Chapter Five

1
Chapter Five

• Making Connections Efficient
• Multiplexing and Compression

2
Introduction
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

3
Frequency Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

4
Frequency Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

5
Chapter Five - Making Connections
Efficient Multiplexing and Compression
6
Frequency Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

7
Time Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

8
Synchronous Time Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

9
Chapter Five - Making Connections
Efficient Multiplexing and Compression
10
Synchronous Time Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

11
Chapter Five - Making Connections
Efficient Multiplexing and Compression
12
Chapter Five - Making Connections
Efficient Multiplexing and Compression
13
Synchronous Time Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

14
Synchronous Time Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• The T-1 multiplexor stream is a continuous series
  of frames

15
Synchronous Time Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• The ISDN multiplexor stream is a also a
  continuous series of frames. Each frame contains
  various control and sync info

16
Synchronous Time Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• Likewise, SONET incorporates a continuous series
  of frames.

17
Statistical Time Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

18
Chapter Five - Making Connections
Efficient Multiplexing and Compression
19
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• To identify each piece of data, an address is
  included

20
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• If the data is of variable size, a length is also
  included

21
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• More precisely, the transmitted frame contains a
  collection of data groups

22
Wavelength Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• Wavelength division multiplexing multiplexes
  multiple data streams onto a single fiber optic
  line
• Different wavelength lasers (called lambdas)
  transmit the multiple signals

23
Wavelength Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

24
Chapter Five - Making Connections
Efficient Multiplexing and Compression
25
Discrete Multitone (DMT)
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• A multiplexing technique commonly found in
  digital subscriber line (DSL) systems
• DMT combines hundreds of different signals, or
  subchannels, into one stream

26
Discrete Multitone (DMT)
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

27
Chapter Five - Making Connections
Efficient Multiplexing and Compression
28
Code Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

29
Code Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

30
Code Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

31
Code Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

32
Code Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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

33
Code Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• Base station decode for Mobile A
• Signal received 3, -1, -1, 1, 1, -1, -3, 3
• Mobile As 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

34
Code Division Multiplexing
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• Base station decode for Mobile B
• Signal received 3, -1, -1, 1, 1, -1, -3, 3
• Mobile Bs 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

35
Chapter Five - Making Connections
Efficient Multiplexing and Compression
36
Chapter Five - Making Connections
Efficient Multiplexing and Compression
37
Compression
Chapter Five - Making Connections
Efficient Multiplexing and 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

38
Compression
Chapter Five - Making Connections
Efficient Multiplexing and 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

39
Chapter Five - Making Connections
Efficient Multiplexing and Compression

Run-Length Compression Replace runs of 0s with a
count of how many 0s. 000000000000001000000000110
00000000000000000001000001100000000000
(30 0s) 14 9 0
20 30 0 11
40
Run-Length Compression
Chapter Five - Making Connections
Efficient Multiplexing and 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
Relative or Differential Encoding (Lossy)
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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
Chapter Five - Making Connections
Efficient Multiplexing and Compression

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 0 0 0 0 0 1 0 0 0 0 -1 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 Difference
43
Image Compression
Chapter Five - Making Connections
Efficient Multiplexing and 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 (224 colors!)

44
Image Compression
Chapter Five - Making Connections
Efficient Multiplexing and 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
JPEG
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• Joint Photographic Experts Group
• Compresses still images
• Lossy
• JPEG compression consists of 3 phases
• Discrete cosine transformations (DCT)
• Quantization
• Encoding

46
JPEG Step 1 - DCT
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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 BW, 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
80 blocks
60 blocks
640 x 480 VGA Screen Image Divided into 8 x 8
Pixel Blocks
48
JPEG Step 1 - DCT
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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
Chapter Five - Making Connections
Efficient Multiplexing and Compression

15 18 21 24 28 32 36 40
19 22 25 28 32 36 40 44
22 25 28 32 36 40 44 48
26 29 32 35 39 43 47 51
30 34 38 42 46 51 56 61
34 38 42 46 51 56 61 66
38 42 46 51 56 61 66 72
43 48 53 58 63 68 74 80
628 -123 21 -8 0 -2 0 -1
-185 23 -5 0 0 0 0 0
10 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
-1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
P Array
T Array
50
JPEG Step 1 - DCT
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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
JPEG Step 2 - Quantization
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• The human eye cant 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 doesnt take into account that upper left
  of matrix has more action (the less subtle
  features of the image, or low spatial frequencies)

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
Qij Round(Tij / Uij), for i 0, 1,
2, 7 and j 0, 1, 2, 7
53
JPEG Step 3 - Encoding
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• Now take the quantized matrix Q and perform
  run-length encoding on it
• But dont just go across the rows. Longer runs
  of zeros if you perform the run-length encoding
  in a diagonal fashion (next slide)

54
Chapter Five - Making Connections
Efficient Multiplexing and Compression

55
JPEG
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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
Business Multiplexing In Action
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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
Chapter Five - Making Connections
Efficient Multiplexing and Compression

58
Possible Solutions
Chapter Five - Making Connections
Efficient Multiplexing and Compression

• 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