Blind recognition of interleaver for Turbo codes based on maximum sequence correlation

WU Zhaojun; ZHANG Limin; ZHONG Zhaogen

doi:10.7527/S1000-6893.2019.22764

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2019 , Vol. 40 >Issue 6: 322764 - 322764

DOI: https://doi.org/10.7527/S1000-6893.2019.22764

Electronics and Electrical Engineering and Control

Blind recognition of interleaver for Turbo codes based on maximum sequence correlation

WU Zhaojun ,
ZHANG Limin ,
ZHONG Zhaogen

Expand

1. Institute of Information Fusion, Naval Aviation University, Yantai 264001, China;
2. School of Basis of Aviation, Naval Aviation University, Yantai 264001, China

Received date: 2018-11-01

Revised date: 2019-01-10

Online published: 2019-02-26

Supported by

National Natural Science Foundation of China (91538201); Special Fund of Taishan Scholars (Ts201511020)

Fold

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

Cite this article

WU Zhaojun , ZHANG Limin , ZHONG Zhaogen . Blind recognition of interleaver for Turbo codes based on maximum sequence correlation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019 , 40(6) : 322764 -322764 . DOI: 10.7527/S1000-6893.2019.22764

References

[1] MUKHTAR H, AL-DWEIK A, SHAMI. Turbo product codes:Applications, challenges, and future directions[J]. IEEE Communications Surveys & Tutorials, 2016, 18(4):3052-3069.
[2] 谢辉, 黄知涛, 王峰华. 信道编码盲识别技术研究进展[J]. 电子学报, 2013, 41(6):1166-1176. XIE H, HUANG Z T, WANG F H. Research progress of blind recognition of channel coding[J]. Acta Electronica Sinica, 2013, 41(6):1166-1176(in Chinese).
[3] 张立民, 吴昭军, 钟兆根. 基于校验方程符合度下的Turbo码编码器盲识别[J]. 电子与信息学报, 2017, 39(9):2155-2161. ZHANG L M, WU Z J, ZHONG Z G. Blind recognition of turbo code encoder based on conformity of parity-check equation[J]. Journal of Electronics & Information Technology, 2017, 39(9):2155-2161(in Chinese).
[4] 张立民, 吴昭军, 钟兆根. 一种基于遗传算法的RSC码盲识别方法[J]. 航空学报, 2017, 38(11):321246. ZHANG L M, WU Z J, ZHONG Z G. Blind identification of RSC code based on genetic algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(11):321246(in Chinese).
[5] 吴昭军, 张立民, 钟兆根, 等. 基于离散PSO算法的RSC码识别[J]. 电子学报, 2018, 46(7):1644-1651. WU Z J, ZHANG L M, ZHONG Z G, et al. Blind recognition of RSC based on discrete PSO[J]. Acta Electronica Sinica, 2018, 46(7):1644-1651(in Chinese).
[6] YU P D, LI J, PENG H. A least square method for parameter estimation of RSC sub-codes of turbo codes[J]. IEEE Communications Letters, 2014, 18(4):644-647.
[7] NASERI A, AZMOON O, FAZELI S. Blind recognition algorithm of Turbo codes for communication intelligence systems[J]. International Journal of Computer Science Issues, 2011, 8(6):68-72.
[8] 张旻, 陆凯, 李歆昊, 等. 归零Turbo码的盲识别方法[J]. 系统工程与电子技术, 2016, 38(6):1424-1427. ZHANG M, LU K, LI X H, et al. Blind recognition method for the turbo codes on trellis termination[J]. Journal of Systems Engineering and Electronics, 2016, 38(6):1424-1427(in Chinese).
[9] 吴昭军, 张立民, 钟兆根. 高误码率下归零Turbo码参数识别[J]. 兵工学报, 2018, 39(4):731-742. WU Z J, ZHNG L M, ZHONG Z G. Blind identification of turbo codes on trellis termination at high bit error rate[J]. Acta Armamen-Tarll, 2018, 39(4):731-742(in Chinese).
[10] 甘露, 刘宗辉, 廖红舒, 等. 卷积交织参数的盲估计[J]. 电子学报, 2011, 39(9):2173-2177. GAN L, LIU Z H, LIAO H S, et al. Blind estimation of the parameters of convolutional interleaver[J]. Acta Electronica Sinica, 2011, 39(9):2173-2177(in Chinese).
[11] 于沛东, 彭华, 巩克现, 等. 利用帧同步码的卷积交织器快速盲识别方法[J]. 电子学报, 2018, 46(6):1530-1536. YU P D, PENG H, GONG K X, et al. Fast blind recognition of convolutional interleavers based on existence of frame sync codes[J]. Acta Electronica Sinica, 2018, 46(6):1530-1536(in Chinese).
[12] 陈泽亮, 巩克现, 彭华, 等. 基于软信息的分组交织和卷积码联合识别[J]. 电子学报, 2018, 46(6):1454-1460. CHEN Z L, GONG K X, PENG H, et al. Joint blind recognition of pachet interleaver and convolution code based on soft information[J]. Acta Electronica Sinica, 2018, 46(6):1454-1460(in Chinese).
[13] MAXIME C, NICOLAS S. Reconstruction of a Turbo-code interleaver from noisy observation[C]//International Symposium on Information Theory 2010. Piscataway, NJ:IEEE Press, 2010:2003-2007.
[14] MATHIEU C, MAHHTIEU F, JEAN P T. Methods for the reconstruction of parallel Turbo codes[C]//International Symposium on Information Theory 2010. Piscataway, NJ:IEEE Press, 2010:2008-2012.
[15] 任亚博, 张健, 刘以农. 高误码率下Turbo码交织器的恢复方法[J]. 电子与信息学报, 2015, 37(8):1927-1930 REN Y B, ZHANG J, LIU Y N. Reconstruction of turbo-code interleaver at high bit error rate[J]. Journal of Electronics & Information Technology, 2015, 37(8):1927-1930(in Chinese).
[16] 刘骏, 李静, 于沛东. 一种Turbo码随机交织器的迭代估计方法[J]. 通信学报, 2015, 36(6):201-206. LIU J, LI J, YU P D. Iterative estimation method for random interleaver of turbo codes[J]. Journal on Communications, 2015, 36(6):201-206(in Chinese).
[17] 刘俊, 李静, 彭华. 基于校验矩阵平均符合度的Turbo码交织器估计[J]. 电子学报, 2016, 44(5):1214-1218. LIU J, LI J, PENG H. Estimation of Turbo-code interleaver based on average conformity of parity-check equation[J]. Acta Electronica Sinica, 2016, 44(5):1213-1218(in Chinese).
[18] 吴昭军, 张立民, 钟兆根. 低信噪比下随机交织器识别[J]. 电讯技术, 2018, 58(1):52-58. WU Z J, ZHANG L M, ZHONG Z G. Blind recognition of random interleaver at low SNR[J]. Telecommunication Engineering, 2018, 58(1):52-58(in Chinese).
[19] 陈泽亮, 李静, 彭华, 等. 利用Gibbs采样进行优化的Turbo码交织器识别[J]. 电子学报, 2018, 46(1):15-23. CHEN Z L, LI J, PENG H, et al. An optimization method using Gibbs sampler for turbo code interleaver identification[J]. Acta Electronica Sinica, 2018, 46(1):15-23(in Chinese).
[20] YU P D, LI J, PENG H. Gibbs sampling based parameter estimation for RSC sub-codes of Turbo codes[C]//Sixth International Conference on Wireless Communications and Signal Processing (WCSP). Piscataway, NJ:IEEE Press, 2014:1-5.
[21] SHEN B, PATAPOUTIAN A, MCEWEN P A, et al. Punctured recursive convolutional encoders and their applications in turbo codes[J]. IEEE Transactions on Information Theory, 2001, 47(6):2300-2320.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References