CSCD 433/533 Network Programming Spring 2018

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

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 Digital Signal 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=cwhere λ is the wavelength, ν is the frequency and c is the speed of light.

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

27. Digital Signal 1 0 1 1 0 1 +A T 0 2T 4T 5T 6T 3T -A 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 noise signal + noise signal 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

34. Ex. Two digital signals: Two signal levels and Four signal levels 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 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 ShannonNoisy 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 http://www-groups.dcs.st-and.ac.uk/~history/Biographies/Shannon.html

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. 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. 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 (1 + 3162) = 3000 log2 (3163) C = 3000  11.62 = 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 34.860 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  log2L 6,000,000 2,000,000 = log2 L L = 23 L = 8