1. 1 CSCD 433 Network Programming Fall 2013 Lecture 5a Digital Line Coding and other...

2. Physical Layer Topics Digital transmission of digital data Physical limits of networks for data Encoding data onto signals 2

3. Summary so Far Analog Communication – Objective is to transform waveform – Varies continuously with time – Distortions unavoidable – More difficult to reproduce signal at receiver Digital Communication – Objective is to transmit a symbol – Binary is 0 or 1 – Done by transmitting positive voltage for 1, negative voltage for 0 – Receiver interprets symbol – Can handle a lot of distortion and still discern symbol

4. Purpose of Digital Transmission Transfer sequence of 0's and 1's from transmitter on left to receiver on right Interested in bit rate in bits/s Can look at cross section of pipe, R Think of it as pipe diameter As R increases, volume of information flow/s increases 0110101 d meters Channel transmit receive R

5. Summary of Analog and Digital Conversions

6. 6 Data Rate Limits Important consideration in data communications is How fast we can send data, in bits per second, over a channel? Also worry about errors... 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)

7. 7 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

8. 8 Fundamental Limits of Digital Transmission Quality measured by: 1. Transmission speed or bit rate 2. Bit error rate in fraction of bits received in error

9. 9 Noiseless Channel: Nyquist Bit Rate Defines theoretical maximum bit rate for Noiseless Channel: Bit Rate = 2 X Bandwidth X log 2 L L = number of signal levels

10. 10 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  log 2 2 = 6000 bps

11. 11 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 log 2 4 = 12,000 bps Bit Rate = 2 x 3000 x log 2 4 = 12,000 bps

12. 12 Increasing the levels of a signal may reduce the reliability of the system Note

13. 13 Capacity of a System The bit rate of a system increases with an increase in the number of signal levels we use to denote a symbol. A symbol can consist of a single bit or “n” bits. The number of signal levels = 2 n. As the number of levels goes up, the spacing between level decreases -> increasing the probability of an error occurring in the presence of transmission impairments.

14. 14 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

15. 15 What is 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.

16. 16 Claude Shannon Noisy Channel Claude Shannon developed mathematical theory in the 1940's for noisy channels Then, defined the amount of information that a message could carry This allowed networks to plan for capacity of information

17. 17 Noisy Channel: Shannon Capacity Defines theoretical maximum bit rate for Noisy Channel: Capacity=Bandwidth X log 2 (1+SNR)

18. 18 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 log 2 (1 + SNR) = B log 2 (1 + 0) = B log 2 (1) = B  0 = 0

19. 19 Result = B log 2 (1) = B  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 !!!

20. 20 Example We can calculate the theoretical highest bit rate of a regular telephone line A telephone line normally has a bandwidth of 3000 bps The signal-to-noise ratio is usually 3162 For this channel the capacity is calculated as C = B log 2 (1 + SNR) = 3000 log 2 (1 + 3162) = 3000 log 2 (3163) C = 3000  11.62 = 34,860 bps

21. 21 Example continued C = 3000  11.62 = 34,860 bps Result C = 3000  11.62 = 34,860 bps This means that the highest bit rate for a telephone line is 34.860 kbps If we want to send data faster than this, we can either increase the bandwidth of the line or improve the signal-to-noise ratio.

22. 22 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 C = B log 2 (1 + SNR) = 10 6 log 2 (1 + 63) = 10 6 log 2 (64) = 6 Mbps Then we use the Nyquist formula to find the number of signal levels. 6 Mbps = 2  1 MHz  log 2 L  L = 8 First, we use the Shannon formula to find our upper limit

23. 23 The Shannon capacity gives us the upper limit; the Nyquist formula tells us how many signal levels we need. Note

24. 24 Digital Line Coding

25. 25 Digital Line Coding Method for converting digital binary information sequence into digital signal Selecting coding technique involves several considerations Previously we said... – Wanted to maximize bit rate over channels with limited bandwidth Yet, LAN's have other concerns – Ease of bit timing recovery from signal – So, receiving sample clock can maintain its synchronization with transmitting clock – Some methods better at noise and interference

26. 26 Line Coding Schemes Unipolar: Uses one voltage level Polar: Uses two voltage levels Bipolar: Uses three or more voltage levels

27. 27 In unipolar encoding, we use only one voltage level, positive Note

28. 28 Unipolar Encoding

29. 29 In polar encoding, we use two voltage levels: positive & negative Note

30. 30 Polar: NRZ-L and NRZ-I Encoding

31. 31 In NRZ-L, level of voltage determines value of the bit In NRZ-I, inversion or lack of inversion determines value of the bit Note

32. 32 Polar: RZ Encoding

33. 33 Polar: Manchester Encoding

34. 34 In Manchester and differential Manchester encoding, the transition at the middle of the bit is used for synchronization. Note

35. 35 Reception Errors Timing mismatch between sending and receiving computers

36. 36 In bipolar encoding, we use three levels: positive, zero, and negative. Note

37. 37 Bipolar: AMI (Alternative Mark Inversion) Encoding

38. 38 Summary

39. Looked at digital data over digital channels Theoretical maximum limits of transmitting bits in presence of noise and without Line encoding makes it possible to send more data as efficiency of coding increases

40. 40 New Assignment is up !!!