Channel Coding Type Identification With Random Interweaving in a Noisy Environment

Channel coding analysis is the reverse analysis and identification of coding parameters. It plays an important role in applications like intelligent communication and signal interception. In order to solve the problem that random interleaving code-words are difficult to recognize under noisy conditions，this paper presents an algorithm based on searching for small weight vectors for interweaving and coding type identification. First, selecting some code-words randomly and converting them to a dual matrix. Next, using the small weight vectors search algorithm to obtain some valid check vectors with the eliminating operation. Then, according to the principle of LDPC decoding, performing similar decoding operation for original code-words and iterating with the previous steps to obtain most of check vectors. Finally, the existence of interweaving and coding type can be recognized through mean span and dispersion of check vectors. The proposed method overcomes the limitations of the existing methods which can not be applied to random interleaving code-words. Simulations are conducted based on convolutional codes of 1/2 code rate and (15,11) Hamming codes. The results show that the interleaving code-words can still be recognized effectively under the bit error rate of 0.006.