Create convolutional code from binary data
Convolutional sublibrary of Error Detection and Correction
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.
This block can accept inputs that vary in length during simulation. For more information about variable-size signals, see Variable-Size Signal Basics (Simulink).
If the encoder takes k input bit streams (that is, it can receive 2k possible input symbols), the block input vector length is L*k for some positive integer L. Similarly, if the encoder produces n output bit streams (that is, it can produce 2n possible output symbols), the block output vector length is L*n.
This block accepts a column vector input signal with any positive integer for L. For variable-size inputs, the L can vary during simulation. The operation of the block is governed by the Operation mode parameter.
For both its inputs and outputs for the data ports, the block supports
ufix1. The port data types are
inherited from the signals that drive the block. The input reset port supports
boolean typed signals.
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 Code. 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 in 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 in the
Trellis structure parameter. 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
The encoder registers begin in the all-zeros state. Set the Operation
mode parameter to
Reset on nonzero input via
port to reset all encoder registers to the all-zeros state during
the simulation. This selection opens a second input port, labeled
Rst, which accepts a scalar-valued input signal. When the
input signal is nonzero, the block resets before processing the data at the first
input port. To reset the block after it processes the data at the first input port,
select Delay reset action to next time step.
MATLAB structure that contains the trellis description of the convolutional encoder.
Continuous mode, the block retains the
encoder states at the end of each input, for use with the next frame.
Truncated (reset every frame) mode, the
block treats each input independently. The encoder states are reset to
all-zeros state at the start of each input.
When this block outputs sequences that vary in length during
simulation and you set the Operation mode to
Truncated (reset every frame) or
Terminate trellis by appending bits, the
block's state resets at every input time step.
Terminate trellis by appending bits mode,
the block treats each input independently. For each input frame, extra bits
are used to set the encoder states to all-zeros state 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,
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).
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
When you select Delay reset action to next time step,
the Convolutional Encoder block resets after computing the encoded data.
This check box only appears when you set the Operation
mode parameter to
Reset on nonzero input via
The delay in the reset action allows the block to support HDL code generation. In order to generate HDL code, you must have an HDL Coder™ license.
When you select Output final state, the second output
port signal specifies the output state for the block. The output signal is a
scalar, integer value. You can select Output final
state for all operation modes except
trellis by appending bits .
When you select Specify initial state via input port
the second input port signal specifies the starting state for every frame in
the block. The input signal must be a scalar, integer value.
Specify initial state via input port appears only
Truncated operation mode.
Selecting this option opens the field Puncture vector.
Vector used to puncture the encoded data. The puncture vector is a pattern
0s where the
0s indicate the punctured bits. This field appears
when you select Punctured code.
For some commonly used puncture patterns for specific rates and polynomials, see the last three references listed on this page.
 Clark, George C. Jr. and J. Bibb Cain, Error-Correction Coding for Digital Communications, New York, Plenum Press, 1981.
 Gitlin, Richard D., Jeremiah F. Hayes, and Stephen B. Weinstein, Data Communications Principles, New York, Plenum, 1992.
 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.
 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.
 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.
This block supports HDL code generation using HDL Coder. HDL Coder provides additional configuration options that affect HDL implementation and synthesized logic. For more information on implementations, properties, and restrictions for HDL code generation, see Convolutional Encoder in the HDL Coder documentation.