Abstract
Direct sequence spread spectrum (DSSS) signals are widely used in various military and civilian communication systems. It adopts the pseudorandom (PN) sequence to modulate baseband signal before transmission, leading to some unique and useful properties such as lower working signal to noise ratio (SNR), strong anti-jamming capability, and robustness to the effect of multi-path fading. In past few years, blind despreading for DSSS signals has been a hot topic in cognitive radio field. Previous works are mostly concentrated on BPSK-DSSS signals, and have developed theories and methods to estimate the PN sequence effectively. But unfortunately, most of them are powerless to process QPSK-DSSS signals. This work proposes a method based on matrix subspace analysis to recover the PN sequences and transmitted symbols for QPSK-DSSS signals with unknown carrier offset under non-cooperative reception occasion. To recover spreading sequences from subspace of self-covariance matrix, we derive the theoretical expressions of eigenvector by studying the complex hermit matrix decomposition and then investigate the influence of carrier residual. Finally, a structure is designed to complete blind despreading and demodulation from the received waveform to bit stream. Simulation verifies pretty good bit-error-rate (BER) performance of proposed algorithm.
Highlights
Direct sequence spread spectrum (DSSS) signals are widely used in satellite, short wave, underwater and mobile communication systems because of its low probability of detection and interception (LPD, LPI) [1]–[4]
Note that due to the phase ambiguity of eigenvectors, the estimate of PN code may be an inverse of original sequence as shown in Fig. 3 and the order can be reversed at the same time
With the increasing of data volume, the proposed two algorithms exhibited pretty better performance in BER of PN code estimation than the other two algorithms when compared under same SNRe, indicating its superiority and feasibility in non-cooperative context
Summary
DSSS signals are widely used in satellite, short wave, underwater and mobile communication systems because of its low probability of detection and interception (LPD, LPI) [1]–[4]. B. DECOMPOSITION METHOD FOR THE ENTIRE SELF-COVARIANCE MATRIX (EVD-C) To analyze the effect of carrier to the subspace, we try to derive the form of the combination coefficients of eigenvectors when decomposing the complex self-covariance matrix. As described in section A, the coefficients of eigenvectors obtained from decomposition for matrix is usually unbalanced resulting in difficulty to compute a and b from the direct linear combination of v1_base and v2_base. Note that due to the phase ambiguity of eigenvectors, the estimate of PN code may be an inverse of original sequence as shown in Fig. 3 and the order can be reversed at the same time. We should mention that the phase ambiguity may be unavoidable when estimating φ0, the influence is just as discussed in table I and can be eliminated by some postprocessing techniques
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