1. 1 Chapter 5 Multiplexing : Sharing a Medium Data Communications and Computer Networks: A Business User’s Approach

2. 2 Data Communications and Computer Networks Chapter 5 Introduction Under the simplest conditions, a medium can carry only one signal at any moment in time. For multiple signals to share one medium, the medium must somehow be divided, giving each signal a portion of the total bandwidth. The current techniques that can accomplish this include frequency division multiplexing, time division multiplexing, and code division multiplexing.

3. 3 Data Communications and Computer Networks Chapter 5 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. 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.

4. 4 Data Communications and Computer Networks Chapter 5

5. 5 Data Communications and Computer Networks Chapter 5 Frequency Division Multiplexing Analog signaling is used to transmits the signals. Broadcast radio and television, cable television, and the AMPS cellular phone systems use frequency division multiplexing. This technique is the oldest multiplexing technique. Since it involves analog signaling, it is more susceptible to noise.

6. 6 Data Communications and Computer Networks Chapter 5 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: 1. Synchronous time division multiplexing, and 2. Statistical, or asynchronous time division multiplexing.

7. 7 Data Communications and Computer Networks Chapter 5 Synchronous Time Division Multiplexing The original time division multiplexing. The multiplexor accepts input from attached devices in a round-robin fashion and transmit the data in a never ending pattern. T-1 and ISDN telephone lines are common examples of synchronous time division multiplexing.

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9. 9 Data Communications and Computer Networks Chapter 5 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 a piece of data from that device into the multiplexed stream.

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12. 12 Data Communications and Computer Networks Chapter 5 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.

13. 13 Data Communications and Computer Networks Chapter 5 The T-1 multiplexor stream is a continuous series of frames.

14. 14 Data Communications and Computer Networks Chapter 5 The ISDN multiplexor stream is also a continuous stream of frames. Each frame contains various control and sync info.

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16. 16 Data Communications and Computer Networks Chapter 5 Statistical Time Division Multiplexing A statistical multiplexor transmits only the data from active workstations. If a workstation is not active, no space is wasted on the multiplexed stream. A statistical multiplexor accepts the incoming data streams and creates a frame containing only the data to be transmitted.

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18. 18 Data Communications and Computer Networks Chapter 5 To identify each piece of data, an address is included.

19. 19 Data Communications and Computer Networks Chapter 5 If the data is of variable size, a length is also included.

20. 20 Data Communications and Computer Networks Chapter 5 More precisely, the transmitted frame contains a collection of data groups.

21. 21 Data Communications and Computer Networks Chapter 5 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. 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, 60, more?) onto one fiber. Coarse wavelength division multiplexing combines only a few lambdas.

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23. 23 Data Communications and Computer Networks Chapter 5 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 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.

24. 24 Data Communications and Computer Networks Chapter 5 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 (chip spreading code) To send a binary 1, mobile device transmits the unique code To send a binary 0, mobile device transmits the inverse of code

25. 25 Data Communications and Computer Networks Chapter 5 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

26. 26 Data Communications and Computer Networks Chapter 5 Code Division Multiplexing Example For simplicity, assume 8-chip spreading codes 3 different mobiles 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

27. 27 Data Communications and Computer Networks Chapter 5 Code Division Multiplexing Example 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

28. 28 Data Communications and Computer Networks Chapter 5 Code Division Multiplexing Example 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 Product results: +12 Decode rule: For result near +8, data is binary 1

29. 29 Data Communications and Computer Networks Chapter 5 Code Division Multiplexing Example 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 Product results: -12 Decode rule: For result near -8, data is binary 0

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31. 31 Data Communications and Computer Networks Chapter 5 Business Multiplexing In Action XYZ 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. List some efficient techniques to link the two buildings.

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33. 33 Data Communications and Computer Networks Chapter 5 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. Connect all the terminal outputs and use microwave transmissions to send the data to the mainframe. Collect all the terminal outputs using multiplexing and send the data to the mainframe computer using a conducted line.

34. 34 Data Communications and Computer Networks Chapter 5 Compression This is another technique used to squeeze more data over a communications line If you can compress a data file down to ½ of its original size, the file will obviously 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

35. 35 Data Communications and Computer Networks Chapter 5 Compression Compress a financial file? You want lossless. Compress a video image, movie, or audio file? Lossy is OK Examples of lossless compression include Huffman codes, run-length compression, and Lempel-Ziv compression Examples of lossy compression include MPEG, JPEG, MP3

36. 36 Data Communications and Computer Networks Chapter 5 Run-Length Compression Replace runs of 0s with a count of how many 0s. 00000000000000100000000011000000000000000000001000…001100000000000 ^ (30 0s) 14 90 20 30011

37. 37 Data Communications and Computer Networks Chapter 5 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.

38. 38 Data Communications and Computer Networks Chapter 5 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 difference from previous frame Then store that frame in buffer, etc.

39. 39 Data Communications and Computer Networks Chapter 5 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

40. 40 Data Communications and Computer Networks Chapter 5 Image Compression One image - JPEG, or continuous images such as video - MPEG A color picture can be defined by red/green/blue, or luminance / chrominance / chrominance which are based on RGB values Either way, you have 3 values, each 8 bits, or 24 bits total (2 24 colors!)

41. 41 Data Communications and Computer Networks Chapter 5 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!

42. 42 Data Communications and Computer Networks Chapter 5 JPEG Joint Photographic Experts Group Compresses still images Lossy JPEG compression consists of 3 phases: Discrete cosine transformations (DCT) Quantization Encoding

43. 43 Data Communications and Computer Networks Chapter 5 JPEG Step 1 -DCT Divide image into a series of 8x8 pixel blocks If the original image was 640x480 pixels, the new picture would be 80 blocks x 60 blocks (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 bits for blue, and 8 bits for green)

44. 44 80 blocks 60 blocks 640 x 480 VGA Screen Image Divided into 8 x 8 Pixel Blocks

45. 45 Data Communications and Computer Networks Chapter 5 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

46. 46 Data Communications and Computer Networks Chapter 5 JPEG Step 1 -DCT An image with uniform color changes (little fine detail) has a P array with closely similar values and a corresponding T array with many zero values (next slide) An image with large color changes over a small area (lots of fine detail) has a P array with widely changing values, and thus a T array with many non-zero values

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48. 48 Data Communications and Computer Networks Chapter 5 JPEG Step 2 -Quantization The human eye can’t see small differences in color 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)

49. 49 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

50. 50 Data Communications and Computer Networks Chapter 5 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

51. 51 Data Communications and Computer Networks Chapter 5 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