# comm.ConvolutionalEncoder

Convolutionally encode binary data

## Description

The comm.ConvolutionalEncoder System object™ encodes a sequence of binary input vectors to produce a sequence of binary output vectors.

To convolutionally encode binary data:

1. Create the comm.ConvolutionalEncoder object and set its properties.

2. Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?

## Creation

### Description

example

convencoder = comm.ConvolutionalEncoder creates a convolutional encoder System object.

example

convencoder = comm.ConvolutionalEncoder(trellis) sets the TrellisStructure property to trellis.

example

convencoder = comm.ConvolutionalEncoder(___,Name,Value) sets Properties using one or more name-value arguments in addition to any argument combinations in previous syntaxes. For example, 'TerminationMethod','Continuous' specifies the termination method as continuous to retain the encoder states at the end of each input vector for use with the next input vector.

## Properties

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Unless otherwise indicated, properties are nontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and the release function unlocks them.

If a property is tunable, you can change its value at any time.

For more information on changing property values, see System Design in MATLAB Using System Objects.

Trellis description of the convolutional code, specified as a structure that contains the trellis description for a rate KN code. K is the number of input bit streams, and N is the number of output bit streams.

You can either use the poly2trellis function to create the trellis structure or create it manually. For more about this structure, see Trellis Description of a Convolutional Code and the istrellis function.

The trellis structure contains these fields.

Number of symbols input to the encoder, specified as an integer equal to 2K, where K is the number of input bit streams.

Data Types: double

Number of symbols output from the encoder, specified as an integer equal to 2N, where N is the number of output bit streams.

Data Types: double

Number of states in the encoder, specified as a power of 2.

Data Types: double

Next states for all combinations of current states and current inputs, specified as a matrix of integers. The matrix size must be numStates-by-2K.

Data Types: double

Outputs for all combinations of current states and current inputs, specified as a matrix of octal numbers. The matrix size must be numStates-by-2K.

Data Types: double

Data Types: struct

Termination method of the encoded frame, specified as one of these values.

• 'Continuous' — The System object retains the encoder states at the end of each input vector for use with the next input vector.

• 'Truncated' — The System object treats each input vector independently. The encoder states are reset at the start of each input vector. If you set the InitialStateInputPort property to 0 (false), the object resets its states to the all-zeros state. If you set the InitialStateInputPort property to 1(true), the object resets its states to the values you specify in the InitialStateInputPort input.

• 'Terminated' — The System object treats each input vector independently. For each input vector, the object uses extra bits to set the encoder states to all-zeros states at the end of the vector. For a rate K/N code, the object outputs a vector of length $N×\left(L+S\right)}{K}$, where S = constraintLength – 1 (or, in the case of multiple constraint lengths, S = sum(constraintLength(i)–1)). L is the length of the input. constraintLength – 1 is defined as log2(NumStates).

Data Types: char | string

Option to enable the encoder reset input, specified as a logical 1(true) or 0 (false). Set this property to 1 (true) to enable the additional input to the object. When this additional reset input is a nonzero value, the internal states of the encoder reset to their initial conditions.

#### Dependencies

To enable this property, set the TerminationMethod property to 'Continuous'.

Data Types: logical

Option to delay the output reset, specified as one of these logical values.

• 1 (true) — The reset of the internal states of the encoder occurs after the object computes the encoded data.

• 0 (false) — The reset of the internal states of the encoder occurs before the object computes the encoded data.

#### Dependencies

To enable this property, set the ResetInputPort property to 1 (true).

Data Types: logical

Option to enable the initial state input, specified as a logical 1 (true) or 0 (false). When you set this property to 1 (true), the object enables you to specify the initial state of the encoder for each input vector.

#### Dependencies

To enable this property, set the TerminationMethod property to 'Truncated'.

Data Types: logical

Option to enable the final state output, specified as a logical 1 (true) or 0 (false). Set this property to 1 (true) to obtain the final state of the encoder as an output.

#### Dependencies

To enable this property, set the TerminationMethod property to 'Continuous' or 'Truncated'.

Data Types: logical

Source of the puncture pattern, specified as one of these values.

• 'None' — The object does not apply puncturing.

• 'Property' — The object punctures the code. This puncturing is based on the puncture pattern vector that you specify for the PuncturePattern property.

#### Dependencies

To enable this property, set the TerminationMethod property to 'Continuous' or 'Truncated'.

Data Types: char | string

Puncture pattern vector to puncture the encoded data, specified as a column vector. The vector must contain 1s and 0s, where 0 indicates the position of punctured bits or excluded bits.

#### Dependencies

To enable this property, set the TerminationMethod property to 'Continuous' or 'Truncated' and the PuncturePatternSource property to 'Property'.

Data Types: double

## Usage

### Description

example

codeword = convencoder(message) encodes the input message using the convolutional encoding scheme specified by the trellis structure. codeword is the encoded codeword. message and codeword are column vectors of numeric, logical, or unsigned fixed-point values with word length 1 (fi (Fixed-Point Designer) object).

codeword = convencoder(message,initstate) specifies the initial state of the encoder for each input vector. To enable this syntax, set the TerminationMethod property to 'Truncated' and the InitialStateInputPort property to 1 (true).

codeword = convencoder(message,resetstate) specifies the input to reset the internal states of the encoder. To enable this syntax, set the TerminationMethod property to 'Continuous' and the ResetInputPort property to 1 (true).

[codeword,finalstate] = convencoder(message) also returns the final state of the encoder. To enable this syntax, set the FinalStateOutputPort property to 1 (true) and the TerminationMethod property to 'Continuous' or 'Truncated'.

### Input Arguments

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Input message, specified as a binary-valued column vector.

Data Types: double | int8 | fi(data,0,1)

Initial state of the encoder, specified as an integer.

#### Dependencies

To enable this argument, set the TerminationMethod property to 'Truncated' and the InitialStateInputPort property to 1 (true).

Data Types: double

Reset for internal states of the encoder, specified as a numeric or logical 1 (true) or 0 (false).

#### Dependencies

To enable this argument, set the TerminationMethod property to 'Continuous' and the ResetInputPort property to 1 (true).

Data Types: double | logical

### Output Arguments

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Convolutionally encoded message, returned as a binary-valued column vector. This output vector has the same data type and orientation as input message.

When the convolutional encoder represents a rate K/N code, the length of the input vector equals K×L for some positive integer L. The object sets the length of this output vector, to L×N.

Data Types: double | int8 | fi(data,0,1)

Final state of the encoder, returned as an integer.

#### Dependencies

To enable this argument, set the TerminationMethod property to 'Continuous' or 'Truncated'.

Data Types: double

## Object Functions

To use an object function, specify the System object as the first input argument. For example, to release system resources of a System object named obj, use this syntax:

release(obj)

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 step Run System object algorithm release Release resources and allow changes to System object property values and input characteristics reset Reset internal states of System object

## Examples

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Transmit a convolutionally encoded 8 differential phase shift keying (DPSK) modulated bit stream through an additive white gaussian noise (AWGN) channel. Then, demodulate and decode the modulated bit stream using a Viterbi decoder.

Create the necessary System objects.

conEnc = comm.ConvolutionalEncoder;
modDPSK = comm.DPSKModulator('BitInput',true);
chan = comm.AWGNChannel('NoiseMethod','Signal to noise ratio (SNR)','SNR',10);
demodDPSK = comm.DPSKDemodulator('BitOutput',true);
vDec = comm.ViterbiDecoder('InputFormat','Hard');

Process the data by following these steps.

1. Generate random bits.

2. Convolutionally encode the data.

3. Apply DPSK modulation.

4. Pass the modulated signal through an AWGN channel.

5. Demodulate the noisy signal.

6. Decode the data using a Viterbi algorithm.

7. Collect error statistics.

for counter = 1:20
data = randi([0 1],30,1);
encodedData = conEnc(data);
modSignal = modDPSK(encodedData);
end

Display the number of errors.

errors(2)
ans = 3

Encode and decode a sequence of bits using a convolutional encoder and a Viterbi decoder with a defined puncture pattern. Verify that the input and output bits are identical.

Define a puncture pattern matrix, and the reshape it into vector form for use with the encoder and decoder System objects.

pPatternMat = [1 0 1;1 1 0];
pPatternVec = reshape(pPatternMat,6,1);

Create a convolutional encoder and a Viterbi decoder in which the puncture pattern is defined by pPatternVec.

conEnc = comm.ConvolutionalEncoder('PuncturePatternSource','Property','PuncturePattern',pPatternVec);
viDec = comm.ViterbiDecoder('InputFormat','Hard','PuncturePatternSource','Property', ...
'PuncturePattern',pPatternVec);

Create an error rate counter with the appropriate receive delay.

Encode a sequence of random bits, and then decode the encoded mesage.

dataIn = randi([0 1],600,1);
dataEncoded = conEnc(dataIn);
dataOut = viDec(dataEncoded);

Verify that no errors exist in the output data.

errStats = error(dataIn,dataOut);
errStats(2)
ans = 0

Concatenated convolutional codes offer high reliability and have gained in prominence and usage as turbo codes. The comm.TurboEncoder and comm.TurboDecoder System objects support rate 1/n convolutional codes only. This example shows the parallel concatenation of two rate 2/3 convolutional codes to achieve an effective rate 1/3 turbo code by using comm.ConvolutionalEncoder and comm.APPDecoder System objects.

System parameters

blkLength = 1024;          % Block length
EbNo = 0:5;                % Eb/No values to loop over
numIter = 3;               % Number of decoding iterations
maxNumBlks = 1e2;          % maximum number of blocks per Eb/No value

Convolutional Encoder/Decoder Parameters

trellis = poly2trellis([5 4],[23 35 0; 0 5 13]);
k = log2(trellis.numInputSymbols);      % number of input bits
n = log2(trellis.numOutputSymbols);     % number of output bits
intrIndices = randperm(blkLength/k)';   % Random interleaving
decAlg = 'True App';                    % Decoding algorithm
modOrder = 2;                           % PSK-modulation order

Initialize System Objects

Initialize Systems object for convolutional encoding, APP Decoding, BPSK modulation and demodulation, AGWN channel, and error rate computation. The demodulation output soft bits using a log-likelihood ratio method.

cEnc1 = comm.ConvolutionalEncoder('TrellisStructure',...
trellis,'TerminationMethod','Truncated');
cEnc2 = comm.ConvolutionalEncoder('TrellisStructure',...
trellis,'TerminationMethod','Truncated');
cAPPDec1 = comm.APPDecoder('TrellisStructure',trellis,...
'TerminationMethod','Truncated','Algorithm',decAlg);
cAPPDec2 = comm.APPDecoder('TrellisStructure',trellis,...
'TerminationMethod','Truncated','Algorithm',decAlg);

bpskMod = comm.BPSKModulator;
bpskDemod = comm.BPSKDemodulator('DecisionMethod','Log-likelihood ratio', ...
'VarianceSource','Input port');

awgnChan = comm.AWGNChannel('NoiseMethod','Variance', ...
'VarianceSource','Input port');

bitError = comm.ErrorRate; % BER measurement

Frame Processing Loop

Loop through a range of ${\mathit{E}}_{\mathrm{b}}/{\mathit{N}}_{0}$ values to generate results for BER performance. The helperTurboEnc and helperTurboDec helper functions perform the turbo encoding and decoding.

ber = zeros(length(EbNo),1);
bitsPerSymbol = log2(modOrder);
turboEncRate = k/(2*n);

for ebNoIdx = 1:length(EbNo)
% Calculate the noise variance from EbNo
EsNo = EbNo(ebNoIdx) + 10*log10(bitsPerSymbol);
SNRdB = EsNo + 10*log10(turboEncRate); % Account for code rate
noiseVar = 10^(-SNRdB/10);

for  numBlks = 1:maxNumBlks
% Generate binary data
data = randi([0 1],blkLength,1);

% Turbo encode the data
[encodedData,outIndices] = helperTurboEnc(data,cEnc1,cEnc2, ...
trellis,blkLength,intrIndices);

% Modulate the encoded data
modSignal = bpskMod(encodedData);

% Pass the modulated signal through an AWGN channel

% Demodulate the noisy signal using LLR to output soft bits

% Turbo decode the demodulated data
receivedBits = helperTurboDec(-demodSignal,cAPPDec1,cAPPDec2, ...
trellis,blkLength,intrIndices,outIndices,numIter);

% Calculate the error statistics
end

ber(ebNoIdx) = errorStats(1);
reset(bitError);
end

Display Results

While the practical wireless systems, such as LTE and CCSDS, specify base rate-1/n convolutional codes for turbo codes, the results show use of higher rate convolutional codes as turbo codes is viable.

figure;
semilogy(EbNo, ber, '*-');
grid on;
xlabel('E_b/N_0 (dB)');
ylabel('BER');
title('High Rate Convolutional Codes for Turbo Coding');
legend(['N = ' num2str(blkLength) ', ' num2str(numIter) ' iterations']);

Helper Functions

function [yEnc,outIndices] = helperTurboEnc(data,hCEnc1,hCEnc2,trellis,blkLength,intrIndices)
% Turbo encoding using two parallel convolutional encoders.
% No tail bits handling and assumes no output stream puncturing.

% Trellis parameters
k = log2(trellis.numInputSymbols);
n = log2(trellis.numOutputSymbols);
cLen = blkLength*n/k;

punctrVec = [0;0;0;0;0;0];      % assumes all streams are output
N = length(find(punctrVec==0));

% Encode random data bits
y1 = step(hCEnc1, data);
y2 = step(hCEnc2, reshape(intrlv(reshape(data, k, [])',intrIndices)', [], 1));
y1D = reshape(y1(1:cLen), n, []);
y2D = reshape(y2(1:cLen), n, []);
yDTemp = [y1D; y2D];
y = yDTemp(:);

% Generate output indices vector using puncturing vector
idx = 0 : 2*n : (blkLength - 1)*2*(n/k);
punctrVecIdx = find(punctrVec==0);
dIdx = repmat(idx, N, 1) + punctrVecIdx;
outIndices = dIdx(:);
yEnc = y(outIndices);
end

function yDec = helperTurboDec(yEnc,cAPPDec1,cAPPDec2,trellis,blkLength,intrIndices,inIndices,numIter)
% Turbo decoding using two a-posteriori probability (APP) decoders

% Trellis parameters
k = log2(trellis.numInputSymbols);
n = log2(trellis.numOutputSymbols);
rCodLen = 2*(n/k)*blkLength;
typeyEnc = class(yEnc);

% Re-order encoded bits according to outIndices
x = zeros(rCodLen, 1);
x(inIndices) = yEnc;

% Generate output of first encoder
yD = reshape(x(1:rCodLen), 2*n, []);
lc1D = yD(1:n, :);
Lc1_in = lc1D(:);

% Generate output of second encoder
lc2D   = yD(n+1:2*n, :);
Lc2_in = lc2D(:);

% Initialize unencoded data input
Lu1_in = zeros(blkLength, 1, typeyEnc);

% Turbo Decode
out1 = zeros(blkLength/k, k, typeyEnc);
for iterIdx = 1 : numIter
[Lu1_out, ~] = step(cAPPDec1, Lu1_in, Lc1_in);
tmp = Lu1_out(1:blkLength);
Lu2_in = reshape(tmp, k, [])';
[Lu2_out, ~] = step(cAPPDec2, ...
reshape(Lu2_in(intrIndices, :)', [], 1), Lc2_in);
out1(intrIndices, :) = reshape(Lu2_out(1:blkLength), k, [])';
Lu1_in = reshape(out1', [], 1);
end
% Calculate llr and decoded bits for the final iteration
llr = reshape(out1', [], 1) + Lu1_out(1:blkLength);
yDec = cast((llr>=0), typeyEnc);
end

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## References

[1] Clark, George C., and J. Bibb Cain. Error-Correction 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] 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.

[4] 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.

[5] 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.

[6] Moision, B. "A Truncation Depth Rule of Thumb for Convolutional Codes." In Information Theory and Applications Workshop (January 27 2008-February 1 2008, San Diego, California), 555-557. New York: IEEE, 2008.

## Extended Capabilities

### Objects

Introduced in R2012a