Decode convolutionally encoded data using Viterbi algorithm
Convolutional sublibrary of Error Detection and Correction
The Viterbi Decoder block decodes input symbols to produce binary output symbols. This block can process several symbols at a time for faster performance.
This block can output sequences that vary in length during simulation. For more information about sequences that vary in length, or variablesize signals, see VariableSize Signal Basics (Simulink).
If the convolutional code uses an alphabet of 2^{n} possible symbols, this block's input vector length is L*n for some positive integer L. Similarly, if the decoded data uses an alphabet of 2^{k} possible output symbols, this block's output vector length is L*k.
This block accepts a column vector input signal with any positive integer value for L. For variablesized inputs, the L can vary during simulation. The operation of the block is governed by the operation mode parameter.
For information about the data types each block port supports, see the Supported Data Types table on this page.
The entries of the input vector are either bipolar, binary, or integer data, depending on the Decision type parameter.
Decision type Parameter  Possible Entries in Decoder Input  Interpretation of Values  Branch metric calculation 

 Real numbers  Positive real: logical zero Negative real: logical one  Euclidean distance 
 0, 1  0: logical zero 1: logical one  Hamming distance 
 Integers between 0 and 2^{b}1, where b is the Number of soft decision bits parameter.  0: most confident decision for logical zero 2^{b}1: most confident decision for logical one Other values represent less confident decisions.  Hamming distance 
To illustrate the soft decision situation more explicitly, the following table lists interpretations of values for 3bit soft decisions.
Input Value  Interpretation 

0  Most confident zero 
1  Second most confident zero 
2  Third most confident zero 
3  Least confident zero 
4  Least confident one 
5  Third most confident one 
6  Second most confident one 
7  Most confident one 
The Viterbi decoder block has three possible methods for transitioning between successive input frames. The Operation mode parameter controls which method the block uses:
In Continuous
mode, the block saves its
internal state metric at the end of each input, for use with the next frame.
Each traceback path is treated independently.
In Truncated
mode, the block treats each input
independently. The traceback path starts at the state with the best metric
and always ends in the allzeros state. This mode is appropriate when the
corresponding Convolutional Encoder block has
its Operation mode set to Truncated (reset
every frame)
.
In Terminated
mode, the block treats each input
independently, and the traceback path always starts and ends in the
allzeros state. This mode is appropriate when the uncoded message signal
(that is, the input to the corresponding Convolutional Encoder block) has
enough zeros at the end of each input to fill all memory registers of the
feedforward encoder. If the encoder has k
input streams
and constraint length vector constr
(using the polynomial
description), “enough” means
k*max(constr1)
. For feedback encoders, this mode is
appropriate if the corresponding Convolutional Encoder block has
Operation mode set to Terminate trellis by
appending bits
.
Note
When this block outputs sequences that vary in length during simulation and
you set the Operation mode to
Truncated
or
Terminated
, the block's state resets at every
input time step.
Use the Continuous
mode when the input signal contains
only one symbol.
The reset port is usable only when the Operation mode
parameter is set to Continuous
. Selecting
Enable reset input port gives the block an additional input
port, labeled Rst
. When the Rst
input is
nonzero, the decoder returns to its initial state by configuring its internal memory
as follows:
Sets the allzeros state metric to zero.
Sets all other state metrics to the maximum value.
Sets the traceback memory to zero.
Using a reset port on this block is analogous to setting Operation
mode in the Convolutional Encoder block to
Reset on nonzero input via port
.
The reset port supports double
or boolean
typed signals.
There are three main components to the Viterbi decoding algorithm. They are branch metric computation (BMC), addcompare and select (ACS), and traceback decoding (TBD). The following diagram illustrates the signal flow for a k/n rate code.
As an example of a BMC diagram, a 1/2 rate, nsdec = 3 signal flow would be as follows.
$$\begin{array}{c}WL=nsdec+n1\\ n=2\Rightarrow WL=4\end{array}$$
The ACS component is generally illustrated as shown in the following diagram.
Where WL2
is specified on the mask by the
user.
In the flow diagrams above, inNT, bMetNT , stMetNT, and outNT are numerictype
(FixedPoint Designer) objects, and bMetFIMATH and
stMetFIMATH, are fimath (FixedPoint Designer)
objects.
For some commonly used puncture patterns for specific rates and polynomials, see the last three references.
The Perform FixedPoint Hard Decision Viterbi Decoding and Perform FixedPoint Soft Decision Viterbi Decoding examples configure the Viterbi decoder block to use fixedpoint processing for hard and softdecision convolutional decoding.
The examples highlight the fixedpoint modeling attributes of the Viterbi decoder, using a familiar layout.
The two simulations have a similar structure and have most parameters in common. A data source produces a random binary sequence that is convolutionally encoded, BPSK modulated, and passed through an AWGN channel.
The Convolutional encoder is configured as a rate 1/2 encoder. For every 2 bits, the encoder adds another 2 redundant bits. To accommodate this, and add the correct amount of noise, the Eb/No (dB) parameter of the AWGN block is in effect halved by subtracting 10*log10(2).
For the harddecision case, the BPSK demodulator produces hard decisions, at the receiver, which are passed onto the decoder.
For the softdecision case, the BPSK demodulator produces soft decisions, at the receiver, using the loglikelihood ratio. These soft outputs are 3bit quantized and passed onto the decoder.
After the decoding, the simulation compares the received decoded symbols with the original transmitted symbols in order to compute the bit error rate. The simulation ends after processing 100 bit errors or 1e6 bits, whichever comes first.
Fixedpoint modeling enables bittrue simulations which take into account hardware implementation considerations and the dynamic range of the data/parameters. For example, if the target hardware is a DSP microprocessor, some of the possible word lengths are 8, 16, or 32 bits, whereas if the target hardware is an ASIC or FPGA, there may be more flexibility in the word length selection.
To enable fixedpoint Viterbi decoding, the block input must be of type ufix1 (unsigned integer of word length 1) for hard decisions. Based on this input (either a 0 or a 1), the internal branch metrics are calculated using an unsigned integer of word length = (number of output bits), as specified by the trellis structure (which equals 2 for the harddecision example).
For soft decisions, the block input must be of type ufixN (unsigned integer of word length N), where N is the number of softdecision bits, to enable fixedpoint decoding. The block inputs must be integers in the range 0 to 2^{N1}. The internal branch metrics are calculated using an unsigned integer of word length = (N + number of output bits  1), as specified by the trellis structure (which equals 4 for the softdecision example).
The State metric word length is specified by the user and usually must be greater than the branch metric word length already calculated. You can tune this to be the most suitable value (based on hardware and/or data considerations) by reviewing the logged data for the system.
Enable the logging by selecting Apps > FixedPoint
Tool. In the FixedPoint Setting GUI, set the
Fixedpoint instruments mode to Minimums, maximums
and overflows
, and rerun the simulation. If you see overflows, it
implies the data did not fit in the selected container. You could either increase
the size of the word length (if your hardware allows it) or try scaling the data
prior to processing it. Based on the minimum and maximum values of the data, you are
also able to determine whether the selected container is of the appropriate
size.
Try running simulations with different values of State metric word length to get an idea of its effect on the algorithm. You should be able to narrow down the parameter to a suitable value that has no adverse effect on the BER results.
To run the same model with double precision data, Select Apps >
FixedPoint Tool. In the FixedPoint Tool GUI, select the
Data type override to be Double
. This
selection overrides all data type settings in all the blocks to use double
precision. For the Viterbi Decoder block, as Output
type was set to Boolean
, this parameter should
also be set to double.
Upon simulating the model, note that the doubleprecision and fixedpoint BER results are the same. They are the same because the fixedpoint parameters for the model have been selected to avoid any loss of precision while still being most efficient.
The two models are set up to run from within BERTool to generate a simulation curve that compares the BER performance for harddecision versus softdecision decoding.
To generate simulation results for Perform FixedPoint Hard Decision Viterbi Decoding, do the following:
Open the Perform FixedPoint Hard Decision Viterbi Decoding example to get the simulink model on path.
Type bertool
at the MATLAB command prompt.
Go to the Monte Carlo pane.
Set the Eb/No range to
2:5
.
Set the Simulation model to
cm_viterbi_harddec_fixpt
. Make sure that the
model is on path.
Set the BER variable name to
BER
.
Set the Number of errors to
100
, and the Number of bits
to 1e6
.
Press Run and a plot is generated.
To generate simulation results for Perform FixedPoint Soft Decision Viterbi Decoding, open the example
to get simulink model on path, change the change the Simulation
model to cm_viterbi_softdec_fixpt
, and press
Run.
Notice that, as expected, 3bit softdecision decoding is better than harddecision decoding, roughly to the tune of 1.7 dB, and not 2 dB as commonly cited. The difference in the expected results could be attributed to the imperfect quantization of the soft outputs from the demodulator.
MATLAB^{®} structure that contains the trellis description of the convolutional encoder. Use the same value here and in the corresponding Convolutional Encoder block.
Select this check box to specify a punctured input code. The field, Punctured code, appears.
Constant puncture pattern vector used at the transmitter (encoder). The
puncture vector is a pattern of 1
s and
0
s. The 0
s indicate the punctured
bits. When you select Punctured code, the
Punctured vector field appears.
When you check this box, the decoder opens an input port labeled
Era
. Through this port, you can specify an erasure
vector pattern of 1
s and 0
s, where the
1
s indicate the erased bits.
For these erasures in the incoming data stream, the decoder does not
update the branch metric. The widths and the sample times of the erasure and
the input data ports must be the same. The erasure input port can be of data
type double
or Boolean
.
Specifies the use of Unquantized
,
Hard Decision
, or Soft
Decision
for the branch metric calculation.
Unquantized
decision uses the Euclidean
distance to calculate the branch metrics.
Soft Decision
and Hard
Decision
use the Hamming distance to calculate the
branch metrics, where Number of soft decision
bits equals 1
.
The number of soft decision bits to represent each input. This field is
active only when Decision type is set to
Soft Decision
.
Select this check box to throw an error when quantized input values are
out of range. This check box is active only when Decision
type is set to Soft Decision
or
Hard Decision
.
The number of trellis branches to construct each traceback path.
Method for transitioning between successive input frames:
Continuous
,
Terminated
, and
Truncated
.
Note
When this block outputs sequences that vary in length during
simulation and you set the Operation mode to
Truncated
or
Terminated
, the block's state resets at
every input time step.
When you check this box, the decoder opens an input port labeled
Rst
. Providing a nonzero input value to this port
causes the block to set its internal memory to the initial state before
processing the input data.
When you select this option, the Viterbi Decoder block resets after
decoding the encoded data. This option is available only when you set
Operation mode to
Continuous
and select Enable reset
input port. You must enable this option for HDL
support.
The output signal's data type can be double
,
single
, boolean
,
int8
, uint8
,
int16
, uint16
,
int32
, uint32
, or set to
'Inherit via internal rule'
or 'Smallest
unsigned integer'
.
When set to 'Smallest unsigned integer'
, the output
data type is selected based on the settings used in the Hardware
Implementation pane of the Configuration Parameters dialog
box of the model. If ASIC/FPGA
is selected in the
Hardware Implementation pane, the output data type
is ufix(1)
. For all other selections, it is an unsigned
integer with the smallest specified wordlength corresponding to the char
value (e.g., uint8
).
When set to 'Inherit via internal rule'
(the default
setting), the block selects doubletyped outputs for double inputs,
singletyped outputs for single inputs, and behaves similarly to the
'Smallest unsigned integer'
option for all other
typed inputs.
Port  Supported Data Types 

Input 

Output 

[1] Clark, George C., and J. Bibb Cain. ErrorCorrection Coding for Digital Communications. Applications of Communications Theory. New York: Plenum Press, 1981.
[2] Gitlin, Richard D., Jeremiah F. Hayes, and Stephen B. Weinstein. Data Communications Principles. Applications of Communications Theory. New York: Plenum Press, 1992.
[3] Heller, J., and I. Jacobs. “Viterbi Decoding for Satellite and Space Communication.” IEEE Transactions on Communication Technology 19, no. 5 (October 1971): 835–48. https://doi.org/10.1109/TCOM.1971.1090711.
[4] Yasuda, Y., K. Kashiki, and Y. Hirata. “HighRate 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.
[5] Haccoun, D., and G. Begin. “HighRate 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.
[6] Begin, G., D. Haccoun, and C. Paquin. “Further Results on HighRate 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.
[7] Moision, B. "A Truncation Depth Rule of Thumb for Convolutional Codes." In Information Theory and Applications Workshop (January 27 2008February 1 2008, San Diego, California), 555557. New York: IEEE, 2008.