An Unsupervised Adaptive Method to Eigenstructure Analysis of Lower SNR DS Signals

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 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.

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.

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.

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.

In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from -20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities.

In an effort to transmit information over communication channels that contain both Gaussian and non-Gaussian noise components, military and commercial applications frequently rely upon the use of direct sequence spread spectrum (DSSS) techniques. Whilst DSSS is an interference tolerant modulation scheme, situations may be encountered when the processing gain offered by the pseudonoise (PN) spreading code is inadequate. Enhancements to the interference immunity of a DSSS system can be accomplished through the use of signal processing techniques that suppress unwanted interference prior to despreading. To date, the majority of interference suppression techniques developed for DSSS applications, have concentrated on improving detector performance exclusively, and perfect synchronisation between transmitter and receiver is assumed. However, the process of PN code acquisition and tracking within a DSSS receiver is arguably one of the most critical functions within the receiver structure since, if it fails to operate correctly, the wanted signal will not be successfully recovered. An evaluation of the benefits of any interference suppression technique within a DSSS receiver would not be complete without considering at least one level within the synchronisation hierarchy. We examine the improvements in receiver performance, when the process of PN code tracking and detection employs a novel wavelet packet denoising (WPD) procedure for a variety of non-Gaussian channel scenarios.

A wireless underwater acoustic communication system based on the combination of chirp modulation and direct-sequence spread-spectrum signaling is presented. The use of chirp signals takes advantage of the low Doppler sensitivity in the matched filter operation whereas the choice of pseudo noise (PN) sequences allows one to reduce narrowband interference arising from other users and self-interference due to multipath propagation. The system here described is made of a transmitter encoding the bits with linear chirps multiplied by PN sequences, and a rake receiver that allows one to positively exploit the energy present in the most significant propagation paths. Moreover a tracking procedure that allows an adaptation to the instantaneous Doppler shift has been devised and tested. Results, obtained on real data over a long-range shallow-water channel with a moving transmitting platform, are presented and discussed.

Under the software environment of MATLAB/Simulink, the communication system of direct sequence spread spectrum (DSSS) is dynamically modeled and simulated over a new practical Rayleigh channel, and the relationship curves of the bit error rate (BER) with the signal-to-noise ratio (SNR) of DSSS communication systems are presented. It is analyzed that DSSS system can effectively overcome multipath fading in multipath Rayleigh Fading Channel (RFC). In addition, the anti-multipath fading performance improved by the way of increasing pseudo noise (PN) code length is better than that by the way of increasing the spread spectrum gain in the condition of strong multipath fading.

Pseudorandom noise (PRN) sequences are used to provide a means to measure the ranging distance between a ground station and a satellite. Satellite Ground Link System (SGLS), Global Positioning System (GPS), and some radars are some of the examples that make use of PRN sequences. PRN ranging also provides a certain level of immunity to narrowband interference. The basic procedure involves transmitting a long binary PRN sequence, and measuring the time between its transmission and reception. A PRN ranging system will typically measure the propagation time by performing correlation of the received sequence with the locally generated sequence. A simple equation is used to convert that propagation time value to an accurate range. The length of the PRN sequence determines the maximum unambiguous distance that can be measured, while the code rate and code signal-to-noise ratio (SNR) or sometimes ranging code power-to-noise density ratio (C/No) determine the ranging accuracy. Typically a delay lock loop is used to measure the propagation delay. The SNR in the correlation receiver tracking loop bandwidth determines the phase error variance which is the range measurement accuracy. The SNR is a function of both the signal and noise power at the receiver, and the range signal power is a function of range modulation index. Normally the range mod indices are related through the turnaround ratio for the uplink and the downlink when the range signal is a coherent signal in SGLS. Range mod indices on the up- and downlinks can be different. This is more frequently found on new satellites with the need to have different uplink and downlink range mod indices to improve the link margin and the ranging accuracy, and also to reduce the interference such as from the command echo and intermodulation (IM) signals. In this paper the effect of different uplink and downlink range modulation indices on the achievable range accuracy is presented along with the details of the equations developed.

Soft synchronization of direct sequence spread spectrum (DSSS) signals introduces the capability of despreading the signals with improving estimates of the spreading code. The technique presented here exploits the eigenstructure of a frequency-channelized DSSS signal to estimate the code. The estimate improves steadily with time, resulting in an improving estimate of the message signal. This allows commencement of despreading at an early stage when the code-estimate is still imperfect. This paper presents the development of the algorithm for a single user environment under infinite time-average assumptions. The performance of the technique under finite time-average conditions is demonstrated through computer simulations for single user as well as multi-user environments.

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 (DSSS) techniques are commonly used for characterizing RF communications channels. These techniques have relative immunity to interference and do not require a direct reference connection between the transmitter and receiver. Current DSSS propagation measurement systems use a pseudo noise (PN) sequence to probe the impulse response of the channel. However, the use of PN sequences can introduce noise into the measurements. This noise is caused by a bias term that is inversely proportional to the length of the PN-Sequence, and limits the dynamic range of the measurement system. In this paper it is shown that this bias term will not be present when zero correlation zone (ZCZ) sequences are used. Consequently the dynamic range of the measurement system is limited by the SNR instead of the sequence itself. This paper also provides test data to show the effects of the bias term and to compare and contrast the use of PN and ZCZ sequences.

For the problem of Direct Sequence-Code Division Multiple Access (DS-CDMA) signal in traditional asynchronous single-channel, including blind estimation of the Pseudo-Noise (PN) sequence and information sequence, a method using multi-channel synchronous and asynchronous based on PARAllel FACtor (PARAFAC) is proposed. Firstly, the signal is modeled as a multi-channel receiving model, then the observed data matrix is equivalent to a factor model. Finally, the iterative least squares algorithm is applied to decomposing the parallel factor, and the information sequence and PN sequences of DS-CDMA signals are further estimated. The simulation results show that the proposed method can effectively estimate the PN sequence and information sequence of the short code DS-CDMA signal, and the estimation of 6 user PN sequences can be realized under the condition that the number of channels is 10 and the Signal-to-Noise Ratio (SNR) is –10 dB.