2) = 1

37. Example 2 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
log2(4) = 2

38. Example 3
We need to send 265 kbps over a noiseless channel with a bandwidth of 20 kHz. How many signal levels do we need?
Solution
We can use the Nyquist formula:
Since this result is not a power of 2, we need to either increase number of levels or reduce bit rate.
If we have 128 levels, the bit rate is 280 kbps.
If we have 64 levels, the bit rate is 240 kbps.

39. Note
Increasing the levels of a signal may reduce the reliability of the system

40. Capacity of a System Bit rate of a system increases with an increase
in number of signal levels we use to denote
symbol
A symbol can consist of a single bit or “n” bits.
The number of signal levels = 2n.
But, as number of levels goes up,
spacing between level decreases ->
Increasing probability of an error occurring
presence of transmission noise

41. Increasing Levels
In theory, can increase the bit rate by increasing the number of levels
Yet, random noise limits the bit rate in practice
Noise causes measurement system to make mistakes

42. Communication Channel Noise
Interference from sources like radio waves
Electrical wires, and
Bad connections that alter the data
Distortion
Alteration in signal caused by communication channel itself
Noise generated by components is categorized as thermal noise
Also known as additive noise.

43. Claude Shannon Noisy Channel
Claude Shannon, another AT&T scientist
Claude Shannon developed mathematical theory in 1940's for noisy channels
Then, defined amount of information that a message could carry
This allowed networks to plan for capacity of information
Wrote: A Mathematical Theory of Communication

44. Noisy Channel: Shannon Capacity
Defines theoretical maximum bit rate for Noisy Channel:
Capacity = Bandwidth X log2(1+SNR)

45. Examples of Nyquist and Shannon Formulas

46. C = B log2 (1 + SNR) = B log2 (1 + 0) = B log2 (1) = B  0 = 0
Example 1
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, Eq. Capacity = Bandwidth X log2(1+SNR)
For this channel capacity is calculated as:
C = B log2 (1 + SNR) = B log2 (1 + 0) = B log2 (1) = B  0 = 0
What does this mean for data?

47. Result of High Noise
C = B log2 (1) = B X 0 = 0
This means that the capacity of this channel is zero regardless of the bandwidth
In other words, we cannot receive any data through this channel !!!

48. C = B log2 (1 + SNR) = 3000 log2 (1 + 3162) = 3000 log2 (3163)
Example 2
We can calculate theoretical highest bit rate of a regular telephone line, with noise
A telephone line normally has bandwidth of 3000 bps
The signal-to-noise ratio is usually 3162
For this channel the capacity is calculated as
C = B log2 (1 + SNR) = 3000 log2 ( ) = 3000 log2 (3163)
C = 3000  = 34,860 bps

49. Example continued Result C = 3000 X11.62 = 34,860 bps
This means that highest bit rate for a telephone line is kbps
If we want to send data faster than this, we can either increase bandwidth of line or improve signal-to-noise ratio.

50. Example 3
We have channel with 1 MHz bandwidth
The SNR for this channel is 63,
What is the appropriate bit rate and signal level?
Solution
First, we use Shannon's formula to find our upper limit of channel capacity
C = B log2 (1 + SNR) = 106 log2 (1 + 63) = 106 log2 (64) = 6 Mbps
Then we use Nyquist formula to find the
number of signal levels.
6 Mbps = 2  1 MHz  log2 L
6,000,000
2,000,000 = log2 L L = 23  L = 8

51. The Shannon capacity gives us the upper limit, assuming noise
Note
The Shannon capacity gives us the upper limit, assuming noise
Nyquist formula tells us how many signal levels we need.

52. Summary Looked at physical medium
Defined Signals – Differences Analog vs. Digital
Transmission of Analog and Digital Signals
Theoretical limits of noiseless and noisy channels

53. Read Chapter 2 - Textbook Also, review basic Wireshark Lab
Assignment 2 is up
Wireshark Lab – Advanced 53