Period Estimation of Spread Spectrum Codes Based on ResNet.

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.

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.

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.

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.

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.

An unsupervised adaptive signal processing method of principal components analysis (PCA) neural networks (NN) based on signal eigen-analysis is proposed to permit the eigenstructure analysis of lower signal to noise ratios (SNR) direct sequence spread spectrum (DS) signals. The objective of eigenstructure analysis is to estimate the pseudo noise (PN) of DS signals blindly. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is two periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Lastly, the PN sequence can be estimated by the principal eigenvector of autocorrelation matrix. Since the duration of temporal window is two periods of PN sequence, the PN sequence can be reconstructed by the first principal eigenvector only. Additionally, the eigen-analysis method becomes inefficient when the estimated PN sequence is long. We can use an unsupervised adaptive method of PCA NN to realize the PN sequence estimation from lower SNR input DS-SS signals effectively.

This paper investigates the application of spread spectrum code-division multiple access (SS-CDMA) techniques to subband image transmission over a bandlimited radio channel. For the transmission of images over SS-CDMA AWGN channels, a subband coding scheme that divides the image information into a number of independent data streams using an analysis filter bank, each of which is multiplied by its unique signature PN code, enables the transmission of these data streams via multiple parallel virtual channels created by their corresponding PN codes. With a sufficiently large number of streams, the total signal is able to fit within the narrow radio channel bandwidth even though the total bandwidth of all the signals may exceed the channel bandwidth. At the receiver, each received signal is separately recovered at the decoder by multiplying its PN code and integrating over the code length in order to obtain the desired subband. All the recovered subbands are then reassembled by a synthesis filter bank into a close reproduction to the original image. Additionally, for color subband image transmission, color images are first transferred to luminance (Y) and two chrominance components (I, Q). Each component is then decomposed independently into several subbands for SS-CDMA transmission. Therefore, a number of additional PN codes are required to support the transmission of the chrominance signals over the CDMA channels whereas the luminance signal was treated in the same manner as monochrome pictures. Moreover, SS-CDMA allows more than one image to be transmitted and be accessed simultaneously at the same limited channel bandwidth. If all the product signals for different images are transmitted over the SS-CDMA AWGN channel with perfect power control, the performance is the same as if the data were transmitted in a single stream. However, the quality of the farther received images would be degraded when the near-far problem occurs. The image quality of both the monochrome and color subband image transmission via SS-CDMA AWGN channels is evaluated for both perfect and imperfect power control.

In a low-earth orbit (LEO) satellite system the received signal is characterized by low signal to noise, it requires a minimum spreading processing gain of 30dB, i.e., the pseudonoise (PN) code length is 1023. However, the application specific integrated circuit (ASIC) based on digital matched-filter (DMF) for PN code acquisition on the market can't support such a long PN code length. It is necessary to use field programmable gate array (FPGA) to design the long PN code acquisition circuit based on DMF for the so high spreading processing gain. In this paper, the PN code acquisition circuit based on DMF is analyzed. A new approach to PN code acquisition based on DMF, which can obtain high spread processing gain (30dB) while the cost of the hardware is largely depressed, is presented. VHDL design, verification and FPGA realization of the DMF is accomplished, and main design results are also given.

The authors have developed a compact spread-spectrum (SS) wireless modem using a SAW (surface acoustic wave) convolver. They utilized the efficient ZnO-SiO/sub 2/-Si Sezawa-wave convolver. The SS modem was based on the DS/FSK (direct sequence/frequency shift keying) method, where the PN (pseudonoise) code length was 127 chips and the chip rate was 14 MHz. The demodulation method of the SS modem was a perfectly asynchronous one, which was useful for its application to wireless communication under a very weak signal. The total number of channels of the DS/FS method was confirmed to be more than 200000 within a 28-MHz SS band. Under the 1989 transmitted-power limits in Japan (0.05 mu W/120 kHz:500 mu V/m at 3 m in the frequency range below 322 MHz), the SS modem proved to be reliable over a distance greater than 110 m. Furthermore, the asynchronous SS modem of 1-W transmission power with a 900-MHz SS band in the US proved to be reliable over a distance as great as 8 km. >

An analysis is presented of the performance of voice amplitude modulation (AM) and frequency modulation (FM) communication systems in the presence of direct-sequence (DS) spread spectrum (SS) signal. The effect of an interfering DS-SS signal on voice AM and FM receivers decreases by decreasing the DS signal power and/or its pseudo-noise (PN) code pulse duration (T c ).

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.

Spread spectrum systems are being widely deployed today and are becoming more prevalent as most next-generation wireless systems are adopting it for their common air interface. These systems include the digital cellular IS-95 A/B/C, IEEE 802.11 wireless local area networks, as well as third-generation wideband code-division multiple access systems. In spread-spectrum systems, the receiver must synchronize on to the transmitted pseudo-noise (PN) code to obtain the improvement performance achieved through spreading. Since PN acquisition must process the spread-spectrum signal at a speed much faster than the transmitted data rate, its energy consumption can become significant and should be minimized for portable applications. Typically, either matched filters or serial correlators are used to acquire the PN code timing. This paper describes a hybrid PN acquisition architecture which employs both matched filters and serial correlators to achieve a lower energy consumption and fast acquisition time as compared to the traditional approaches of using either matched filters or serial correlators alone. The hybrid architecture has been implemented in RTL VHDL and synthesized down to gate level in a 0.5 micron CMOS library. Synthesis results show a factor of four reduction in energy for the hybrid scheme as compared to the matched filters architecture and a factor of two reduction in energy as compared to the serial architecture.

Low Density Parity Check (LDPC) codes are Shannon-Limit approaching codes. Shannon stated the theoretical bounds for digital data transmission in a given channel capacity with error correction coding. Spread Spectrum also called as CDMA (Code Division Multiple Access) is an efficient way of radio wave communication deployed in commercial broadcasts, ham radios, military communications, police radios, etc. CDMA is a means of signal transmission in which the signal frequency is spread over a very wide bandwidth. Spreading of data is achieved prior to transmission with the help of an independent code called ‘Pseudo Noise Code’ (PN sequences), which is subsequently used in the receiver end to de-spread the signal received in order to recover the signal data. PN sequences based on prime numbers like Legendre & Weil sequences are used in the present work. An impressive part of LDPC lies in its decoding algorithm. Sum-Product Algorithm (SPA) decoding offers the best error performance amongst all the types of decoding techniques of LDPC codes and thus applied here. This paper demonstrates an idea of introducing LDPC codes along with DS-Spread Spectrum technique using Legendre & Weil sequences. In the present work, the encoding and decoding algorithms are applied after spreading of the signal. SPA decoding technique is successfully employed and simulated results are plotted taking an example design to illustrate the concepts discussed above.

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.

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.