*i.e.*, the detection of the presence of convolutional coding in a given bit stream.

**The method outlined below is not new**and a good reference it is this paper: M. Marazin, R. Gautier, G. Burel “

*Blind recovery of k/n rate convolutional encoders in a noisy environment*”

The basic idea is to arrange a given bit stream \(b_i\) in matrices \(R_k\) \begin{equation} B_k = \begin{pmatrix} b_0 & b_1 & b_2 & \cdots & b_{k-1}\\ b_k & b_{k+1} & b_{k+2} & \cdots & b_{2k-1}\\ \vdots & \vdots & \vdots & \ddots & \vdots \end{pmatrix} \end{equation} and then to determine the rank of \(R_k = \mathsf{rank}(B_k)\) as a function of \(k\). Note that for computing the rank \(B_k\) is treated as a matrix with values in \(\mathbb{F}_2\).

If the bit stream is completely random, all matrices \(B_k\) will likely have rank \(R_k=k\). However, the redundancy introduced by convolutional coding shows up as some matrices not having full rank,

*i.e.*, there are dependent rows in some \(B_k\) and therefore \( R_k < k \) for some \(k\).

Starting from a \(k=7,\;r=1/2\) mother code, rank deficiency plots are shown below for different puncturing patterns, together with the expected behavior.

Note that this method detects any redundancy (error coding) in a given bit stream and therefore can be generalized to other coding schemes.

Rank deficiencies for the k=7 r=1/2 mother code. |

Rank deficiencies for a k=7 r=2/3 code obtained by puncturing. |

Rank deficiencies for a k=7 r=3/4 code obtained by puncturing. |

Rank deficiencies for a k=7 r=4/5 code obtained by puncturing. |

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