An eigenstructure technique for soft synchronization of DSSS signals

Due to the broad bandwidth nature of the direct sequence spread spectrum (DSSS) signal, the application of narrowband interference (NBI) cancelation algorithms based on the Nyquist‐Shannon sampling theory are limited by the high sampling rate. To solve the sampling and processing difficulty in the traditional DSSS communication interference cancelation method, we used the compressive sensing (CS) to reduce the sampling rate of the DSSS communication system, and further proposed a compressed domain NBI elimination method in this study. We proved that NBI with a certain bandwidth and DSSS signal are both sparse in specific dictionaries and proposed the corresponding sparse dictionary construction method. Detailed analysis of the compressed domain features of NBI and the DSSS signal demonstrated that it is possible to extract the strong NBI components from the compressed measurements. Two compressed domain NBI cancelation algorithms were proposed based on this idea. The extracted NBI components were filtered out from the compressed measurements directly in the first algorithm, and then we achieved the DSSS signal demodulation in compressed domain using the separated DSSS signal components with the orthogonal matching pursuit (OMP) algorithm. A proposed extraction filter block sparse Bayesian learning (EFBSBL) framework algorithm was utilized to estimate the NBI from the compressed measurements in the second algorithm. The estimated NBI was eliminated from the received signal, and then we recompressed the DSSS signal after NBI cancelation and realized the DSSS signal demodulation in compressed domain with the OMP algorithm. Simulation results support our theoretical analysis results and reveal that the two proposed algorithms are both effective for NBI cancelation and significant interference cancelation performance are achieved.

According to the demand of spread spectrum signal processing in lower SNR conditions, a novel blind estimation algorithm of the pseudo-noise (PN) sequence for the direct sequence spread spectrum (DSSS) signal is proposed based on the similarities among the DSSS signals and the eigenanalysis technique. At first, through segmentation processing and average cross-correlation calculation, the starting symbol of PN sequence is determined. Next, by utilizing the eigenanalysis technique PN sequence can be estimated accurately without any information of the DSSS signal. Simulation results show that compared with the existing algorithms under the same condition, the proposed algorithm not only overcomes the partial-encode problem, but also improves estimation performance by 2dB. Therefore, the proposed algorithm achieves an accurate estimation of PN sequence in lower SNR conditions.

In this paper, we investigate the possibility of improving the blind detection performance of direct sequence spread spectrum (DSSS) signals for cognitive radios (CRs). We consider a scenario where a wide range of frequency spectrum needs to be monitored by a single CR, and the presence of spread spectrum signals need to be identified reliably in a cost effective manner. We employ compressive sensing to achieve realistic sensing time without imposing excessive sampling-rate requirements on the analog-to-digital converter of the CR. We assume that the number, the center frequencies, and the spreading codes of the DSSS signals are unknown, but only the spread signal's bandwidth is known. We propose a three-step algorithm for the CR. First, the power spectral density (PSD) of the wideband spectrum is estimated using compressed samples, and then detection is performed by thresholding to detect spectrum occupancy based on the estimated PSD. In the third step, knowledge of the spread signal's bandwidth, and the estimated PSD are used to perform fusion by looking at spectrum occupancy at adjacent frequency bins using a sliding window. We present some simulation results illustrating the performance gain in detection achieved by introducing the fusion step. This is a useful result since it allows us to detect with improved performance the presence of multiple DSSS signals distributed over a very wideband spectrum, without requiring the knowledge of each signal's spreading code.

Direct-Sequence Spread Spectrum (DS-SS) signals are widely used in modern communications. The detection and parameter estimation of DS-SS signals in non- cooperative context have been paid many attentions. Up to now, most of the proposed methods consider only short-code DS-SS signals, and long-code DS-SS signals have hardly any been studied. In this paper, a method for blind estimation of the symbol period of a long-code DS-SS signal is proposed. The proposed method is based on further processing of the autocorrelation estimator. Simulation results are provided to illustrate the approach.

W399 the development of underwater acoustic communications (UWAC), jamming methods for UWAC becomes much more attentive in communication countermeasure system. Direct sequence spread spectrum (DSSS) signal as a way of reliable covert communications is widely used for UWA channels. In this paper, single-tone jamming, narrowband jamming and correlated jamming based on PN code PSK modulation for UWA DSSS communication system are compared under different signal-to-noise ratio first, which assumes that the jammer knows perfect parameters in DSSS signal. Besides, performance of the above three jamming methods are compared by simulations under the blind estimates of carrier frequency, chip length, period of PN code and PN code itself, which demonstrates the influence of the estimation error on these jamming methods.

In this paper, we consider the problem of Doppler tracking and compensation for a direct sequence spread spectrum (DSSS) signal in underwater acoustic (UWA) communication. Since the dynamic property of the UWA channel and the long duration of DSSS signals result in significant Doppler spread that severely distorts the propagated signal, Doppler tracking and compensation are required. Based on the ultra-wideband property of UWA signal, the Doppler spread not only results in the frequency shift, but also changes the signal duration. Therefore, the accurate estimation of the signal expansion/compression in the time domain can reflect the Doppler spread. Accordingly, we present a Doppler tracking and compensation algorithm for a DSSS signal operating on the correlation output of passband signal at a symbol-by-symbol basis. Note that the carrier frequency of UWA communication is around several kilohertz, and thus the time delay estimation can be performed on the passband to improve the accuracy. Furthermore, the prior information of Doppler limit is used to refine the resolution of delay estimation and achieve sequential estimation. To compensate the correlation magnitude distortion induced by the velocity variation, the local reference signal is selected adaptively based on the filtered Doppler factor. Simulation results demonstrate that the proposed passband Doppler tracking algorithm achieves a superior performance compared with the conventional receiver.

In this paper, we develop a method based on the reprocessing of the power spectrum density (PSD) of direct sequence spread spectrum (DS-SS) signals with residual carrier, it can solve difficult problem of period detection and estimation of the pseudo noise (PN) sequence for weak DS-SS signals in dynamic environments. The method is developed from another method, which is based on the reprocessing of DS-SS signalspsila PSD in normal environments before. The first step of the method is to calculate the PSD of weak DS-SS signals in dynamic environments; then, we use the calculated PSD as an input signal to calculate their PSD again; the second PSD result will appear some regular tip-pulses at the integer-times of PN sequencepsilas period. We can detect and estimate the period of PN sequence from the interval measurement between the regular tip-pulses of the second PSD result in the end. The computer simulations show that the method can work well on lower signal to noise ratio (SNR) input signals with larger residual carrier in dynamic environments.

Direct sequence spread spectrum (DSSS) communication has been extensively adopted in military and civilian fields because of its high processing gain, anti-jamming capability and low probability of interception (LPI). In this paper, non-cooperative interception of DSSS signal in multiple signals environment is presented. The proposed algorithm is based on the fluctuations of autocorrelation, and also banks upon proper thresholding criteria. This methodology comes out to be a robust practical method for blind detection of DSSS signals in a dense multiple signals environment. In the proposed approach, firstly DSSS signals are blindly detected by leveraging the variations in autocorrelation of the received signal. Then, the presence of desired DSSS signal is confirmed when correlation peaks cross the threshold at multiple positions after a fixed period. In a real-time environment, signals other than spread spectrum with much better signal-to-noise ratio (SNR) can also co-exist. Among these multiple signals, the high SNR signals disturb the threshold selection criteria for detection. Consequently, this leads to erroneous or miss detection of the DSSS signal. The proposed scheme aims to reduce the effect of better SNR signals on the blind detection of DSSS signal. In order to validate the proposed algorithm, QPSK and 16-QAM signals are considered to co-exist with the desired DSSS signal. Analytical and numerical analysis signify the efficiency of the proposed algorithm with acceptable computational complexity, making it suitable for real-time practical systems.

The complex electromagnetic environment will limit the efficacy of communication equipment. It is critical to construct a complex electromagnetic environment to test communication equipment in order to maximize its capability. One of the most important methods for constructing a complex electromagnetic environment is signal reconstruction. This paper proposes a VAE‐GAN‐based method for reconstructing direct sequence spread spectrum (DSSS) signals. In this method, the deep residual shrinkage network (DRSN) and self‐attention mechanism are added to the encoder and discriminator of VAE‐GAN. In feature learning, the DRSNs can reduce the redundant information caused by noise in the collected signal. The self‐attention mechanism can establish the long‐distance dependence between the input sequences, making it easier for the network to learn the samples’ pseudonoise (PN) sequence features. In addition, feature loss is applied to the encoder and generator to improve network stability during training. The results of the experiments indicate that this method can reconstruct DSSS signals with the characteristics of the target signal.

The detection and parameter estimation of spread spectrum signals under non-cooperative conditions are key problems to which more attentions are paid in the field of signal processing. A cyclic-cumulant based DS-SS (direct sequence spread spectrum) signal detection scheme is developed in this paper by extending the asymptotically optimal chi-squared test for the presence of cyclostationarity to DS-SS signal detection. The explicit formula of the second-order cyclic-cumulant is derived, and the influence of time interval on the cycle-frequency estimation from the cyclic-cumulant is analyzed in detail. On the base of this, a new cyclic-cumulant based DS-SS signal parameter estimation method is proposed, which can perform blind detection and blind parameter estimation without a priori knowledge of the signal such as pseudorandom sequence and frequencies. Moreover, the method also avoids multi-dimensional searching in parameter estimation. Simulation results have proved the validity of the method.

A new adaptive estimator for direct sequence spread spectrum (DSSS) signals using fourth-order cumulant based adaptive method is considered. The general higher-order statistics may not be easily applied in signal processing with too complex computation. Based on the fourth-order cumulant with 1D slices and adaptive linear filters, an efficient algorithm is proposed to solve the problem and extended for non-stationary stochastic processes. In order to achieve accurate parameter estimation of direct sequence spread spectrum (DSSS) signals, the first step uses the modified fourth-order cumulant to reduce the computing complexity. While the second step employs an adaptive recursive system to estimate the power spectrum in the frequency domain. For the case of intercepted signals with enough large data samples, the estimator provides good performance in parameter estimation and the white Gaussian noise suppression. Computer simulations are included to corroborate the theoretical development with different signal-to-noise ratio conditions and iterative coefficients.

Direct sequence spread spectrum (DSSS) signal uses phase coherent signals where the information symbols are multiplied with a code sequence, commonly known as chips. The signals are processed at the receiver using the code sequence as a matched filter to extract the information symbols. Taking advantage of the spread processing gain derived from the matched filter, communications can be carried out at low signal levels, which is the preferred method for high quality underwater acoustic (UWA) communication and remote UWA communication. However, in practical applications, DSSS UWA communication will face lots of interferences including multipath interference, Doppler compression interference and phase fluctuation interference, which will seriously affect the performance of DSSS. This paper first analyzes the interferences that DSSS faces in practical applications based on multiple actual received data collected in different water areas. By calculating the gain of the matched filter loss, one can find that influence of phase fluctuation interference has the most serious impact on DSSS. Secondly, the corresponding receiver processing algorithms are given for these interferences, including the differential energy detector and Doppler estimation method. The performance of differential energy detector under the above interferences is analyzed, which shows that the differential energy detector is insensitive to slow phase fluctuation interference and multipath interference and the differential energy detector combined with Doppler compensation can work well based on moving at-sea data. Finally, the parameter design of DSSS system in practical application is discussed and parameter design of DSSS UWA communication system should comprehensively consider the Doppler compression interference and phase fluctuation interference.

The conventional hybrid search algorithm for direct sequence spread spectrum (DSSS) signal acquisition terminates once a decision variable exceeds the detection threshold. Therefore, a spurious “hit” declared before the search reaches the cell where the target signal is located avoids any possibility of a correct detection. This behavior increases the probability of false detection, especially in abnormal scenarios, such as when in the presence of interfering signals. To improve the probability of correct detection under a constant false-alarm rate criterion, this paper proposes an advanced hybrid acquisition scheme, whose search area is extended to the whole Doppler frequency and code phase delay uncertainty region. To reduce the mean acquisition time (MAT) increment resulting from this increased search area, both parameters of a Tong detector and the detection threshold are adaptively customized, according to a signal-to-noise ratio estimate obtained during the search operation. In addition, analytic expressions for the detection probability and false-alarm probability are derived. The most prominent advantage of the proposed method is that it can reduce the false detection probability noticeably, with little increase in resource utilization, and an acceptable MAT. The validity of this algorithm is verified, both theoretically and through simulation. The proposed algorithm is particularly suitable for DSSS signal acquisition when in the presence of either multiple access interference or continuous wave interference.

This study considers the transmission of digital signals in-band on-channel for application in digital audio broadcasting (DAB). In the FM-channel used for analog transmission, two different DAB scenarios are simulated. In the first approach, the digital data is spread in form of a weak direct sequence spread spectrum (DSSS) signal underneath the FM. Since the FM signal is a multitone interference to the DSSS signal, it is to be excised. In this paper, a new adaptive frequency domain technique is proposed for excising multitone interference in DAB, The second scenario performs an amplitude modulation on the FM signal. This technique assures no interference in-between the new, digital, and the already existing analog service,.

The delay lock loop (DLL) is a prevailing approach to synchronizing a direct sequence spread spectrum (DS-SS) signal, but the analysis and results are mainly applied to its steady state behavior. In some burst situations, DLL (or some other closed tracking loop) may be in transient state, or unable to lock the signal with high accuracy in the given time interval. The authors propose a simple and efficient delay estimation interpolation method to such burst circumstances and consider the channel filter distortion effect at the same time.