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Moradi, R., kiani, H., khabaz, S. (2025). Spreading factor estimation of direct sequence spread spectrum signals with frequency offset at low SNR ratio. Electronic and Cyber Defense, (), -.
Rasoul Moradi; hossein kiani; saeid khabaz. "Spreading factor estimation of direct sequence spread spectrum signals with frequency offset at low SNR ratio". Electronic and Cyber Defense, , , 2025, -.
Moradi, R., kiani, H., khabaz, S. (2025). 'Spreading factor estimation of direct sequence spread spectrum signals with frequency offset at low SNR ratio', Electronic and Cyber Defense, (), pp. -.
Moradi, R., kiani, H., khabaz, S. Spreading factor estimation of direct sequence spread spectrum signals with frequency offset at low SNR ratio. Electronic and Cyber Defense, 2025; (): -.

Spreading factor estimation of direct sequence spread spectrum signals with frequency offset at low SNR ratio

پدافند الکترونیکی و سایبری
Articles in Press, Corrected Proof, Available Online from 01 February 2025
Document Type: Original Article
Authors
Rasoul Moradi* 1; hossein kiani2; saeid khabaz3
1Master's degree, University of Science and Technology, Tehran, Iran
2Master's degree, Shahid University, Tehran, Iran
3Master's degree, Imam Hossein University, Tehran, Iran
Receive Date: 30 August 2024,  Revise Date: 01 December 2024,  Accept Date: 12 January 2025 
Abstract
In direct sequence spread spectrum systems (DSSS), two types of short or long code pseudo noise (PN) sequence are used to spread the signal. In receivers that extract signal content blindly, the spreading factor is unknown. In this article, the problem of estimating the spreading factor in DSSS signals with frequency offset is discussed. At first, the data correlation matrix is calculated based on the overlapping time intervals method. Then, using correlation matrix, eigenvalues and eigenvectors are calculated. Finally, using the second power of the eigenvector power spectrum density, the value of the frequency offset is estimated. In the next step, using the missing data method, the covariance matrix of these data is constructed for the length of different time windows at different delays. Then, by removing the main digonal of the covariance matrices, the effect of noise is reduced. In the next step, the second power of the Ferbenius norm of the covariance matrices is calculated and the variance value of these norms is the criterion for detecting the spreading factor. The simulation results show that the proposed method presented in this article is highly efficient at low SNRs and has better results than the existing estimators.
Keywords
Direct sequence spread spectrum; Spreading factor; missing data; Covariance matrice; PN sequence
Main Subjects
Signal processing related areas
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