Convolutional Encoder
Encodes binary data using a convolutional encoding scheme.
blockType: ConvolutionalEncoder
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Path in the library: |
Description
Block Convolutional Encoder encodes the input binary message using a convolutional encoding scheme specified by the lattice structure.
The block icon and the number of input and output ports change depending on the value of the parameters Specify initial state via input port and Output final state.
Ports
Output
#
OUT_1
—
output signal
binary column vector
Details
A convolutional code word returned as a binary column vector.
This port has no name on the block icon.
If the encoder produces output bit streams (that is, it can produce possible output characters), the length of the output vector of the block is for some positive integer . This output inherits the data type from the In input.
| Data types |
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| Complex numbers support |
No |
#
FSt
—
the final state of the registers
a non-negative integer
Details
The final state of the encoder registers for each frame output from the block, returned as a non-negative integer.
Dependencies
To use this port, set the parameter Operation mode one of the values is: Continuous, Truncated (reset every frame), or Reset on nonzero input via port and check the box Output final state.
| Data types |
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| Complex numbers support |
No |
Input
#
In
—
The input signal
binary column vector
Details
An input message specified as a binary column vector. This port remains unnamed until the second input port is enabled.
The encoder accepts only one input bit stream .
Example: [1 1 0 1 0 0 1 1] defines a message as a binary vector of strings with eight elements.
| Data types |
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| Complex numbers support |
No |
#
ISt
—
the initial state of the registers
a non-negative integer
Details
The initial state of the encoder registers for each frame entering the block is set as a non-negative integer.
Dependencies
To use this port, set the parameter Operation mode meaning Truncated (reset every frame) and check the box Specify initial state via input.
| Data types |
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| Complex numbers support |
No |
Parameters
Main
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Trellis structure —
description of the convolutional code through a code grid
String
Details
A lattice description of a convolutional code, defined as a lattice structure for a code with a velocity , where is the number of input bit streams, and — the number of output bit streams.
To create the lattice structure, you can use the function poly2trellis or set it manually.
The lattice structure contains the following fields:
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numInputSymbols— the number of characters entering the input of the encoding device is set as an integer equal to , where K is the number of input bit streams. -
numOutputSymbols— the number of characters coming to the output of the encoding device is set as an integer equal to , where K is the number of output bit streams. -
numStates— the number of states in the encoding device, set as a power of 2. -
nextStates— the following states for all combinations of current states and current inputs, specified as a matrix of integers. The size of the matrix should benumStateson . -
outputs— outputs for all combinations of current states and current inputs, specified as a matrix of octal numbers. The size of the matrix should benumStateson .
| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
Yes |
#
Operation mode —
encoded frame completion method
Continuous | Truncated (reset every frame)
Details
The completion method of the encoded frame, set as one of these mode values.
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Continuous— The block saves the encoder states at the end of each input for use in the next frame. -
Truncated (reset every frame)— the unit processes each input independently. At the beginning of each input frame, the encoder states are reset to the "all zeros" state or, if the Specify initial state via input port checkbox is selected, to the state specified by the ISt port.
| Values |
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| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
No |
#
Specify initial state via input port —
set the initial state via the input port
Logical
Details
Select this option to add the ISt input port to the block.
Dependencies
To use this parameter, set for the parameter Operation mode meaning Truncated (reset every frame).
| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
No |
#
Output final state —
output the final state
Logical
Details
Select this option to add the FSt output port to the block.
Dependencies
To use this parameter, set for the parameter Operation mode meaning Continuous or Truncated (reset every frame).
| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
No |
#
Puncture code —
enabling convolutional code punching
Logical
Details
Check this box to use the parameter Puncture vector.
| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
No |
# Puncture vector — the perforation pattern
Details
The perforation pattern, defined as a vector. The perforation vector is a template from 1 and 0, where 0 denote the bits excluded from the output encoded data.
The length of the vector must be an integer multiple of length(In), the length of the input message vector.
Some commonly used puncture patterns for certain velocities and polynomials are given in [3], [4], [5].
Dependencies
To use this option, select the Puncture code checkbox.
| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
Yes |
More about convolutional coding
Convolutional coding is an error—controlled coding that has memory. In particular, the calculations and the encoded output depend on the current set of input symbols and on the number of previous input symbols, which varies depending on the lattice configuration.
The convolutional encoder outputs bits for each input bits. In the process of modeling, the input data can be multiples bits.
Using a lattice structure that defines a set of generator polynomials, you can simulate unsystematic, systematic convolutional codes with direct or systematic feedback.
To decode the output signal of the convolutional code, you can use:
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Block Viterbi Decoder — Uses Viterbi algorithm with hard and soft decoding.
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The APP Decoder block uses a posteriori probabilistic decoder to soft decode the output of convolutional codes.
Definition of the encoder
To define a convolutional encoder, use the parameter Trellis structure. The lattice structure can be specified by calling the function poly2trellis.
For example, to use an encoder with a length limit 7, code generator polynomials 171 and 133 (in octal numbers), set for the parameter Trellis structure value poly2trellis(7,[171 133]).
Literature
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Clark, George C., and J. Bibb Cain. Error-Correction Coding for Digital Communications. Applications of Communications Theory. New York: Plenum Press, 1981.
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Gitlin, Richard D., Jeremiah F. Hayes, and Stephen B. Weinstein. Data Communications Principles. Applications of Communications Theory. New York: Plenum Press, 1992.
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Yasuda, Y., K. Kashiki, and Y. Hirata. “High-Rate Punctured Convolutional Codes for Soft Decision Viterbi Decoding.” IEEE Transactions on Communications 32, no. 3 (March 1984): 315–19. https://doi.org/10.1109/TCOM.1984.1096047.
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Haccoun, D., and G. Begin. “High-Rate Punctured Convolutional Codes for Viterbi and Sequential Decoding.” IEEE Transactions on Communications 37, no. 11 (November 1989): 1113–25. https://doi.org/10.1109/26.46505.
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Begin, G., D. Haccoun, and C. Paquin. “Further Results on High-Rate Punctured Convolutional Codes for Viterbi and Sequential Decoding.” IEEE Transactions on Communications 38, no. 11 (November 1990): 1922–28. https://doi.org/10.1109/26.61470.