基于离散PSO算法的RSC码识别

doi:10.3969/j.issn.0372-2112.2018.07.015

电子学报 ›› 2018, Vol. 46 ›› Issue (7): 1644-1651.DOI: 10.3969/j.issn.0372-2112.2018.07.015

基于离散PSO算法的RSC码识别

吴昭军¹, 张立民², 钟兆根¹, 刘杰²

1. 海军航空大学电子信息工程系, 山东烟台 264001;
2. 海军航空大学信息融合所, 山东烟台 264001

收稿日期:2017-02-22 修回日期:2017-08-01 出版日期:2018-07-25
- 作者简介:
- 吴昭军,男,1992年生于四川蓬溪.海军航空大学博士研究生,主要研究方向为信道编码盲识别;张立民,男,1966年出生于辽宁开原,博士,教授、博士生导师,主要研究方向为卫星信号处理及应用.E-mail:iamzlm@163.com
- 基金资助:
- 国家自然科学基金重大研究计划 (No.91538201）; 泰山学者工程专项经费 (No.Ts201511020）

Blind Recognition of RSC Based on Discrete PSO

WU Zhao-jun¹, ZHANG Li-min², ZHONG Zhao-gen¹, LIU Jie²

1. Department of Electronics and Information Engineering, Naval Aviation University, Yantai, Shandong 264001, China;
2. Department of Information Fusion, Naval Aviation University, Yantai, Shandong 264001, China

Received:2017-02-22 Revised:2017-08-01 Online:2018-07-25 Published:2018-07-25
- Supported by:
- Major Research Plan of National Natural Science Foundation of China (No.91538201); Taishan Scholars Special Funding for Construction Projects (No.Ts201511020)

摘要/Abstract

摘要： 针对目前RSC码盲识别算法容错性不好、计算量大的缺点，提出了基于离散PSO算法的RSC码识别方法.首先根据RSC码特殊的编码结构，构建出基于离散PSO算法的识别模型，并推导出了算法的适应度函数以及算法终止的判决门限；其次，按照构建出的识别模型，提出了详细的RSC码编码参数的识别步骤和流程框图，克服了目前RSC码识别算法的缺陷.仿真实验表明：该算法在寄存器个数较小时，具有较高的识别性能；同时在误码率高达0.05条件下，各种寄存器个数下的RSC码参数识别率均能达到90%以上，部分甚至达到100%，且算法的计算量仅仅与寄存器个数以及码元路数呈线性增加，与Walsh-Hadamard算法相比，计算量大大减少.

关键词: RSC码, 离散PSO算法, 适应度函数, 终止门限, 识别

Abstract: In order to address the defects of poor error tolerance and large amount of calculation in current RSC encoder recognition algorithms,a new algorithm based on discrete PSO is proposed.Firstly,because of the special structure of RSC codes,the recognized model based on the discrete PSO algorithm is built and the fitness of algorithm as well as terminal threshold are deduced.Then,the detailed steps and process to recognize the RSC codes are put forward according to the identification model to overcome the defects in current algorithms.The simulation results show that the algorithm has perfect performance when the number of memories is small.The correct ratio of recognition can reach 90% at different number of memories when the rate of bit error is 0.05,some of which can even reach 100%.Besides,the calculation of algorithm increases linearly with the number of memories and branches of codes,which is reduced greatly compared with the Walsh-Hadamard.

Key words: recursive systematice convolutional codes, the discrete particle swarm optimization, the function of fitness, terminal threshold, recognition

中图分类号:

TN911.7

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 Zhao-jun, ZHANG Li-min, ZHONG Zhao-gen, et al. Blind Recognition of RSC Based on Discrete PSO[J]. Acta Electronica Sinica, 2018, 46(7): 1644-1651.

参考文献

[1] Jaeyong S,Kyungwhoon C,Kyeongcheol Y.Low-complexity decoding of block Turbo codes based on the chase algorithm[J].IEEE Communication Letters,2017,21(4):706-709.
[2] Thomas J,Balazs M.Turbo code design for short blocks[A].8^th Advanced Satellite Multimedia Systems Conference and the 14thSignal Processing for Space Communications Workshop[C].Palma de Mallorca,2016.1-6.
[3] 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.
[4] 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)
[5] 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 inverage conformity of parity-check equation[J].Acta Electronica Sinica,2016,44(5):1213-1217.(in Chinese)
[6] 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)
[7] BARBIER J.Reconstruction of Turbo-code encoders[J].Defense & Security,2005,5819:463-473.
[8] 解辉,王峰华,黄知涛,张锡祥.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)
[9] 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)
[10] 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)
[11] 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.
[12] 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.
[13] 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(5):302-305.(in Chinese)
[14] 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)
[15] 解辉,王峰华,黄知涛.基于最大似然检测的(n,1,m)卷积码盲识别方法[J].电子与信息学报,2013,35(7):1671-1676. XIE H,WANG F H,HUANG Z T.Blind recognition of(n,1,m) convolutional code based on maximum likelihood detection[J].Journal of Electronics & Information Technology,2013,35(7):1671-1676.(in Chinese)
[16] 张旻,陆凯,李歆昊,吕全通.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)
[17] ALI N,OMID A,SAMAD F.Blind recognition algorithm of Turbo codes for communication intelligence systems[J].International Journal of Computer Science Issues,2011,8(6):68-72.
[18] MARAZIN M,GAUTIER R,BUREL G.Blind recovery of k/n rate convolutional encoders in a noisy environment[J].EURASIP Journal on Wireless Communications and Networking,2011,2011(1):168-177.
[19] 纪震,廖惠连,吴青华.粒子群算法及应用[M].北京:北京科学出版社,2009:17-50. JI Z,LIAO H L,WU Q H.The Particle Swarm Optimization Algorithm and Its Application[M].Beijing:Science Press.2009.17-50.(in Chinese)
[20] 谢承旺,邹秀芬,夏学文,王志杰.一种多策略融合的多目标粒子群优化算法[J].电子学报,2015,43(8):1538-1544. XIE C W,ZOU X F,XIA X W,et al.A muti-objective particle swarm optimization algorithm integrating multiply strategies[J].Acta Electronica Sinica,2015,43(8):1538-1544.(in Chinese)

基于离散PSO算法的RSC码识别

Blind Recognition of RSC Based on Discrete PSO

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	李钦, 刘伟, 牛朝阳, 宝音图, 惠周勃. 低信噪比下基于分裂EfficientNet网络的雷达信号调制方式识别[J]. 电子学报, 2023, 51(3): 675-686.
[2]	杨威, 李玮杰, 刘永祥, 黎湘. 基于测地线流式核的雷达目标高分辨距离像鲁棒识别方法[J]. 电子学报, 2023, 51(3): 527-536.
[3]	张轶, 翟盛华, 陶海红. 雨衰时间序列的混沌识别与预测[J]. 电子学报, 2023, 51(2): 365-371.
[4]	石光明, 高大化, 杨旻曦, 谢雪梅, 董明皓, 李雷达, 于凯. 信号的语义刻画与度量[J]. 电子学报, 2022, 50(9): 2068-2078.
[5]	张亚洲, 俞洋, 朱少林, 陈锐, 戎璐, 梁辉. 一种量子概率启发的对话讽刺识别网络模型[J]. 电子学报, 2022, 50(8): 1885-1893.
[6]	邵志文, 周勇, 谭鑫, 马利庄, 刘兵, 姚睿. 基于深度学习的表情动作单元识别综述[J]. 电子学报, 2022, 50(8): 2003-2017.
[7]	邓海刚, 王传旭, 李成伟, 林晓萌. 深度学习框架下群组行为识别算法综述[J]. 电子学报, 2022, 50(8): 2018-2036.
[8]	陈嘉兴, 程杰, 董云玲, 刘志华. 基于弯曲声线和测距修正的水下节点定位算法[J]. 电子学报, 2022, 50(7): 1567-1572.
[9]	赵俊男, 佘青山, 孟明, 陈云. 基于多流空间注意力图卷积SRU网络的骨架动作识别[J]. 电子学报, 2022, 50(7): 1579-1585.
[10]	利强, 张伟, 金秋园, 姚欣. 基于知识原型网络的小样本多功能雷达工作模式识别[J]. 电子学报, 2022, 50(6): 1344-1350.
[11]	张伟, 王沙飞, 林静然, 利强, 邵怀宗. 基于孪生网络的电磁目标跨模式识别算法[J]. 电子学报, 2022, 50(6): 1281-1290.
[12]	杨伟超, 杜宇, 文伟, 侯舒维, 徐常志, 张建华. 基于多重分形谱智能分析的卫星信号调制识别研究[J]. 电子学报, 2022, 50(6): 1336-1343.
[13]	杨柳, 利强, 邵怀宗. Open-MUSIC：基于度量学习与特征子空间投影的电磁目标开集识别算法[J]. 电子学报, 2022, 50(6): 1310-1318.
[14]	刘倩, 张昊, 宋莹炯, 王刚. 小样本条件下基于矩阵乘法和秩分析的LDPC参数估计方法[J]. 电子学报, 2022, 50(5): 1075-1082.
[15]	张继龙, 李业振, 刘勇, 王栋. 基于透镜成像原理的微波阵列快速成像技术[J]. 电子学报, 2022, 50(5): 1107-1116.