Abstract: The existing algorithms in the recognition of interleaver for Turbo codes have poor adaptability to low SNR and deteriorate sharply with the increase of interleaving length. To address these disadvantages, a new algorithm based on the maximum correction sequences correlation is proposed. First, the algorithm estimates the information sequences at each interleaved position by using the identified interleaved positions sequences. The possible interleaved positions are traversed and the cross-correlation operation is performed. When the traversing position is an interleaved mapping relation, the data sequences at that position have the greatest similarity with the estimated sequences, thus completing recognition. Then, by making full use of the sequences on the interleaved positions, the corrected data sequence is estimated and the corrected sequence is superimposed with the original sequence to complete symbol correction until all the interleaved relations are identified. The proposed algorithm utilizes the intercepted soft decision information directly and achieves symbol correction at the same time, overcoming two shortcomings of the previous algorithms. Simulation results show that when the SNR is -1 dB and interleaving length is 1024, the recognition rate can reach 100% with 1000 blocks of data frames. Compared with the previous algorithms, the performance of the proposed algorithm is improved by 2 to 3 dB, and the amount of data needed to complete a reliable identification is reduced to less than 1/4 of the original data.

Key words: Turbo codes, random interleaver, sequence estimation, cross correlation operation, correction