Convolutional Coding

Convolutional and turbo encoding and decoding, APP, posteriori, Viterbi, and binary, octal, or trellis conversion

Communications Toolbox™ includes tools using either MATLAB® or Simulink® for forward error correction encoding and decoding of signal data.

Functions

convencConvolutionally encode binary message
distspecCompute distance spectrum of convolutional code
iscatastrophicTrue for trellis corresponding to catastrophic convolutional code
istrellisTrue for valid trellis structure
oct2decConvert octal to decimal numbers
poly2trellisConvert convolutional code polynomials to trellis description
vitdecConvolutionally decode binary data by using Viterbi algorithm

Objects

comm.APPDecoderDecode convolutional code by using APP method
comm.ConvolutionalEncoderConvolutionally encode binary data
comm.gpu.ConvolutionalEncoderConvolutionally encode binary data with GPU
comm.TurboDecoderDecode input signal using parallel concatenated decoding scheme
comm.gpu.TurboDecoderDecode input signal using parallel concatenation decoding with GPU
comm.TurboEncoderEncode input signal using parallel concatenated encoding scheme
comm.ViterbiDecoderDecode convolutionally encoded data using Viterbi algorithm
comm.gpu.ViterbiDecoderDecode convolutionally encoded data using Viterbi algorithm with GPU

Blocks

APP DecoderDecode convolutional code using a posteriori probability (APP) method
Convolutional EncoderCreate convolutional code from binary data
Turbo DecoderDecode input signal using parallel concatenated decoding scheme
Turbo EncoderEncode binary data using parallel concatenated encoding scheme
Viterbi DecoderDecode convolutionally encoded data using Viterbi algorithm

Topics

Iterative Decoding of a Serially Concatenated Convolutional Code

This model shows how to use an iterative process to decode a serially concatenated convolutional code (SCCC).

Punctured Convolutional Encoding

This model shows how to use the Convolutional Encoder and Viterbi Decoder blocks to simulate a punctured coding system.

Creation, Validation, and Testing of User Defined Trellis Structure

Use MATLAB to create and validate a user defined trellis structure, then use a unit test bench built in Simulink to test the implementation.

Estimate Turbo Code BER Performance in AWGN

Simulate an end-to-end communication link employing 16-QAM using turbo codes in an AWGN channel.

Featured Examples

Parallel Concatenated Convolutional Coding: Turbo Codes

Parallel Concatenated Convolutional Coding: Turbo Codes

The basic structure of turbo codes, both at the transmitter and receiver ends, and characterizes their performance over a noisy channel using components from the Communications Toolbox™. We chose the Long Term Evolution (LTE) specifications [ 4 ] for the constituent component parameters.

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Tail-Biting Convolutional Coding

Tail-Biting Convolutional Coding

How to use the Convolutional Encoder and Viterbi Decoder blocks to simulate a tail-biting convolutional code. Terminating the trellis of a convolutional code is a key parameter in the code's performance for packet-based communications. Tail-biting convolutional coding is a technique of trellis termination which avoids the rate loss incurred by zero-tail termination at the expense of a more complex decoder [ 1 ].

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Rate 2/3 Convolutional Code in AWGN

Rate 2/3 Convolutional Code in AWGN

Generate a bit error rate versus Eb/No curve for a link that uses 16-QAM modulation and a rate 2/3 convolutional code in AWGN.

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Punctured Convolutional Coding

Punctured Convolutional Coding

Use the convolutional encoder and Viterbi decoder System objects to simulate a punctured coding system. The complexity of a Viterbi decoder increases rapidly with the code rate. Puncturing is a technique that allows the encoding and decoding of higher rate codes using standard rate 1/2 encoders and decoders.

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Estimate BER for Hard and Soft Decision Viterbi Decoding

Estimate BER for Hard and Soft Decision Viterbi Decoding

Estimate bit error rate (BER) performance for hard-decision and soft-decision Viterbi decoders in AWGN. Compare the performance to that of an uncoded 64-QAM link.

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Using GPUs To Accelerate Turbo Coding Bit Error Rate Simulations

Using GPUs To Accelerate Turbo Coding Bit Error Rate Simulations

Use GPUs to dramatically accelerate bit error rate simulations. Turbo Codes form the backbone of many modern communication systems. Because of the intense amount of computation involved in a Turbo Decoder and the massive amount of trials required for a valid bit error rate simulation, the Turbo Decoder is an ideal candidate for GPU acceleration. See the "Parallel Concatenated Convolutional Coding: Turbo Codes" example, which explains the data processing chain, for more information on Turbo Codes.

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