Abstract:The generalized orthogonal matching pursuit gOMP algorithm has been used in the compressed sensing theory to solve the minimizing problem of the l0 norm. According to the compressed sensing model under the no noise condition, the compressed sensing model of the reconstructing error pattern E of the cyclic code is built in this paper. With the check matrix H as the measurement matrix, the syndrome S as the measurement signal, the error pattern E is reconstructed using the gOMP algorithm, while the value of the code word C is calculated by the receiving code R adding the error pattern E on modulo 2. Furthermore, we provide the form of the check matrix H, and propose two relevant theorems. The error correction ability of the gOMP algorithm is analyzed through some cyclic code examples, and the relationship between the selecting atom number s of the gOMP algorithm and the error correction bits is investigated. Finally, based on the bit error rate and the code word C reconstructing success rate, the decoding effects of the gOMP algorithm and the maximum likelihood algorithm are analyzed and compared. The simulation experiment results prove that the compressed sensing theory and gOMP algorithm are feasible and effective in decoding the cyclic code.
姜恩华. 基于gOMP算法的循环码译码研究[J]. 浙江大学学报(理学版), 2017, 44(5): 555-560,575.
JIANG Enhua. Research on the cyclic codes decoding based on the generalized orthogonal matching pursuit gOMP algorithm. Journal of ZheJIang University(Science Edition), 2017, 44(5): 555-560,575.
[1] JIAN W, SEOKBEOP K, BYONGHYO S. Generalized orthogonal matching pursuit[J].IEEE Transactions on Signal Processing,2012,60(12):6202-6216.
[2] WANG J, KWON S, LI P, et al. Recovery of sparse signals via generalized orthogonal matching pursuit:A new analysis[J].IEEE Transactions on Signal Processing,2016,64(4):1076-1089.
[3] 曹雪虹,张宗橙. 信息论与编码[M].第2版,北京:清华大学出版社,2009. CAO X H, ZHANG Z C. Information Theory and Coding[M]. 2nd ed. Beijing:Tsinghua University Press,2009.
[4] CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory,2006,52(2):489-509.
[5] DONOHO D L. Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[6] DASKALAKIS C, DIMAKIS A G, KARP R M, et al. Probabilistic analysis of linear programming decoding[J].IEEE Transactions on Information Theory,2008,54(8):3565-3578.
[7] DIMAKIS A G, VONTOBEL P O. LP decoding meets LP decoding:A connection between channel coding and compressed sensing[C]//FortyGSeventh AnnualAllertonConference. Illinois:IEEI,2009:8G15.
[8] 李耀辉,赵海豹,马春芽.循环码译码的Dixon结式方法[J]. 应用数学学报,2011,34(4):602-617. LI Y H, ZHAO H B, MA C Y. Decoding cyclic codes by using Dixon resultant method[J].Acta Mathematicae Applicatae Sinica,2011,34(4):602-617.
[9] AUGOT D, BARDET M, FAUGÈRE J C. On the decoding of binary cyclic codes with the Newton identities[J]. Journal of Symbolic Computation,2009,44(12):1608-1625.
[10] LOUSTAUNAU P, YORK E V. On the decoding of cyclic codes using Gr bner bases[J].Applicable Algebra in Engineering Communication & Computing,1997,8(6):469-483.
[11] ELAD M.Sparse and Redundant Representations:From Theory to Applications in Signal and Image Processing[M]. New York:Springer Publishing Company,2010:26-30.
[12] LIN L, LIU F, JIAO L. Compressed sensing by collaborative reconstruction on over complete dictionary[J].Signal Processing,2014,103:92-102.
[13] 董小亮,杨良龙,赵生妹,等.用信道编码构造压缩感知测量矩阵[J]. 信号处理,2013,29(7):809-815. DONG X L, YANG L L, ZHAO S M, et al. Channel coding for compressed sensing measurement matrix[J].Journal of Signal Processing,2013,29(7):809-815.
[14] 芦存博,肖嵩,权磊.基于二进制序列族的压缩感知测量矩阵构造[J]. 电子与信息学报,2016,38(7):1682-1688. LU C B, XIAO S, QUAN L. Construction of compressed sensing measurement matrix based on binary sequence family[J].Introduction of Journal of Electronics & Information Technology,2016,38(7):1682-1688.
[15] 张景雄,阳柯,郭建中.压缩感知的信息论解译[J]. 武汉大学学报:信息科学版,2014,39(11):1261-1268. ZHANG J X, YANG K, GUO J Z. Information-theoretic interpretations of compressive sampling[J]. Geomatics and Information Science of Wuhan University,2014,39(11):1261-1268.
[16] 张轶,达新宇,褚振勇.低密度奇偶校验码的压缩感知重构[J]. 吉林大学学报:工学版,2015,45(3):985-990. ZHANG Y, DA X Y, CHU Z Y. Compressed sensing reconstruction of LDPC code[J].Journal of Jilin University:Engineering and Technology Edition,2015,45(3):985-990.