基于新信息准则与梅西算法的LSC-DSSS信号序列估计

doi:10.12305/j.issn.1001-506X.2025.02.32

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (2): 659-665.doi: 10.12305/j.issn.1001-506X.2025.02.32

• 通信与网络 • 上一篇

基于新信息准则与梅西算法的LSC-DSSS信号序列估计

张天骐, 吴仙越, 吴云戈, 李春运

重庆邮电大学通信与信息工程学院, 重庆 400065

收稿日期:2023-11-29 出版日期:2025-02-25 发布日期:2025-03-18
通讯作者: 吴仙越
作者简介:张天骐(1971—), 男, 教授, 博士研究生导师, 博士, 主要研究方向为通信信号的调制解调与盲处理、图像与语音信号处理、神经网络实现、现场可编程逻辑门阵列实现、超大规模集成电路实现
吴仙越(2000—), 女, 硕士研究生, 主要研究方向为扩频信号盲估计
吴云戈(2000—), 女, 硕士研究生, 主要研究方向为通信信号盲处理、深度学习
李春运(2000—), 男, 硕士研究生, 主要研究方向为信道编码参数盲识别
基金资助:
国家自然科学基金(61671095);国家自然科学基金(61702065);国家自然科学基金(61701067);国家自然科学基金(61771085);重庆市自然科学基金(cstc2021jcyj-msxmX0836);信号与信息处理重庆市市级重点实验室建设(CSTC2009CA2003);重庆市教育委员会科研项目(KJ1600427);重庆市教育委员会科研项目(KJ1600429)

Sequence estimation of LSC-DSSS signals based on novel information criterion and Massey algorithm

Tianqi ZHANG, Xianyue WU, Yunge WU, Chunyun LI

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2023-11-29 Online:2025-02-25 Published:2025-03-18
Contact: Xianyue WU

摘要/Abstract

摘要：

针对长短码直接扩频序列(long and short code direct sequence spread spectrum, LSC-DSSS)信号序列估计难题, 在已知LSC-DSSS信号参数的条件下, 提出一种基于新信息准则(novel information criterion, NIC)神经网络联合梅西算法的长短码信号序列估计方法。将LSC-DSSS信号输入NIC神经网络以估计随机采样起点, 再通过不断输入数据训练NIC神经网络权值向量。当网络收敛时, 权值向量的符号值即为LSC-DSSS信号的复合码序列片段。使用延迟相乘, 消除幅度模糊与短扩频码序列的影响, 再利用梅西算法获得扰码序列的生成多项式。仿真实验结果表明, NIC神经网络较特征值分解法的抗噪声性能提高6 dB, 同时较Hebbian准则神经网络所需学习组数减少50%。

关键词: 新信息准则, 长短码估计, 梅西算法, 主子空间跟踪

Abstract:

In addressing the challenge of sequence estimation in long and short code direct spread spectrum sequence (LSC-DSSS) signals, a method for estimating long and short code signal sequences based on the novel information criterion (NIC) neural network in conjunction with the Massey algorithm is proposed with known parameters of the LSC-DSSS signal. The LSC-DSSS signal is input into the NIC neural network to estimate random sampling starting points, and the NIC neural network's weight vector is trained by continuously inputting data. When the network converges, the sign values of the weight vector represent a segment of the composite code sequence for the LSC-DSSS signal. Delay multiplication is then used to eliminate the influence of amplitude ambiguity and short spreading sequences. The Massey algorithm is applied to obtain the generating polynomial of the scrambling code sequence. Simulation experiment results demonstrate that the NIC neural network outperforms the eigenvalue decomposition method in noise resistance by 6 dB and it requires 50% fewer learning iterations compared to a Hebbian rules neural network.

Key words: novel information criterion (NIC), long and short code estimation, Massey algorithm, principal subspace tracking (PST)

中图分类号:

TN911.7

张天骐, 吴仙越, 吴云戈, 李春运. 基于新信息准则与梅西算法的LSC-DSSS信号序列估计[J]. 系统工程与电子技术, 2025, 47(2): 659-665.

Tianqi ZHANG, Xianyue WU, Yunge WU, Chunyun LI. Sequence estimation of LSC-DSSS signals based on novel information criterion and Massey algorithm[J]. Systems Engineering and Electronics, 2025, 47(2): 659-665.

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参考文献 22

1	张慧芝,张天骐,方蓉,等.基于SVD-K-means算法的软扩频信号伪码序列盲估计[J].系统工程与电子技术,2024,46(1):326-333. doi: 10.12305/j.issn.1001-506X.2024.01.37
	ZHANGH Z,ZHANGT Q,FANGR,et al.Blind estimation of pseudo-code sequence of soft spread spectrum signal based on SVD-Kmeans algorithm[J].Systems Engineering and Electronics,2024,46(1):326-333. doi: 10.12305/j.issn.1001-506X.2024.01.37
2	QIANG X Z, ZHANG T Q. Estimation of spreading code in non-periodic long-code DSSS signal[C]//Proc. of the 6th International Conference on Wireless Communications, Signal Processing and Networking, 2021: 162-165.
3	王勃,沈雷,卢英俊,等.非协作多用户短码直扩信号伪码估计[J].杭州电子科技大学学报(自然科学版),2023,43(3):9-15, 54.
	WANGB,SHENL,LUY J,et al.Pseudo-code estimation of non-cooperative multi user short code direct spread signal[J].Journal of Hangzhou Dianzi University(Natural Sciences),2023,43(3):9-15, 54.
4	CHOIH,MOONM.Blind estimation of spreading sequence and data bits in direct-sequence spread spectrum communication systems[J].IEEE Access,2020,8,148066-148074. doi: 10.1109/ACCESS.2020.3014884
5	CHOI Y, KIM D, JANG M, et al. Spreading sequence blind estimation in DSSS system using gradient ascent method[C]//Proc. of the 33rd International Telecommunication Networks and Applications Conference, 2023: 76-79.
6	赵知劲,李淼,詹毅,等.LSC-DSSS信号长短伪码盲估计[J].信号处理,2016,32(3):268-275.
	ZHAOZ J,LIM,ZHANY,et al.Blind estimation of long and short PN code in LSC-DSSS signals[J].Journal of Signal Processing,2016,32(3):268-275.
7	喻盛琪,张天骐,赵健根.非周期长码直扩信号PN码盲估计[J].计算机工程与设计,2020,41(6):1509-1515.
	YUS Q,ZHANGT Q,ZHAOJ G.Blind estimation of PN codes for non periodic long code DSSS signals[J].Computer Engineering and Design,2020,41(6):1509-1515.
8	强幸子,金翔,张天骐.基于相似度的NPLC-DSSS信号扩频码盲估计[J].电子学报,2022,50(8):2043-2048.
	QIANGX Z,JINX,ZHANGT Q.Blind estimation for spread spectrum code of NPLC-DSSS signal based on similarity[J].Acta Electronica Sinica,2022,50(8):2043-2048.
9	张天骐.直扩信号的盲处理[M].北京:国防工业出版社,2012:156-197.
	ZHANGT Q.Blind processing for signal of direct sequence spread spectrum[M].Beijing:National Defense Industry Press,2012:156-197.
10	杜柏阳,孔祥玉,冯晓伟,等.主奇异子空间跟踪算法与性能分析[J].控制理论与应用,2020,37(7):1491-1500.
	DUB Y,KONGX Y,FENGX W,et al.Algorithm and its performance analysis of principal singular subspace tracking[J].Control Theory & Applications,2020,37(7):1491-1500.
11	XUL,OJAE,SUENC Y.Modified Hebbian learning for curve and surface fitting[J].Neural Networks,1992,5(3):441-457. doi: 10.1016/0893-6080(92)90006-5
12	WILLIAMSR J.Feature discovery through error-correction learning[M].San Diego:Institute for Cognitive Science, University of California,1985.
13	XIANGY K,CHONGZ H,RUIX W.Modified gradient algorithm for total least square filtering[J].Neurocomputing,2006,70(1):568-576.
14	XUL.Least mean square error reconstruction principle for self-organizing neural-nets[J].Neural Networks,1993,6(5):627-648. doi: 10.1016/S0893-6080(05)80107-8
15	YANGB.Projection approximation subspace tracking[J].IEEE Trans. on Signal Processing,1995,43(1):95-107. doi: 10.1109/78.365290
16	FUZ,DOWLINGE M.Conjugate gradient eigenstructure tracking for adaptive spectral estimation[J].IEEE Trans. on Signal Processing,1995,43(5):1151-1160. doi: 10.1109/78.382400
17	MATHEWG,REDDYV U,DASGUPTAS.Adaptive estimation of eigensubspace[J].IEEE Trans. on Signal Processing,1995,43(2):401-411. doi: 10.1109/78.348123
18	MIAOY,HUAY.Fast subspace tracking and neural network learning by a novel information criterion[J].IEEE Trans. on Signal Processing,1998,64(7):1967-1979.
19	LIX,WANGL Q,MUNOCHIVEYIM.Experimental verification of the modified PSA algorithm[J].IEEE Access,2022,10(7):80394-80402.
20	孔祥玉,冯晓伟,胡昌华.广义主成分分析算法及应用[M].北京:国防工业出版社,2018:27-28.
	KONGX Y,FENGX W,HUC H.General principal component analysis and application[M].Beijing:National Defense Industry Press,2018:27-28.
21	MASSEYJ.Shift-register synthesis and BCH decoding[J].IEEE Trans. on Information Theory,1969,15(1):122-127. doi: 10.1109/TIT.1969.1054260
22	MANAS G, ARULMURUGAN A, SAVITHA R. HebbNet: a simplified hebbian learning framework to do biologically plausible learning[C]//Proc. of the ICASSP IEEE International Conference on Acoustics, Speech and Signal Processing, 2021: 3115-3119.

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基于新信息准则与梅西算法的LSC-DSSS信号序列估计

Sequence estimation of LSC-DSSS signals based on novel information criterion and Massey algorithm

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