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 informationcriterion and Massey algorithm

作　　者：张天骐[1] 吴仙越 吴云戈 李春运 ZHANG Tianqi;WU Xianyue;WU Yunge;LI Chunyun(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《系统工程与电子技术》2025年第2期659-665,共7页Systems Engineering and Electronics

基　　金：国家自然科学基金(61671095,61702065,61701067,61771085);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,KJ1600429)资助课题。

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%。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.

关 键 词：新信息准则 长短码估计 梅西算法 主子空间跟踪 

分 类 号：TN911.7[电子电信—通信与信息系统]