comm.ViterbiDecoder

Decode convolutionally encoded data using Viterbi algorithm

Description

The ViterbiDecoder object decodes input symbols to produce binary output symbols. This object can process several symbols at a time for faster performance. This object processes variable-size signals; however, variable-size signals cannot be applied for erasure inputs.

To decode input symbols and produce binary output symbols:

1. Define and set up your Viterbi decoder object. See Construction.

2. Call step to decode input symbols according to the properties of comm.ViterbiDecoder. The behavior of step is specific to each object in the toolbox.

Note

Starting in R2016b, instead of using the step method to perform the operation defined by the System object™, you can call the object with arguments, as if it were a function. For example, y = step(obj,x) and y = obj(x) perform equivalent operations.

Construction

H = comm.ViterbiDecoder creates a Viterbi decoder System object, H. This object uses the Viterbi algorithm to decode convolutionally encoded input data.

H = comm.ViterbiDecoder(Name,Value) creates a Viterbi decoder object, H, with each specified property set to the specified value. You can specify additional name-value pair arguments in any order as (Name1,Value1,...,NameN,ValueN).

H = comm.ViterbiDecoder(TRELLIS,Name,Value) creates a Viterbi decoder object, H. This object has the TrellisStructure property set to TRELLIS and the other specified properties set to the specified values.

Properties

TrellisStructure	Trellis structure of convolutional code Specify the trellis as a MATLAB® structure that contains the trellis description of the convolutional code. The default is the result of poly2trellis(7, [171 133]). Use the istrellis function to verify whether a structure is a valid trellis.
InputFormat	Input format Specify the format of the input to the decoder as Unquantized | Hard | Soft. The default is Unquantized. When you set this property to Unquantized, the input must be a real vector of double- or single-precision soft values that are unquantized. The object considers negative numbers to be 1s and positive numbers to be 0s. When you set this property to Hard, the input must be a vector of hard decision values, which are 0s or 1s. The data type of the inputs can be double-precision, single-precision, logical, 8-, 16-, and 32-bit signed integers. You can also use 8-, 16-, and 32-bit unsigned integers. When you set this property to Soft, the input requires a vector of quantized soft values represented as integers between 0 and . The data type of the inputs can be double-precision, single-precision, logical, 8-, 16-, and 32-bit signed integers. You can also use 8-, 16-, and 32-bit unsigned integers. Alternately, you can specify the data type as an unsigned and unscaled fixed point object (fi) with a word length equal to the word length that you specify in the SoftInputWordLength property. The object considers negative numbers to be 0s and positive numbers to be 1s.
SoftInputWordLength	Soft input word length Specify the number of bits to represent each quantized soft input value as a positive, integer scalar value. The default is 4 bits. This property applies when you set the InputFormat property to Soft.
InvalidQuantizedInputAction	Action when input values are out of range Specify the action the object takes when input values are out of range as Ignore | Error. The default is Ignore. Set this property to Error so that the object generates an error when the quantized input values are out of range. This property applies when you set the InputFormat property to Hard or Soft.
TracebackDepth	Traceback depth Specify the number of trellis branches to construct each traceback path as a numeric, integer scalar value. The default is 34. The traceback depth influences the decoding accuracy and delay. The number of zero symbols that precede the first decoded symbol in the output represent a decoding delay. When you set the TerminationMethod property to Continuous, the decoding delay consists of TracebackDepth×K zero bits for a rate K/N convolutional code. When you set the TerminationMethod property to Truncated or Terminated, there is no output delay. For more information, see Traceback and Decoding Delay and Traceback Depth Estimates.
TerminationMethod	Termination method of encoded frame Specify the termination method as Continuous | Truncated | Terminated. The default is Continuous. In Continuous mode, the object saves the internal state metric at the end of each frame for use with the next frame. The object treats each traceback path independently. In Truncated mode, the object treats each frame independently. The traceback path starts at the state with the best metric and always ends in the all-zeros state. In Terminated mode, the object treats each frame independently, and the traceback path always starts and ends in the all-zeros state.
ResetInputPort	Enable decoder reset input Set this property to true to enable an additional step method input. The default is false. When the reset input is a nonzero value, the object resets the internal states of the decoder to initial conditions. This property applies when you set the TerminationMethod property to Continuous.
DelayedResetAction	Reset on nonzero input via port Set this property to true to delay resetting the object output. The default is false. When you set this property to true, the reset of the internal states of the decoder occurs after the object computes the decoded data. When you set this property to false, the reset of the internal states of the decoder occurs before the object computes the decoded data. This property applies when you set the ResetInputPort property to true.
PuncturePatternSource	Source of puncture pattern Specify the source of the puncture pattern as None | Property. The default is None. When you set this property to None, the object assumes no puncturing. Set this property to Property to decode punctured codewords based on a puncture pattern vector specified via the PuncturePattern property.
PuncturePattern	Puncture pattern vector Specify puncture pattern to puncture the encoded data. The default is [1; 1; 0; 1; 0; 1]. The puncture pattern is a column vector of 1s and 0s. The 0s indicate the position to insert dummy bits. The puncture pattern must match the puncture pattern used by the encoder. This property applies when you set the PuncturePatternSource property to Property.
ErasuresInputPort	Enable erasures input Set this property to true to specify a vector of erasures as a step method input. The default is false. The erasures input must be a double-precision or logical, binary, column vector. This vector indicates which symbols of the input codewords to erase. Values of 1 indicate erased bits. The decoder does not update the branch metric for the erasures in the incoming data stream. The lengths of the step method erasure input and the step method data input must be the same. When you set this property to false, the object assumes no erasures.
OutputDataType	Data type of output Specify the data type of the output as Full precision | Smallest unsigned integer | double | single | int8 | uint8 | int16 | uint16 | int32 | uint32 | logical. The default is Full precision. When the input signal is an integer data type, you must have a Fixed-Point Designer™ user license to use this property in Smallest unsigned integer or Full precision mode.

 Fixed-Point Properties

StateMetricDataType

Data type of state metric Specify the state metric data type as Full precision | Custom. The default is Full precision. When you set this property to Full precision, the object sets the state metric fixed-point type to numerictype([],16). This property applies when you set the InputFormat property to Hard or Soft. When you set the InputFormat property to Hard, the step method data input must be a column vector. This vector comprises unsigned, fixed point numbers (fi objects) of word length 1 to enable fixed-point Viterbi decoding. Based on this input (either a 0 or a 1), the object calculates the internal branch metrics using an unsigned integer of word length L. In this case, L indicates the number of output bits as specified by the trellis structure. When you set the InputFormat property to Soft, the step method data input must be a column vector. This vector comprises unsigned, fixed point numbers (fi objects) of word length N. N indicates the number of soft-decision bits specified in the SoftInputWordLength property. The step method data inputs must be integers in the range 0 to 2N–1. The object calculates the internal branch metrics using an unsigned integer of word length L = (N + Nout – 1). In this case, Nout represents the number of output bits as specified by the trellis structure.

CustomStateMetricDataType

Fixed-point data type of state metric Specify the state metric fixed-point type as an unscaled, numerictype (Fixed-Point Designer) object with a signedness of Auto. The default is numerictype([],16). This property applies when you set the StateMetricDataType property to Custom.

Methods

step	Decode convolutionally encoded data using Viterbi algorithm

Common to All System Objects
release	Allow System object property value changes
reset	Reset internal states of System object

Examples

Encode and Decode 8-DPSK Modulated Data

Transmit a convolutionally encoded 8-DPSK modulated bit stream through an AWGN channel. Then, demodulate and decode using a Viterbi decoder.

Create the necessary System objects.

hConEnc = comm.ConvolutionalEncoder;
hMod = comm.DPSKModulator('BitInput',true);
hChan = comm.AWGNChannel('NoiseMethod', ...
    'Signal to noise ratio (SNR)',...
    'SNR',10);
hDemod = comm.DPSKDemodulator('BitOutput',true);
hDec = comm.ViterbiDecoder('InputFormat','Hard');
hError = comm.ErrorRate('ComputationDelay',3,'ReceiveDelay', 34);

Process the data using the following steps:

1. Generate random bits

2. Convolutionally encode the data

3. Apply DPSK modulation

4. Pass the modulated signal through AWGN

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 = step(hConEnc, data);
    modSignal = step(hMod, encodedData);
    receivedSignal = step(hChan, modSignal);
    demodSignal = step(hDemod, receivedSignal);
    receivedBits = step(hDec, demodSignal);
    errors = step(hError, data, receivedBits);
end

Display the number of errors.

errors(2)

ans = 3

Convolutional Encoding and Viterbi Decoding with a Puncture Pattern Matrix

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 reshape it into vector form for use with the Encoder and Decoder objects.

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

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

ENC = comm.ConvolutionalEncoder(...
    'PuncturePatternSource','Property', ...
    'PuncturePattern',pPatternVec);

DEC = comm.ViterbiDecoder('InputFormat','Hard', ...
    'PuncturePatternSource','Property',...
    'PuncturePattern',pPatternVec);

Create an error rate counter with the appropriate receive delay.

ERR = comm.ErrorRate('ReceiveDelay',DEC.TracebackDepth);

Encode and decode a sequence of random bits.

dataIn = randi([0 1],600,1);

dataEncoded = step(ENC,dataIn);

dataOut = step(DEC,dataEncoded);

Verify that there are no errors in the output data.

errStats = step(ERR,dataIn,dataOut);
errStats(2)

ans = 0

More About

Traceback and Decoding Delay

The traceback depth influences the decoding delay. The decoding delay is the number of zero symbols that precede the first decoded symbol in the output.

• For the continuous operating mode, the decoding delay is equal to the number of traceback depth symbols.

• For the truncated or terminated operating mode, the decoding delay is zero. In this case, the traceback depth must be less than or equal to the number of symbols in each input.

Traceback Depth Estimates

As a general estimate, a typical traceback depth value is approximately two to three times (ConstraintLength – 1) / (1 – coderate). The constraint length of the code, ConstraintLength, is equal to (log2(trellis.numStates) + 1). The coderate is equal to (K / N) × (length(PuncturePattern) / sum(PuncturePattern).

K is the number of input symbols, N is the number of output symbols, and PuncturePattern is the puncture pattern vector.

For example, applying this general estimate, results in these approximate traceback depths.

• A rate 1/2 code has a traceback depth of 5(ConstraintLength – 1).

• A rate 2/3 code has a traceback depth of 7.5(ConstraintLength – 1).

• A rate 3/4 code has a traceback depth of 10(ConstraintLength – 1).

• A rate 5/6 code has a traceback depth of 15(ConstraintLength – 1).

Algorithms

This object implements the algorithm, inputs, and outputs described on the Viterbi Decoder block reference page. The object properties correspond to the block parameters, except:

• The Decision type parameter corresponds to the InputFormat property.

• The Operation mode parameter corresponds to the TerminationMethod property.

References

[1] 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

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

See System Objects in MATLAB Code Generation (MATLAB Coder).