Blind Reconstruction of Binary Linear Block Codes Based on Association Rules Mining

In cognitive radio context, the coding parameters are unknown at the receiver. The design of an intelligent receiver is essentially to identify these parameters from the received data blindly. In this paper, we are interested in the blind identification of binary linear block codes from received noisy data. In order to recognize the code length, the concept of the normalized column weight vector is defined and cosine similarity is used to measure the difference between linear block codes and random codes. Then, the correct code length could be obtained by finding the local minimum of cosine similarity. The proposed code length recognition method needs no prior knowledge about the codes, which results in completely blind identification. To reconstruct the parity check matrix, the concept of association rules mining is introduced to the problem of blind identification of channel codes for the first time. Furthermore, five criteria are proposed to reduce the redundant rules mined by the association rules mining algorithm and to recognize the parity check vectors effectively. Simulations show that the proposed two methods have excellent performance even in a high error rate transport environment. The performance comparisons with existing methods validate the advantages of our two proposed methods.