Blind Estimation PN Sequence in Soft Spread Spectrum Signal of Improved K-means Algorithm

ZHANG Tianqi; YANG Qiang; SONG Yulong; XIONG Mei

doi:10.11999/JEIT170306

Volume 40 Issue 1

Jan. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(1): 226-234

ZHANG Tianqi, YANG Qiang, SONG Yulong, XIONG Mei. Blind Estimation PN Sequence in Soft Spread Spectrum Signal of Improved K-means Algorithm[J]. Journal of Electronics & Information Technology, 2018, 40(1): 226-234. doi: 10.11999/JEIT170306

Citation:

ZHANG Tianqi, YANG Qiang, SONG Yulong, XIONG Mei. Blind Estimation PN Sequence in Soft Spread Spectrum Signal of Improved K-means Algorithm[J]. Journal of Electronics & Information Technology, 2018, 40(1): 226-234. doi: 10.11999/JEIT170306

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Blind Estimation PN Sequence in Soft Spread Spectrum Signal of Improved K-means Algorithm

doi: 10.11999/JEIT170306 cstr: 32379.14.JEIT170306

Funds:

The National Natural Science Foundation of China (61671095, 61371164), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009 CA2003), The Research Project of Chongqing Educational Commission (KJ130524, KJ1600427, KJ1600429)

Received Date: 2017-04-07
Rev Recd Date: 2017-07-11
Publish Date: 2018-01-19

Abstract

Abstract

For the problem of the soft spread spectrum signal Pseudo-Noise (PN) sequence is difficult to estimate by using the coding technology, a blind estimation PN sequence method of soft spread spectrum signal is proposed based on improved K-means algorithm. Firstly, the received signal is divided into continuous non-overlapping temporal vectors according to one period of PN sequence to construct observation data matrix. Secondly, the similarity measure theory is applied to find out the optimal initial clustering center point of K-means algorithm from the observed matrix. Then the number of scale of PN sequence can be estimated by searching for the maximum absolute value of the average Silhouette Coefficient (SC). Finally, the estimated clustering center point corresponding to the number of scale of PN sequence is found, the blind estimation PN sequence of the soft spread spectrum signal is further completed. The simulation results show that the proposed method improves the Signal-to-Noise Ratio (SNR) about 4 dB compared to the traditional method under the condition of the estimation error probability of PN sequence is less than 0.1. Moreover, the blind dispreading performance is also better than unmodified method under the same condition.
- Soft spread spectrum,
- Pseudo Noise (PN) sequence,
- K-means clustering,
- Blind estimation

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References(27)

References

TIAN Ricai and CHI Yonggang. Spread Spectrum Communication[M]. 2nd Ed. Beijing: Tsinghua University Press, 2014: 1-5.

田日才, 迟永钢. 扩频通信[M]. 第2版, 北京: 清华大学出版社, 2014: 1-5.

PURSLEY M B and ROYSTER T C. High-rate direct- sequence spread spectrum with error control coding[J]. IEEE Transactions on Communications, 2006, 54(9): 1693-1702. doi: 10.1109/TCOMM.2006.881256.

周佳晶, 唐友喜. JTIDS扩频序列的估计[J]. 电子科技大学学报, 2007, 36(5): 1054-1056.

ZHOU Jiajing and TANG Youxi. Spread spectrum sequence estimation for JTIDS[J]. Journal of University of Electronic Science and Technology of China, 2007, 36(5): 1054-1056.

沈斌, 王建新. 窄带干扰条件下含有未知载频的直扩信号的伪码序列估计[J]. 电子与信息学报, 2015, 37(7): 1556-1561. doi: 10.11999/JEIT141322.

SHEN Bin and WANG Jianxin. Estimation of PN sequence in DSSS signals with unknown carrier frequency under narrow band interferences[J]. Journal of Electronics Information Technology, 2015, 37(7): 1556-1561. doi: 10.11999/JEIT141322.

任啸天, 徐晖, 黄知涛, 等. 短码DS-SS信号扩频序列及信息序列联合盲估计方法[J]. 通信学报, 2012, 33(4): 169-175. doi: 10.3969/j.issn.1000-436X.2012.04.023.

REN Xiaotian, XU Hui, HUANG Zhitao, et al. Joint blinding estimation of the spread-spectrum sequence and information sequence for short-code DS-SS signal[J]. Journal on Communications, 2012, 33(4): 169-175. doi: 10.3969/j.issn. 1000-436X.2012.04.023.

张天骐, 赵军桃, 江晓磊. 基于多主分量神经网络的同步DS- CDMA伪码盲估计[J]. 系统工程与电子技术, 2016, 38(11): 2638-2647. doi: 10.3969/j.issn.1001-506X.2016.11.27.

ZHANG Tianqi, ZHAO Juntao, and JIANG Xiaolei. PN code sequence blind estimate of synchronous DS-CDMA based on multi-principal component neural network[J]. Systems Engineering and Electronics, 2016, 38(11): 2638-2647. doi: 10.3969/j.issn.1001-506X.2016.11.27.

张天骐, 强幸子, 马宝泽, 等. 基于最小二乘的同步多用户非周期长码直扩信号扩频序列估计[J]. 电波科学学报, 2016, 31(6): 1113-1123. doi: 10.13443/j.cjors.2016030201.

ZHANG Tianqi, QIANG Xingzi, MA Baoze, et al. Estimation of the spread spectrum sequence for synchronous multi-user a periodic long-code DSSS signals based on least squares[J]. Chinese Journal of Radio Science, 2016, 31(6): 1113-1123. doi: 10.13443/j.cjors.2016030201.

GU Xiaolei, ZHAO Zhijin, and SHEN Lei. Blind estimation of pseudo-random codes in periodic long code direct sequence spread spectrum signals[J]. IET Communications, 2016, 10(11): 1273-1281. doi: 10.1049/iet-com.2015.0374.

赵知劲, 李淼, 尚俊娜. 基于矩阵填充和三阶相关的长短码DS-CDMA信号多伪码盲估计[J]. 电子与信息学报, 2016, 38(7): 1788-1793. doi: 10.11999/JEIT151087.

ZHAO Zhijin, LI Miao, and SHANG Junna. Blind estimation of LSC-DS-CDMA signal based on matrix completion and triple correlation[J]. Journal of Electronics Information Technology, 2016, 38(7): 1788-1793. doi: 10.11999/ JEIT151087.

王航, 郭静波, 王赞基. 基于聚类的软扩频信号盲解扩方法[J]. 电子与信息学报, 2009, 31(2): 422-425.

WANG Hang, GUO Jingbo, and WANG Zanji. Clustering based blind despread method of tamed direct sequence spread spectrum signals[J]. Journal of Electronics Information Technology, 2009, 31(2): 422-425.

KISORE N R and KOTESWARAIAH C B. Improving ATM coverage area using density based clustering algorithm and voronoi diagrams[J]. Information Sciences, 2016, 376: 1-20. doi: 10.1016/j.ins.2016.09.058.

ZHANG Tianqi, QIAN Wenrui, ZHANG Gang, et al. Parameter estimation of MC-CDMA signals based on modified cyclic autocorrelation[J]. Digital Signal Processing, 2016, 54: 46-53. doi: 10.1016/j.dsp.2016.03.007

李晓瑜, 俞丽颖, 雷航, 等. 一种K-means改进算法的并行化实现与应用[J]. 电子科技大学学报, 2017, 46(1): 61-68. doi: 10.3969/j.issn.1001-0548.2017.01.010.

LI Xiaoyu, YU Liying, LEI Hang, et al. The parallel implementation and application of an improved K-means algorithm[J]. Journal of University of Electronic Science and Technology of China, 2017, 46(1): 61-68. doi: 10.3969/j.issn. 1001-0548.2017.01.010.

翟东海, 鱼江, 高飞, 等. 最大距离法选取初始聚类中心的K-means文本聚类算法的研究[J]. 计算机应用研究, 2014, 31(3): 713-715. doi: 10.3969/j.issn.1001-3695.2014.03.017.

ZHAI Donghai, YU Jiang, GAO Fei, et al. K-means text clustering algorithm based on centers selection according to maximum distance[J]. Application Research of Computers, 2014, 31(3): 713-715. doi: 10.3969/j.issn.1001-3695.2014.03. 017.

张健沛, 杨悦, 杨静, 等. 基于最优划分的K-Means初始聚类中心选取算法[J]. 系统仿真学报, 2009, 21(9): 2586-2589.

ZHANG Jianpei, YANG Yue, YANG Jing, et al. Algorithm for initialization of K-means clustering center based on optimized-division[J]. Journal of System Simulation, 2009, 21(9): 2586-2589.

THEODORIDIS S and KOUTROUMBAS K. Pattern Recognition[M]. Fourth Ed. USA: Academic Press, 2010: 415-417.

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