Direct Sequence Spread Spectrum Signals Research Articles

Direct sequence spread spectrum (DSSS) signal detection based on eigenvalues local binary pattern residual network (EL-ResNet)

Direct sequence spread spectrum (DSSS) signal detection based on eigenvalues local binary pattern residual network (EL-ResNet)

Detection of Signals with Chaotic Varying Forms and Low Intercept Probability

Low probability of intercept performance of direct-sequence spread-spectrum system with chaotic spreading sequences is investigated. The energy detectors, synchronous and asynchronous, coherent and non-coherent structures are studied here to detect the presence of chaotic direct-sequence spread-spectrum signals. Simple detection approach using a binary correlation function to detect nonbinary chaotic sequences is proposed. The expressions of detection probabilities of chaotic spreading signals are derived. Comparisons between systems using chaotic and binary sequences are given in terms of the low probability of intercept performance, and the performance improvement with chaotic spreading sequences is observed.

Cyclostationarity of Communication Signals in Underwater Acoustic Channels

The effect of underwater acoustic propagation on the cyclostationary features of communication signals is modeled and analyzed. Two kinds of channels are considered: the multiscale-multilag channel, over which mobile and wideband acoustic systems usually communicate, and the dispersive channel resulting from low-frequency modal propagation in shallow water. It is shown that multiscale-multilag channels transform cyclostationary signals into a sum of velocity and acceleration-dependent time-warped cyclostationary processes. This time-warping is carefully taken into account to efficiently recover the cyclostationary features. On the other hand, it is found that low-frequency dispersive channels preserve the original periodicity but attenuate the shorter cycles and spread the correlations. To illustrate the theoretical results, applications with simulated and real data are also presented. Specifically, the problem of estimating time-varying Doppler scales is addressed for multiscale-multilag channels as well as the detection of signals with unique cyclostationary signatures. The example of blind symbol-rate estimation applied to covert communications in dispersive channels is also discussed. Special attention is paid to phase-shift keying (PSK), quadrature amplitude modulation (QAM), orthogonal frequency-division multiplexing (OFDM), and direct-sequence spread-spectrum (DSSS) signals. Accompanying supplementary material provides the MATLAB code used for the estimation and detection examples.

Period Estimation of Spread Spectrum Codes Based on ResNet.

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.

Intelligent Search Strategy for Doppler Frequency Based on Fuzzy Logic in DSSS Signal Acquisition

This study focuses on Doppler frequency search strategy for signal acquisition in direct sequence spread spectrum (DSSS) systems. According to DSSS signal accumulation energy vibration characteristics, the whole two-dimension search region can be divided into non-vicinity region, transition region and vicinity region. If only one search strategy is used for the whole region, it is difficult to balance the frequency search times and the frequency estimation accuracy. To solve the above problem, this study proposes an intelligent Doppler frequency search (IDFS) strategy. A fuzzy logic (FL) controller with two sets of fuzzy parameters is utilized to adjust the frequency search step size in the non-vicinity region and vicinity region, respectively. In the non-vicinity region, by jointly considering signal amplitude and code phase to design the fuzzy parameters of the FL controller, the IDFS strategy can reduce the frequency search times while reducing frequency false detection caused by abnormal change of signal energy. In the vicinity region, the fuzzy parameters are designed based on our previous work which aims to achieve higher frequency estimation accuracy. The effectiveness of the IDFS strategy is evaluated through Monte Carlo simulations. Simulation results show that the proposed intelligent search strategy can reduce the frequency search times while ensuring frequency estimation accuracy compared to other existing search strategies. Moreover, the proposed search strategy can reduce false detection in the non-vicinity region.

Cell-Straddling Robust-Fast Cross-Correlation Mitigation via MMSE Filter for DSSS Signals

Existing cross-correlation mitigation algorithms based on the minimum mean square error (MMSE) criterion can effectively suppress multiple access interference (MAI) but suffer from high complexity and modeling grid mismatch. In this work, we propose a cell-straddling robust-fast cross-correlation mitigation (CSR-FCCM) algorithm with two improvements. First, CSRFCCM combines the two-dimensional joint iterative adaptive filtering with interference cancellation, which significantly reduces the number and computational cost of complex amplitude MMSE filters. Second, two discriminators are designed to estimate the straddling offset of delay and frequency for the direct sequence spread spectrum (DSSS) signal. The mismatch problem can be ameliorated by substituting estimated straddling offsets into the signal model, which further improves the MAI mitigation effect. The effectiveness of the CSR-FCCM is verified by simulations using 1023- and 63- length Gold codes. Simulation results show that CSR-FCCM has a better MAI mitigation performance and a lower complexity than the open-loop MAI mitigation algorithms, including 2D-JIAF, and RSR-APC.

DSSS Signal Detection Based on CNN.

With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB.

Collaborative LEO Satellites for Secure and Green Internet of Remote Things

The Internet of Remote Things (IoRT) supported by Low Earth Orbit (LEO) satellites is becoming indispensable for remote sensing, and it will play an important role in the forthcoming sixth generation (6G) communication network. In exploring its applications in remote mining and smart grid, etc., it is found that the implementation of IoRT faces challenges, including limited energy supplies, high mobility, and security vulnerabilities. To address these challenges, we propose employing collaborative LEO satellites to enable the implementation of secure and green IoRT. By combining the uplink signals received at collaborative LEO satellites, the Signal-to-Noise Ratio (SNR) can be significantly improved, so that relieving the transmit power requirement of the energy-limited terminal. Aiming at constructing a collaborative LEO satellite-based IoRT network, this paper introduces the system design principles regarding to frequency planning, waveform selection, collaboration strategies, and terminal design. In order to obtain optimal collaboration performance, we propose a signal coherent combining scheme to compensate Doppler shift, propagation delay, and phase differences. Furthermore, we propose a modified SUMPLE algorithm to estimate and compensate phase differences among satellites, which is applicable to Direct Sequence Spread Spectrum (DSSS) signal scheme. Simulation results demonstrate that our proposed algorithm outperforms the traditional SUMPLE algorithm in combining gain.

Blind despreading and deconvolution of asynchronous multiuser direct sequence spread spectrum signals under multipath channels

AbstractIn non‐cooperative scenarios, the spreading sequences or waveforms of the direct sequence spread spectrum (DSSS) signals is unknown to the receiver. This paper focuses on addressing the problem of blind estimation of the spreading waveform under multipath channels. In the scenario of direct signal path transmission, the spreading sequences can be directly obtained based on the estimated spreading waveforms. However, in the presence of multipath channels, the spreading waveform becomes the convolution of the spreading sequence and channel response, thus deconvolution should also be performed after estimating the spreading waveforms. In order to perform blind despreading and deconvolution of asynchronous multiuser DSSS signals under multipath channels, the authors propose to exploit the finite symbol characteristics of information and spreading sequences and then the iterative least square with projection method is adopted. Besides, the Cramer‐Rao bound of spreading waveforms is derived in such a circumstance as a performance benchmark. The effectiveness of the proposed method is verified via simulation experiments.

Gaussian-Distributed Spread-Spectrum for Covert Communications.

Covert communication techniques play a crucial role in military and commercial applications to maintain the privacy and security of wireless transmissions from prying eyes. These techniques ensure that adversaries cannot detect or exploit the existence of such transmissions. Covert communications, also known as low probability of detection (LPD) communication, are instrumental in preventing attacks such as eavesdropping, jamming, or interference that could compromise the confidentiality, integrity, and availability of wireless communication. Direct-sequence spread-spectrum (DSSS) is a widely used covert communication scheme that expands the bandwidth to mitigate interference and hostile detection effects, reducing the signal power spectral density (PSD) to a low level. However, DSSS signals possess cyclostationary random properties that an adversary can exploit using cyclic spectral analysis to extract useful features from the transmitted signal. These features can then be used to detect and analyse the signal, making it more susceptible to electronic attacks such as jamming. To overcome this problem, a method to randomise the transmitted signal and reduce its cyclic features is proposed in this paper. This method produces a signal with a probability density function (PDF) similar to thermal noise, which masks the signal constellation to appear as thermal white noise to unintended receivers. This proposed scheme, called Gaussian distributed spread-spectrum (GDSS), is designed such that the receiver does not need to know any information about the thermal white noise used to mask the transmit signal to recover the message. The paper presents the details of the proposed scheme and investigates its performance in comparison to the standard DSSS system. This study used three detectors, namely, a high-order moments based detector, a modulation stripping detector, and a spectral correlation detector, to evaluate the detectability of the proposed scheme. The detectors were applied to noisy signals, and the results revealed that the moment-based detector failed to detect the GDSS signal with a spreading factor, N = 256 at all signal-to-noise ratios (SNRs), whereas it could detect the DSSS signals up to an SNR of -12 dB. The results obtained using the modulation stripping detector showed no significant phase distribution convergence for the GDSS signals, similar to the noise-only case, whereas the DSSS signals generated a phase distribution with a distinct shape, indicating the presence of a valid signal. Additionally, the spectral correlation detector applied to the GDSS signal at an SNR of -12 dB showed no identifiable peaks on the spectrum, providing further evidence of the effectiveness of the GDSS scheme and making it a favourable choice for covert communication applications. A semi-analytical calculation of the bit error rate is also presented for the uncoded system. The investigation results show that the GDSS scheme can generate a noise-like signal with reduced identifiable features, making it a superior solution for covert communication. However, achieving this comes at a cost of approximately 2 dB on the signal-to-noise ratio.

Direct Sequence Spread Spectrum Signal’s Demodulator Acquisition Implementation Based on Fast Fourier Transform. Part 1. Problem Statement and Solution Approach

The article is dedicated to direct sequence spread spectrum signals acquisition. Communication channels that use direct sequence spread spectrum signals are characterized by significant delay and carrier frequency shift as a rule. That’s why the acquisition is most significant problem in receive equipment design. Suggested approach to acquisition problem base on the Fast Fourier Transform and discussed modeling suggested algorithm’s results.

A Novel Recognition Method for Direct Sequence Spread Spectrum (DSSS) Signals Based on Secondary Power Spectrum

A Novel Recognition Method for Direct Sequence Spread Spectrum (DSSS) Signals Based on Secondary Power Spectrum

Blind Estimation Method of the Pseudo-Noise sequence of DS-SS Signals Based on Improved Propagator

The problem of pseudo-noise (PN) sequence estimation of direct sequence spread spectrum (DS-SS) signals is studied in this paper. A new estimation method for PN sequence is given based on improved propagator. The algorithm could estimate the spread spectrum sequence and the signal out-of-step time by using the signal subspace. Compared with the eigenvalue decomposition (EVD) method, the estimation accuracy of the out-of-step time is improved and the amount of calculation is effectively reduced. Computational complexity and performance comparisons are made between the new method and the existing algorithm. Simulation results show that the method can provide a good estimation, even the received signal is below the noise level.

Detection of Direct Sequence Spread Spectrum Signals Based on Deep Learning

Direct spread spectrum communication has the advantages of strong anti-jamming ability and low probability of interception, which plays an essential role in both civil and military communications. The detection of direct sequence spread spectrum (DSSS) signal becomes very difficult because of its low power spectral density. In this paper, the detection of DSSS signals under non-cooperative conditions is carried out based on the classification technology of deep learning. Firstly, a detection scheme based on convolutional neural network (CNN) is proposed, in which the neural network is used to learn the features of DSSS signal and noise automatically without extracting the features in advance. In addition, we also propose a hybrid CNN-correlation (CORR)-based detection scheme in order to reduce the computational complexity. In this scheme, the autocorrelation of the received signal is truncated and used as the input of the neural network for training and inference. A large number of simulation results show that the detection performance of the proposed two schemes is significantly better than the traditional autocorrelation-based detection method in various scenarios.

Waveform Reconstruction of DSSS Signal Based on VAE-GAN

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.

Performance of DSSS Signal Transmission with SCM over Low-Frequency MMF Passbands

The transmission of a high data rate signal over low-frequency passbands of multimode fibers is studied in this paper. The subcarrier multiplexing (SCM) technique is applied to mitigate the frequency-selective nature of the passbands, at which many nulls can degrade the signal transmission. The subcarrier frequencies must be chosen appropriately, especially for some low-frequency passbands; otherwise, poorly received subcarrier signals will be obtained, affecting the entire transmission. Rather than transmitting many subcarrier signals over these passbands, the direct sequence spread spectrum (DSSS) technique can be adopted. In this work, a high data rate signal is transmitted over 1 km of multimode fiber using the 3-dB modal band and six other low-frequency passbands. The signal is separated into four sub-signals transmitted over four different channels: the 3-dB modal band, two low-frequency passbands, and one passband (containing four low-frequency passbands). In the last passband, the sub-signal is transmitted using the DSSS, while the other sub-signals are transmitted through amplitude shift keying (ASK) modulation. The performance of the system is determined using the bit error rate (BER). The findings reveal that by applying the DSSS to some low-frequency passbands, robustness is obtained in the subcarrier frequency for use in the DSSS passband. A BER lower than 10-9 with a total data rate of 600 Mbps is achieved. This data rate is three times higher than the data rate obtained by the 3-dB modal band. This performance is achieved without applying any error-correction code.

Acquisition of Time and Doppler Shift in WFRFT Encrypted Chaotic Direct Sequence Spread Spectrum System

Weighted fractional Fourier transform encrypted chaotic direct sequence spread spectrum (WFRFT-CD3S) signal is aperiodic and complex Gaussian distributed. The aperiodicity and complex Gaussian distribution are conducive to the covert transmission of information but also make it difficult to acquire the WFRFT-CD3S signal. An acquisition method implemented in two steps is proposed to acquire the Doppler-shifted WFRFT-CD3S signal. First, a bidirectional correlation search method is proposed to align the received signal with the local spreading sequence in the time domain. The correlation peak attenuation caused by the Doppler frequency shift is eliminated by differential. Second, the accumulated phase angle caused by the Doppler frequency shift is obtained by the differential correlation value between the aligned received signal and the local spreading sequence. Then, the Doppler frequency shift is estimated by the accumulated phase angle. The detection probability, the false alarm probability, and the root mean square error (RMSE) of the Doppler shift estimation are analyzed theoretically and simulated. The theoretical results are verified by simulation. Theoretical results and simulations show that the detection probability is higher than 0.999, the false alarm probability is less than 0.001, and the RMSE of Doppler shift estimation is lower than 63 Hz when the signal-to-noise ratio is higher than -7.3 dB.

The case against alphabet awareness for detection of direct-sequence spread-spectrum signals

In this paper we investigate the detection of a direct-sequence spread-spectrum signal with QPSK coded chips, whose spreading sequence is unknown to the detector, and whose spreading code period is longer than the detector observation window. We propose a likelihood-ratio-test detector that takes advantage of knowledge of the signal alphabet, whose complexity grows linearly with the observation window length, and that makes use of multiple antennas. The proposed alphabet-aware detector outperforms multi-antenna alphabet-unaware detectors, especially when the SNR is high and the observation window is small. However, the performance advantage is not large, it diminishes further when the SNR is low, and it comes at the cost of higher computational complexity. In most cases, simpler alphabet-unaware detectors such as the energy detector provide comparable detection performance with less complexity. Due to the i.i.d. signal model this paper utilizes, the results in this work are also applicable to single-carrier unspread QPSK signals, with direct-sequence spread-spectrum signals as a specific case in which the SNR is low.

Turbo Iterative DSSS Acquisition in Satellite High-Mobility Communications

Satellite high-mobility communications can provide global communication services for mobile terminals, which plays a vital role in today's world of wireless communications. Different from traditional terrestrial-satellite communications, high-mobility terminals impose more challenges on the direct sequence spread spectrum (DSSS) signal acquisition in satellite communications. On the one hand, the spread spectrum sequence used in satellite communication systems is relatively long, which inevitably increases the computational complexity. On the other hand, high Doppler frequency offsets and time-varying Doppler rates caused by high-mobility terminals make satellite DSSS signal acquisition become the bottleneck. To solve such a problem in high-mobility satellite communications, we propose a new acquisition algorithm based on the new factor graph where time-varying Doppler rate is modeled by the random walk model, named the turbo iterative acquisition algorithm. It consists of the constraint-detection loop and the Doppler-estimation loop. By means of soft messages passed between these loops, the negative influence of time-varying Doppler frequency offsets can be eliminated effectively. Simulation results show that, compared with traditional algorithms, the proposed algorithm improves acquisition performance more than 1.3 dB with low computational complexity, which demonstrates the superiority of the proposed algorithm in terms of acquisition performance and complexity.

Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication

The Shannon-Nyquist sampling theorem that is based on narrowband interference (NBI) detection and parameter estimation methods in direct sequence spread spectrum (DSSS) communication is limited by the high sampling rate. Compressive sensing (CS) is adopted to address the problem. But it will change the signal nature, which leads to the unavailability of Shannon-Nyquist sampling theorem-based interference detection and parameter estimation methods. According to the different posterior probability distribution features of NBI, DSSS signal, and noise in compressed domain, a posterior probability model of whether NBI exists in the received signal is constructed by using the compressed measurements. The posterior probability that whether NBI exists in the received signal is employed as the feature parameters of NBI detection and parameter estimation. With the feature parameters detected, NBI detection can be achieved and the edge location of NBI components can be located. The relationship between the edge location of NBI components and the edge frequency of NBI is constructed, which will contribute to estimate the NBI edge frequency. The numerical simulation results demonstrate that the proposed method can effectively achieve NBI detection and parameter estimation in the compressed domain and it performs significantly better than the other existing methods.