电子电气工程与控制

一种基于遗传算法的RSC码盲识别方法

  • 张立民 ,
  • 吴昭军 ,
  • 钟兆根
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  • 1. 海军航空大学 信息融合所, 烟台 264001;
    2. 海军航空大学 电子信息工程系, 烟台 264001

收稿日期: 2017-03-15

  修回日期: 2017-07-07

  网络出版日期: 2017-07-07

Blind identification of RSC code based on genetic algorithm

  • ZHANG Limin ,
  • WU Zhaojun ,
  • ZHONG Zhaogen
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  • 1. Institute of Information Fusion, Naval Aeronautical University, Yantai 264001, China;
    2. Department of Electronic and Information Engineering, Naval Aeronautical University, Yantai 264001, China

Received date: 2017-03-15

  Revised date: 2017-07-07

  Online published: 2017-07-07

摘要

针对目前递归系统卷积(RSC)码盲识别算法容错性差、计算量大的问题,提出了基于遗传算法的RSC多项式参数盲识别算法。首先根据RSC码特殊的编码结构,构建了基于遗传算法的识别模型,将结果向量的码重作为适应度函数,然后推导出了不同误码率条件下平均码重的理论值,实现了算法中最优门限的获得。该算法容错性能较好,并且最大计算量只与初始种群的规模、遗传代数的上限以及输出路数成正比。最后仿真验证表明,理论推导的码重分布情况能够与仿真结果较好地吻合,并且在误码率高达0.06的情况下,各种寄存器个数下的RSC码参数识别率接近于0.9。

本文引用格式

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 -321246 . DOI: 10.7527/S1000-6893.2017.321246

Abstract

To address the problems of poor performance and heavy computation in blind identification of Recursive Systematic Convolutional (RSC) code, a new algorithm for blind identification of RSC polynomial parameters is proposed based on the genetic algorithm. Considering the special structure of RSC code, the identification model is constructed based on the genetic algorithm. The weight of the result vector is used as fitness function, and the theoretical value of the average code weight is derived at different Bit Error Rates, as the results. The optimal threshold is then obtained. The performance of the proposed algorithm is good, and the maximum amount of calculation is only proportional to the initial population size, genetic generations, and paths of outputs. The simulation results show that the theoretical derivation of the code weight is in good agreement with the simulation results, and the recognition rate of the RSC code is close to 0.9 at different number of registers when the Bit Error Rate is up to 0.06.

参考文献

[1] MUKHTAR H, AL-DWEIK A, SHAMI A. Turbo product codes:Applications, challenges, and future directions[J]. IEEE Communications Surveys & Tutorials, 2016, 18(4):3052-3069.
[2] LI H, GAO Z, ZHAO M, et al. Partial iterative decode of turbo codes for on-board processing satellite platform[J]. IEEE Journals & Magazines, 2015, 12(11):1-8.
[3] 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).
[4] 刘俊, 李静, 彭华. 基于校验方程平均符合度的Turbo码交织器估计[J]. 电子学报, 2016, 44(5):1213-1217. 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):1213-1217(in Chinese).
[5] 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).
[6] BARBIER J. Reconstruction of turbo-code encoders[J]. The International Society for Optical Engineering, 2005, 5819:463-473.
[7] 解辉, 王峰华, 黄知涛, 等. 基于改进欧几里得算法的卷积码快速盲识别算法[J]. 国防科技大学报, 2012, 34(6):159-162. XIE H, WANG F H, HUANG Z T, et al. A fast method for blind recognition of convolutional codes based on improved Euclidean algorithm[J]. Journal of National University of Defense Technology, 2012, 34(6):159-162(in Chinese).
[8] 邹艳, 陆佩忠. 关键方程的新推广[J]. 计算机学报, 2006, 29(5):711-718. ZOU Y, LU P Z. A new generalization of key equation[J]. Chinese Journal of Computers, 2006, 29(5):711-718(in Chinese).
[9] 刘健, 王晓军, 周希元. 基于Walsh-Hadamard变换的卷积码盲识别[J]. 电子与信息学报, 2010, 32(4):884-888. LIU J, WANG X J, ZHOU X Y. Blind recognition of convolutional coding based on walsh hadamard transform[J]. Journal of Electronics & Information Technology, 2010, 32(4):884-888(in Chinese).
[10] DEBESSU Y G, WU H C, JIANG H. Novel blind encoder parameter estimation for turbo codes[J]. IEEE Communications Letters, 2012, 16(16):1917-1920.
[11] 于沛东, 李静, 彭华. 一种利用软判决的信道编码识别新算法[J]. 电子学报, 2013, 41(2):302-305. YU P D, LI J, PENG H. A novel algorithm for channel coding recognition using soft decision[J]. Acta Electronica Sinica, 2013, 41(2):302-305(in Chinese).
[12] 武恒洲, 罗霄斌, 刘杰. Turbo码盲识别技术研究[J].无线电工程, 2015, 45(5):24-27. WU H Z, LUO X B, LIU J. Research on blind recognition of turbo codes[J]. Journal of Radio Engineering, 2015, 45(5):24-27(in Chinese).
[13] 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).
[14] QIU M, ZHONG M, LI J, et al. Phase-change memory optimization for green cloud with genetic algrithm[J]. IEEE Transactions on Computers, 2015, 64(12):3528-3540.
[15] 杨振强, 王常虹, 庄显义. 自适应复制、交叉和突变的遗传算法[J]. 电子与信息学报, 2000, 22(1):112-117. YANG Z Q, WANG C H, ZHUANG X Y. The adaptive genetic algorithms of copy, intersect and mutation[J]. Journal of Electronics & Information Technology, 2000, 22(1):112-117(in Chinese).
[16] 林晓娴, 王维欢. SIMD-BF模型上的并行FWHT算法研究[J]. 计算机时代, 2011(1):30-32. LIN X X, WANG W H. A study of parallel FWHT algorithm based on SIMD-BF model[J]. Computer Era, 2011(1):30-32(in Chinese).

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