Table Of Contents

MT Puncture Data Stream (G Dataflow)

Last Modified: January 9, 2017

Punctures a data stream and returns it at a higher rate.

Puncturing is the process of artificially increasing the rate of the input data by selectively deleting certain elements in the input data stream. The positions where the elements are deleted from the input data stream are defined by the puncture pattern matrix. Elements in the input data stream that correspond to values of zero in the puncture pattern matrix are omitted from the data stream output. The rate of the output data stream is given by the following equation:

R o u t = R i n × p q ( p q L )
where the input data stream arrives at a rate R in and the puncture pattern is a P× Q matrix containing L zeros.
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input data stream

The data stream with a rate to be increased by puncturing. This data stream is usually obtained from the output of a block or convolutional encoder.

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puncture pattern

A binary-valued matrix that determines which elements in the input data stream are omitted.

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Note  

The puncture pattern matrix is chronologically ordered by columns with respect to the input data stream. If the dimensions of the puncture pattern matrix are m×n, the m elements of the first column correspond to the first m elements of the input data stream, and so on.

Default: 1

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error in

Error conditions that occur before this node runs. The node responds to this input according to standard error behavior.

Default: no error

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reset?

A Boolean that determines whether this node uses the stored state information from a previous iteration while puncturing the input data stream in the current iteration.

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Note  

When the length L of the input data stream is not an integer multiple of the product P×Q of the P×Q puncture pattern matrix, the last L modulo (P×Q) bits are buffered inside the node.

TRUE Clears all previous state information and reinitializes the buffers.
FALSE Uses the stored state information from the previous iteration while puncturing the input data stream in the current iteration. The buffered elements are added to the beginning of the input data stream during the next iteration, prior to the puncturing operation.

Default: TRUE

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output data stream

The data stream returned by this node, which consists of all elements in the input data stream except those that were deleted during the puncturing operation.

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error out

Error information. The node produces this output according to standard error behavior.

Examples of Puncturing Matrices

Code Rates (Rout) Puncture Pattern Output Data Stream
1/2

X : 1

Y : 1

X1, Y1
2/3

X : 1 0

Y : 1 1

X1, Y1, Y2
3/4

X : 1 0 1

Y : 1 1 0

X1, Y1, Y2, X3
5/6

X : 1 0 1 0 1

Y : 1 1 0 1 0

X1, Y1, Y2, X3, Y4, X5
7/8

X : 1 0 1 0 1 0 1

Y : 1 1 0 1 0 1 0

X1, Y1, Y2, X3, Y4, X5, Y6, X7

Typically, the puncturing operation occurs in conjunction with the channel encoding operation. In such a case, the number of rows (P) in the puncture pattern matrix must equal the code word length (n) of the mother block/convolutional code. The preceding table shows examples of puncturing matrices for deriving higher rate codes from a mother rate ½ convolutional encoder. The input data stream is assumed to be of the form X1, Y1, X2, Y2, X3, Y3, and so on.

Preparing punctured data for decoding

If you perform convolutional encoding followed by puncturing the data stream returned on the transmit side, you must depuncture the data prior to performing convolutional decoding on the receiving side. In addition, you must call the instance of MT Convolutional Decoder that implements either soft-decision or unquantized Viterbi decoding.

Where This Node Can Run:

Desktop OS: Windows

FPGA: Not supported


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