Communications Blockset™

Convolutional Encoder - Create convolutional code from binary data

Library

Convolutional sublibrary of Channel Coding

Description

The Convolutional Encoder block encodes a sequence of binary input vectors to produce a sequence of binary output vectors. This block can process multiple symbols at a time.

Input and Output Sizes

If the encoder takes k input bit streams (that is, can receive 2^k possible input symbols), this block's input vector length is L*k for some positive integer L. Similarly, if the encoder produces n output bit streams (that is, can produce 2ⁿ possible output symbols), this block's output vector length is L*n.

The input can be a sample-based vector with L = 1, or a frame-based column vector with any positive integer for L.

For both its inputs and outputs for the data ports, the block supports double, single, boolean, int8, uint8, int16, uint16, int32, uint32, and ufix1. The port data types are inherited from the signals that drive the block. The input reset port supports double and boolean typed signals.

Note This block works for cases , where it has the same values for constraint lengths in each input stream (e.g., constraint lengths of [2 2] or [7 7] will work, but [5 4] will not).

Specifying the Encoder

To define the convolutional encoder, use the Trellis structure parameter. This parameter is a MATLAB^® structure whose format is described in Trellis Description of a Convolutional Encoder in the Communications Toolbox™ documentation. You can use this parameter field in two ways:

If you have a variable in the MATLAB workspace that contains the trellis structure, enter its name as the Trellis structure parameter. This way is preferable because it causes Simulink^® to spend less time updating the diagram at the beginning of each simulation, compared to the usage described next.
If you want to specify the encoder using its constraint length, generator polynomials, and possibly feedback connection polynomials, use a poly2trellis command within the Trellis structure field. For example, to use an encoder with a constraint length of 7, code generator polynomials of 171 and 133 (in octal numbers), and a feedback connection of 171 (in octal), set the Trellis structure parameter to
```
poly2trellis(7,[171 133],171)
```

The encoder registers begin in the all-zeros state. You can configure the encoder so that it resets its registers to the all-zeros state during the course of the simulation. To do this, set the Operation mode to Reset on nonzero input via port. The block then opens a second input port, labeled Rst. The signal at the Rst port is a scalar signal. When it is nonzero, the encoder resets before processing the data at the first input port.

Dialog Box

Trellis structure

MATLAB structure that contains the trellis description of the convolutional encoder.

Operation mode

In Continuous mode, the block retains the encoder states at the end of each frame, for use with the next frame.

In Truncated (reset every frame) mode, the block treats each frame independently. I.e., the encoder states are reset to all-zeros states at the start of each frame.

In Terminate trellis by appending bits mode, the block treats each frame independently. For each input frame, extra bits are used to set the encoder states to all-zeros states at the end of the frame. The output length is given by , where x is the number of input bits, and (or, in the case of multiple constraint lengths, s =sum(ConstraintLength(i)-1)). This mode is supported for frame based inputs only.

In Reset on nonzero input via port mode, the block has an additional input port, labeled Rst. When the Rst input is nonzero, the encoder resets to the all-zeros state.

Puncture code

Selecting this option opens the field Puncture vector.

Puncture vector

Vector used to puncture the encoded data. The puncture vector is a pattern of 1s and 0s where the 0s indicate the punctured bits. This field appears when the check box Punctured code is selected.

Puncture Pattern Examples

For some commonly used puncture patterns for specific rates and polynomials, see the last three references.

References

[1] Clark, George C. Jr. and J. Bibb Cain, Error-Correction Coding for Digital Communications, New York, Plenum Press, 1981.

[2] Gitlin, Richard D., Jeremiah F. Hayes, and Stephen B. Weinstein, Data Communications Principles, New York, Plenum, 1992.

[3] Yasuda, Y., et. al., "High rate punctured convolutional codes for soft decision Viterbi decoding," IEEE Transactions on Communications, Vol. COM-32, No. 3, pp 315–319, March 1984.

[4] Haccoun, D., and Begin, G., "High-rate punctured convolutional codes for Viterbi and Sequential decoding," IEEE Transactions on Communications, Vol. 37, No. 11, pp 1113–1125, Nov. 1989.

[5] Begin, G., et.al., "Further results on high-rate punctured convolutional codes for Viterbi and sequential decoding," IEEE Transactions on Communications, Vol. 38, No. 11, pp 1922–1928, Nov. 1990.

Convolutional Deinterleaver

Convolutional Interleaver