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

Abstract

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)

CLC Number:

TN911.7

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

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