Skip to content Skip to navigation

Connexions

You are here: Home » Content » Convolutional Codes

Navigation

Content Actions

  • Download module PDF
  • Add to ...
    Add the module to:
    • My Favorites
    • A lens
    • An external social bookmarking service
    • My Favorites (What is 'My Favorites'?)
      'My Favorites' is a special kind of lens which you can use to bookmark modules and collections directly in Connexions. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need a Connexions account to use 'My Favorites'.
    • A lens (What is a lens?)

      Definition of a lens

      Lenses

      A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

      What is in a lens?

      Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

      Who can create a lens?

      Any individual Connexions member, a community, or a respected organization.

      What are tags? tag icon

      Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

    • External bookmarks
  • E-mail the author
  • Rate this module (How does the rating system work?)

    Rating system

    Ratings

    Ratings allow you to judge the quality of modules. If other users have ranked the module then its average rating is displayed below. Ratings are calculated on a scale from one star (Poor) to five stars (Excellent).

    How to rate a module

    Hover over the star that corresponds to the rating you wish to assign. Click on the star to add your rating. Your rating should be based on the quality of the content. You must have an account and be logged in to rate content.

    (0 ratings)

Recently Viewed

This feature requires Javascript to be enabled.

Convolutional Codes

Module by: Behnaam Aazhang

Summary: A description of channel coding using convolutional codes.

Note: Your browser may not currently support MathML. See our browser support page for additional details. You can always view the correct math in the PDF version.

Convolutional codes are one type of code used for channel coding. Another type of code used is block coding.

Convolutional codes

In convolutional codes, each block of kk bits is mapped into a block of nn bits but these nn bits are not only determined by the present kk information bits but also by the previous information bits. This dependence can be captured by a finite state machine.

Example 1

A rate 12 1 2 convolutional coder k=1 k 1 , n=2 n 2 with memory length 2 and constraint length 3.

Figure 1
Figure 1 (Figure7-55.png)

Since the length of the shift register is 2, there are 4 different rates. The behavior of the convolutional coder can be captured by a 4 state machine. States: 00, 01, 10, 11,

For example, arrival of information bit 0 transitions from state 10 to state 01.

The encoding and the decoding process can be realized in trellis structure.

Figure 2
Figure 2 (Figure7-56.png)

If the input sequence is

     1 1 0 0

the output sequence would be

     11 10 10 11

The transmitted codeword is then 11 10 10 11. If there is one error on the channel 11 00 10 11

Figure 3
Figure 3 (Figure7-57.png)

Starting from state 00 the Hamming distance between the possible paths and the received sequence is measured. At the end, the path with minimum distance to the received sequence is chosen as the correct trellis path. The information sequence will then be determined.

Convolutional coding lends itself to very efficient trellis based encoding and decoding. They are very practical and powerful codes.

Comments, questions, feedback, criticisms?

Send feedback