A Fast Method for Blind Identification of Punctured Convolutional Codes

Abstract

The existing method for blind identification of a punctured convolutional code involves searching for dual words and the puncturing pattern exhaustively. As the length of the dual words and the code rate increase, the computational complexity of this method expands exponentially. To address this problem, a fast scheme for blind identification of punctured convolutional codes is proposed. First, a recursive algorithm for solving the parity check equation set is proposed. The dual word and generator polynomial bases of the punctured convolutional code are estimated by using the recursive algorithm. After this, by using the structural properties of the generator matrix of the blocked code, possible generator matrices of the punctured convolutional code are obtained. Finally, since a generator polynomial of the parent convolutional code can be recovered from any column of its polycyclic pseudocirculant matrix, the corresponding generator matrix of the parent code and the puncturing pattern are reconstructed simultaneously from an estimation of the generator matrix of the punctured code. The reconstructed generator matrix of the parent code with a minimal constraint length is determined to be the identification result. Simulation experiments show the effectiveness of the proposed method. As there is no need to search for the dual word and puncturing pattern exhaustively, the method can achieve fast identification of punctured convolutional codes. Additionally, the method is robust to bit errors in the received sequence.