1. 1 Chapter Five Making Connections Efficient: Multiplexing and Compression

2. 2 Introduction Under the simplest conditions, a medium can carry only one signal at any moment in time For multiple signals to share a medium, the medium must somehow be divided, giving each signal a portion of the total bandwidth The current techniques include frequency division multiplexing, time division multiplexing, and code division multiplexing Chapter Five - Making Connections Efficient: Multiplexing and Compression

3. 3 Frequency Division Multiplexing Assignment of non-overlapping frequency ranges to each “user” or signal on a medium. Thus, all signals are transmitted at the same time, each using different frequencies A multiplexor accepts inputs and assigns frequencies to each device Chapter Five - Making Connections Efficient: Multiplexing and Compression

4. 4 Frequency Division Multiplexing The multiplexor is attached to a high-speed communications line A corresponding multiplexor, or demultiplexor, is on the end of the high-speed line and separates the multiplexed signals Chapter Five - Making Connections Efficient: Multiplexing and Compression

5. 5 Chapter Five - Making Connections Efficient: Multiplexing and Compression

6. 6 Frequency Division Multiplexing Analog signaling is used to transmit the data Broadcast radio and television, cable television, and cellular telephone systems use frequency division multiplexing This technique is the oldest multiplexing technique Since it involves analog signaling, it is more susceptible to noise Chapter Five - Making Connections Efficient: Multiplexing and Compression

7. 7 Time Division Multiplexing Sharing of the signal is accomplished by dividing available transmission time on a medium among users Digital signaling is used exclusively Time division multiplexing comes in two basic forms: –Synchronous time division multiplexing –Statistical time division multiplexing Chapter Five - Making Connections Efficient: Multiplexing and Compression

8. 8 Synchronous Time Division Multiplexing The original time division multiplexing The multiplexor accepts input from attached devices in a round-robin fashion and transmits the data in a never ending pattern T-1 and ISDN telephone lines are common examples of synchronous time division multiplexing Chapter Five - Making Connections Efficient: Multiplexing and Compression

9. 9 Chapter Five - Making Connections Efficient: Multiplexing and Compression

10. 10 Synchronous Time Division Multiplexing If one device generates data at a faster rate than other devices, then the multiplexor must either sample the incoming data stream from that device more often than it samples the other devices, or buffer the faster incoming stream If a device has nothing to transmit, the multiplexor must still insert something into the multiplexed stream Chapter Five - Making Connections Efficient: Multiplexing and Compression

11. 11 Chapter Five - Making Connections Efficient: Multiplexing and Compression

12. 12 Chapter Five - Making Connections Efficient: Multiplexing and Compression

13. 13 Synchronous Time Division Multiplexing So that the receiver may stay synchronized with the incoming data stream, the transmitting multiplexor can insert alternating 1s and 0s into the data stream Chapter Five - Making Connections Efficient: Multiplexing and Compression

14. 14 Synchronous Time Division Multiplexing The T-1 multiplexor stream is a continuous series of frames Chapter Five - Making Connections Efficient: Multiplexing and Compression

15. 15 Synchronous Time Division Multiplexing The ISDN multiplexor stream is a also a continuous series of frames. Each frame contains various control and sync info Chapter Five - Making Connections Efficient: Multiplexing and Compression

16. 16 Synchronous Time Division Multiplexing Likewise, SONET incorporates a continuous series of frames. Chapter Five - Making Connections Efficient: Multiplexing and Compression

17. 17 Statistical Time Division Multiplexing A statistical multiplexor transmits the data from active workstations only If a workstation is not active, no space is wasted in the multiplexed stream A statistical multiplexor accepts the incoming data streams and creates a frame containing the data to be transmitted Chapter Five - Making Connections Efficient: Multiplexing and Compression

18. 18 Chapter Five - Making Connections Efficient: Multiplexing and Compression

19. 19 Chapter Five - Making Connections Efficient: Multiplexing and Compression To identify each piece of data, an address is included

20. 20 Chapter Five - Making Connections Efficient: Multiplexing and Compression If the data is of variable size, a length is also included

21. 21 More precisely, the transmitted frame contains a collection of data groups Chapter Five - Making Connections Efficient: Multiplexing and Compression

22. 22 Wavelength Division Multiplexing Wavelength division multiplexing multiplexes multiple data streams onto a single fiber optic line Different wavelength lasers (called lambdas) transmit the multiple signals Chapter Five - Making Connections Efficient: Multiplexing and Compression

23. 23 Wavelength Division Multiplexing Each signal carried on the fiber can be transmitted at a different rate from the other signals Dense wavelength division multiplexing combines many (30, 40, 50 or more) onto one fiber Coarse wavelength division multiplexing combines only a few lambdas Chapter Five - Making Connections Efficient: Multiplexing and Compression

24. 24 Chapter Five - Making Connections Efficient: Multiplexing and Compression

25. 25 Discrete Multitone (DMT) A multiplexing technique commonly found in digital subscriber line (DSL) systems DMT combines hundreds of different signals, or subchannels, into one stream Chapter Five - Making Connections Efficient: Multiplexing and Compression

26. 26 Discrete Multitone (DMT) Each subchannel is quadrature amplitude modulated (recall eight phase angles, four with double amplitudes) Theoretically, 256 subchannels, each transmitting 60 kbps, yields 15.36 Mbps Unfortunately, there is noise Chapter Five - Making Connections Efficient: Multiplexing and Compression

27. 27 Chapter Five - Making Connections Efficient: Multiplexing and Compression

28. 28 Code Division Multiplexing Also known as code division multiple access An advanced technique that allows multiple devices to transmit on the same frequencies at the same time Each mobile device is assigned a unique 64-bit code Chapter Five - Making Connections Efficient: Multiplexing and Compression

29. 29 Code Division Multiplexing To send a binary 1, a mobile device transmits the unique code To send a binary 0, a mobile devices transmits the inverse of the code Chapter Five - Making Connections Efficient: Multiplexing and Compression

30. 30 Code Division Multiplexing Receiver gets summed signal, multiplies it by receiver code, adds up the resulting values Interprets as a binary 1 if sum is near +64 Interprets as a binary 0 if sum is near -64 Chapter Five - Making Connections Efficient: Multiplexing and Compression

31. 31 Code Division Multiplexing For simplicity, assume 8-bit code Three different mobile devices use the following codes: –Mobile A: 10111001 –Mobile B: 01101110 –Mobile C: 11001101 Assume Mobile A sends a 1, B sends a 0, and C sends a 1 Chapter Five - Making Connections Efficient: Multiplexing and Compression

32. 32 Code Division Multiplexing Signal code: 1-chip = +N volt; 0-chip = -N volt Three signals transmitted: –Mobile A sends a 1, or 10111001, or +-+++--+ –Mobile B sends a 0, or 10010001, or +--+---+ –Mobile C sends a 1, or 11001101, or ++--++-+ Summed signal received by base station: +3, - 1, -1, +1, +1, -1, -3, +3 Chapter Five - Making Connections Efficient: Multiplexing and Compression

33. 33 Code Division Multiplexing Base station decode for Mobile A: Signal received: +3, -1, -1, +1, +1, -1, -3, +3 Mobile A’s code: +1, -1, +1, +1, +1, -1, -1, +1 Product result: +3, +1, -1, +1, +1, +1, +3, +3 Sum of Products: +12 Decode rule: For result near +8, data is binary 1 Chapter Five - Making Connections Efficient: Multiplexing and Compression

34. 34 Code Division Multiplexing Base station decode for Mobile B: Signal received: +3, -1, -1, +1, +1, -1, -3, +3 Mobile B’s code: -1, +1, +1, -1, +1, +1, +1, -1 Product result: -3, -1, -1, -1, +1, -1, -3, -3 Sum of Products: -12 Decode rule: For result near -8, data is binary 0 Chapter Five - Making Connections Efficient: Multiplexing and Compression

35. 35 Chapter Five - Making Connections Efficient: Multiplexing and Compression

36. 36 Chapter Five - Making Connections Efficient: Multiplexing and Compression

37. 37 Compression This is another technique used to squeeze more data over a communications line If you can compress a data file to ½ of its original size, the file will transfer in less time Two basic groups of compression: –Lossless – when data is uncompressed, original data returns –Lossy – when data is uncompressed, you do not have the original data Chapter Five - Making Connections Efficient: Multiplexing and Compression

38. 38 Compression Compress a financial file? Need lossless Compress a video image, movie, or audio file? Lossy is OK Examples of lossless compression include Huffman codes, run-length compression, Lempel-Ziv compression, Apple Lossless, and FLAC (Free Lossless Audio Codec) Examples of lossy compression include MPEG, JPEG, MP3 Chapter Five - Making Connections Efficient: Multiplexing and Compression

39. 39 Run-Length Compression Replace runs of 0s with a count of how many 0s. 00000000000000100000000011000000000000000000001000…001100000000000 ^ (30 0s) 14 9 0 20 30011 Chapter Five - Making Connections Efficient: Multiplexing and Compression

40. 40 Chapter Five - Making Connections Efficient: Multiplexing and Compression Run-Length Compression Now replace each decimal value with a 4-bit binary value (nibble) Note: If you need to code a value larger than 15, you need to use two code two consecutive 4-bit nibbles. The first is decimal 15, or binary 1111, and the second nibble is the remainder. For example, if the decimal value is 20, you would code 1111 0101 which is equivalent to 15 + 5. If you want to code the value 15, you still need two nibbles: 1111 0000. The rule is that if you ever have a nibble of 1111, you must follow it with another nibble.

41. 41 Chapter Five - Making Connections Efficient: Multiplexing and Compression Relative or Differential Encoding (Lossy) Video does not compress well using run-length encoding In one color video frame, not much is alike But what about from frame to frame? Send a frame, store it in a buffer Next frame is just different from previous frame Then store that frame in buffer, etc.

42. 42 5 7 6 2 8 6 6 3 5 6 6 5 7 5 5 6 3 2 4 7 8 4 6 8 5 6 4 8 8 5 5 1 2 9 8 6 5 5 6 6 First Frame 5 7 6 2 8 6 6 3 5 6 6 5 7 6 5 6 3 2 3 7 8 4 6 8 5 6 4 8 8 5 5 1 3 9 8 6 5 5 7 6 Second Frame 0 0 0 0 0 0 0 0 1 0 0 0 0 -1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 Difference Chapter Five - Making Connections Efficient: Multiplexing and Compression

43. 43 Chapter Five - Making Connections Efficient: Multiplexing and Compression Image Compression One image – JPEG, or continuous images such as video – MPEG A color picture (or pixel) can be defined by red/green/blue For each pixel you have 3 values, each 8 bits, or 24 bits total (2 24 colors!)

44. 44 Chapter Five - Making Connections Efficient: Multiplexing and Compression Image Compression A VGA screen is 640 x 480 pixels 24 bits x 640 x 480 = 7,372,800 bits. Ouch! And video comes at you 30 images per second. Double Ouch! We need compression

45. 45 Chapter Five - Making Connections Efficient: Multiplexing and Compression JPEG Joint Photographic Experts Group Compresses still images Lossy JPEG compression consists of 3 phases: –Discrete cosine transformations (DCT) –Quantization –Encoding

46. 46 Chapter Five - Making Connections Efficient: Multiplexing and Compression JPEG Step 1 - DCT Divide image into a series of 8 x 8 blocks If the original image was 640 x 480 pixels, the new picture would be 80 blocks x 60 blocks (see next slide) If B&W, each pixel in 8x8 block is an 8-bit value (0-255) If color, each pixel is a 24-bit value (8 bits for red, 8 for blue, and 8 for green)

47. 47 80 blocks 60 blocks 640 x 480 VGA Screen Image Divided into 8 x 8 Pixel Blocks

48. 48 Chapter Five - Making Connections Efficient: Multiplexing and Compression JPEG Step 1 - DCT So what does DCT do? Takes an 8x8 array (P) and produces a new 8x8 array (T) using cosines T matrix contains a collection of values called spatial frequencies These spatial frequencies relate directly to how much the pixel values change as a function of their positions in the block

49. 49 Chapter Five - Making Connections Efficient: Multiplexing and Compression T Array 1518212428323640 1922252832364044 2225283236404448 2629323539434751 3034384246515661 3438424651566166 3842465156616672 4348535863687480 P Array 628-12321-80-20 -18523-500000 100000000 00000000 30000000 0000000 00000000 00000000

50. 50 Chapter Five - Making Connections Efficient: Multiplexing and Compression JPEG Step 1 - DCT An image with uniform color changes (fine detail) has a P array with closely similar values and a corresponding T array with many zero values An image with large color changes over a small area has a P array with widely changing values, and thus a T array with fewer zero values

51. 51 Chapter Five - Making Connections Efficient: Multiplexing and Compression JPEG Step 2 - Quantization The human eye can’t see small differences in color changes So take T matrix and divide all values by 10. This will give us more zero entries. More 0s means more compression! But this is too lossy. And dividing all values by 10 doesn’t take into account that upper left of matrix has more action (the less subtle features of the image, or low spatial frequencies)

52. 52 1 3 5 7 9 11 13 15 3 5 7 9 11 13 15 17 5 7 9 11 13 15 17 19 7 9 11 13 15 17 19 21 9 11 13 15 17 19 21 23 11 13 15 17 19 21 23 25 13 15 17 19 21 23 25 27 15 17 19 21 23 25 27 29 U matrix Q[i][j] = Round(T[i][j] / U[i][j]), for i = 0, 1, 2, …7 and j = 0, 1, 2, …7

53. 53 Chapter Five - Making Connections Efficient: Multiplexing and Compression JPEG Step 3 - Encoding Now take the quantized matrix Q and perform run-length encoding on it But don’t just go across the rows. Longer runs of zeros if you perform the run-length encoding in a diagonal fashion (next slide)

54. 54 Chapter Five - Making Connections Efficient: Multiplexing and Compression

55. 55 Chapter Five - Making Connections Efficient: Multiplexing and Compression JPEG How do you get the image back? –Undo run-length encoding –Multiply matrix Q by matrix U yielding matrix T –Apply similar cosine calculations to get original P matrix back

56. 56 Chapter Five - Making Connections Efficient: Multiplexing and Compression Business Multiplexing In Action XYX Corporation has two buildings separated by a distance of 300 meters A 3-inch diameter tunnel extends underground between the two buildings Building A has a mainframe computer and Building B has 66 terminals What are some efficient techniques for linking the two building?

57. 57 Chapter Five - Making Connections Efficient: Multiplexing and Compression

58. 58 Chapter Five - Making Connections Efficient: Multiplexing and Compression Possible Solutions Connect each terminal to the mainframe computer using separate point-to-point lines Connect all the terminals to the mainframe computer using one multipoint line (polling) Connect all the terminal outputs and use some form of wireless (microwave?) Connect all the terminal outputs using multiplexing and send data to mainframe using a conducted (or wireless?) line