Viterbi algorithm matlab program

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:. 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. For some commonly used puncture patterns for specific rates and polynomials, see the last three references. The examples highlight the fixed-point modeling attributes of the Viterbi decoder, using a familiar layout. The two simulations have a similar structure and have most parameters in common.

For every 2 bits, the encoder adds another 2 redundant bits. For the hard-decision case, the BPSK demodulator produces hard decisions, at the receiver, which are passed onto the decoder.

For the soft-decision case, the BPSK demodulator produces soft decisions, at the receiver, using the log-likelihood ratio. These soft outputs are 3-bit 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 bit errors or 1e6 bits, whichever comes first. 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 fixed-point Viterbi decoding, the block input must be of type ufix1 unsigned integer of word length 1 for hard decisions. For soft decisions, the block input must be of type ufixN unsigned integer of word length N , where N is the number of soft-decision bits, to enable fixed-point decoding.

The block inputs must be integers in the range 0 to 2 N The State metric word length is specified by the user and usually must be greater than the branch metric word length already calculated. 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. 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 double-precision and fixed-point BER results are the same. They are the same because the fixed-point 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 hard-decision versus soft-decision decoding.

Make sure that the model is on path. Set the Number of errors to , and the Number of bits to 1e6. Notice that, as expected, 3-bit soft-decision decoding is better than hard-decision decoding, roughly to the tune of 1.

The difference in the expected results could be attributed to the imperfect quantization of the soft outputs from the demodulator. 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. Method for transitioning between successive input frames: Continuous , Terminated , and Truncated.

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. Implementation of Viterbi algorithm Usage.

Implementation of Viterbi algorithm Matlab implementation of Viterbi algorithm with early decision for binary encoding. The code containes several files: trellis. Usage Choose three window lenghts and the confidence interval size in terms of minimum number of wrong bits in the file run.

About Matlab implementation of Viterbi algorithm for binary encoding Topics encoding viterbi-algorithm information-theory binary-encoding viterbi-decoder. Releases No releases published. Packages 0 No packages published.

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