An Improved Blind Recognition Method of the Convolutional Interleaver Parameters in a Noisy Channel

In a digital communication system, forward error correction (FEC) codes and interleavers are implemented to code the data so as to improve the error performance, which is hindered by random and burst errors. In the context of noncooperative communication, interleaver parameter recognition, which is the prerequisite for frame synchronization, channel coding recognition subsequently, is of vital significance. Methods of blindly recognizing convolutional interleaver parameters have been proposed in published literature, but the accuracy of recognition decreases significantly when bit error rate (BER) is high. To improve the performance of the algorithm, the effect of error bits on Gauss-Jordan elimination through pivoting (GJTEP) algorithm is analyzed in this paper. The following conclusion is drawn: error bits on the principal diagonal of data storage matrix will exert a great impact on the recognition accuracy. Based on the conclusion, an improved blind recognition method with denoising, the core principle of which is reducing error bits on the principal diagonal of data storage matrix, is proposed in this paper. The simulation experiment results demonstrate that the performance on error resilience is markedly improved.

[1]  Kwok Hung Li,et al.  Blind Detection of Interleaver Parameters for Non-Binary Coded Data Streams , 2009, 2009 IEEE International Conference on Communications.

[2]  A. S. Madhukumar,et al.  Parameter estimation of block and helical scan interleavers in the presence of bit errors , 2017, Digit. Signal Process..

[3]  John L. Ramsey.  Realization of optimum interleavers , 1970, IEEE Trans. Inf. Theory.

[5]  Dongweon Yoon,et al.  Enhanced Blind Interleaver Parameters Estimation Algorithm for Noisy Environment , 2018, IEEE Access.

[6]  Lu Gan,et al.  Blind Estimation of Convolutional Interleaver Parameters , 2012 .

[7]  A. S. Madhukumar,et al.  Parameter Estimation of Convolutional and Helical Interleavers in a Noisy Environment , 2017, IEEE Access.

[8]  Lu Gan,et al.  A low complexity algorithm of blind estimation of convolutional interleaver parameters , 2012, Science China Information Sciences.

[9]  A. S. Madhukumar,et al.  Blind Reconstruction of Reed-Solomon Encoder and Interleavers Over Noisy Environment , 2018, IEEE Transactions on Broadcasting.

[10]  Sébastien Houcke,et al.  Blind detection of interleaver parameters , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[11]  Audrey Tixier.  Blind identification of an unknown interleaved convolutional code , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).

[12]  Kwok Hung Li,et al.  Blind identification of convolutional interleaver parameters , 2009, 2009 7th International Conference on Information, Communications and Signal Processing (ICICS).

[13]  Jr. G. Forney,et al.  Burst-Correcting Codes for the Classic Bursty Channel , 1971 .

[14]  Roland Gautier,et al.  Blind recovery of k/n rate convolutional encoders in a noisy environment , 2011, EURASIP J. Wirel. Commun. Netw..

[15]  Dongweon Yoon,et al.  Blind detection of interleaver parameter using error-less symbols , 2017, 2017 International Conference on Information and Communication Technology Convergence (ICTC).

[16]  A. S. Madhukumar,et al.  Classification of Error Correcting Codes and Estimation of Interleaver Parameters in a Noisy Transmission Environment , 2017, IEEE Transactions on Broadcasting.