In this paper, a spectrum efficient spatial polarized quadrature amplitude modulation (SPQM) scheme for physical layer security in dual-polarized satellite systems is proposed, which uses the carrier's polarization state, amplitude, phase and the polarization characteristics of the transmitting beams as information bearing parameters, which can improve the transmission efficiency and enhance the transmission security at the same time. As we know, the depolarization effect is the main drawback that affects the symbol error rate performance when polarization states are used to carry information. To solve the problem, we exploit an additional degree of freedom, time, in the proposed scheme, which means that two components of the polarized signal are transmitted in turn in two symbol periods, thus they can be recovered without mutual interference. Furthermore, orthogonal polarizations of the transmitting beam are used as spatial modulation for further increasing the throughput. In addition, in order to improve the transmission security, two transmitting beams are designed to transmit the two components of the polarized signal respectively. In this way, a secure transmission link is formed from the transmitter to the receiver to prevent eavesdropping. Finally, superiorities of SPQM are validated by the theoretical analysis and simulation results in dual-polarized satellite systems.

This paper proposes a matched myriad filter based detector for MSK signal under symmetric alpha-stable (SαS) noise. As shown in the previous literatures, SαS distribution is more accurate to characterize the atmospheric noise, which is the main interference in VLF communication. MSK modulation is widely used in VLF communication for its high spectral efficiency and constant envelope properties. However, the optimal detector for MSK under SαS noise is rarely reported due to its memory modulation characteristic. As MSK signal can be viewed as a sinusoidal pulse weighted offset QPSK (OQPSK), a matched myriad filter is proposed to derive a near-optimal detection performance for the in-phase and quadrature components, respectively. Simulations for MSK demodulation under SαS noise with different α validate the effectiveness of the proposed method.

Excitation of Extremely Low Frequency (ELF)/Very Low Frequency (VLF) from ionosphere,which is artificial modulated by High Frequency (HF) waves can provide a way of antenna generation for deep submarine communication. In this paper, based on plasma energy conservation equation, the theoretical model of amplitude modulation HF pump heating low ionosphere for ELF/VLF generation is established. The linear frequency modulation technique of up-chirp and down-chirp have good self-correlation and cross-correlation, by which information can be transmitted by up-chirp and down-chirp. Thus, the linear frequency modulation technique can be applied to the ionosphere ELF/VLF communication. Based on this, a Chirp-BOK (Binary Orthogonal Keying) communication scheme is proposed. Indeed the Chirp-BOK amplitude and power modulation function are designed by combining the linear frequency modulation technique with the square wave amplitude modulation technique. The simulation results show in the condition that the ionosphere is heated by the Chirp-BOK power modulation HF waves, the temperature of ionospheric electronic and the variations of conductivity have obvious frequency modulation characteristics which are the same as that of power modulation, so does the variation of ionospheric current. Thus, when the ionosphere is heated by Chirp-BOK power modulation HF waves, the up-chirp (symbol ‘0’) and down-chirp (symbol ‘1’) ELF/VLF signals can be generated.

In this letter, a novel mainlobe anti-jamming method via eigen-projection processing and covariance matrix reconstruction is proposed. The present work mainly focuses on two aspects: the first aspect is to obtain the eigenvector of the mainlobe interference accurately in order to form the eigen-projection matrix to suppress the mainlobe interference. The second aspect is to reconstruct the covariance matrix which is uesd to calculate the adaptive weight vector for forming an ideal beam pattern. Additionally, the self-null effect caused by the signal of interest and the sidelobe interferences elimination are also considered in the proposed method. Theoretical analysis and simulation results demonstrate that the proposed method can suppress the mainlobe interference effectively and achieve a superior performance.

The dyadic filter bank property of multivariate empirical mode decomposition (MEMD) for white Gaussian noise (WGN) is well established. In order to investigate the way MEMD behaves in the presence of fractional Gaussian noise (fGn), we conduct thorough numerical experiments for MEMD for fGn inputs. It turns out that similar to WGN, MEMD follows dyadic filter bank structure for fGn inputs, which is more stable than empirical mode decomposition (EMD) regardless of the Hurst exponent. Moreover, the estimation of the Hurst exponent of fGn contaminated with different kinds of signals is also presented via MEMD in this work.

In this letter, a method of wideband direction of arrival (DOA) estimation based on co-prime arrays with sub-Nyquist sampling is proposed. Previous works have employed co-prime arrays for wideband DOA estimation, which can increase the degrees of freedom (DOFs) in the spatial domain. However, they are all based on Nyquist sampling. Different from existing methods, we incorporate a sub-Nyquist sampling scheme called multicoset sampling for DOA estimation to relax hardware condition. Simulation results show the correctness and effectiveness.

Memory-efficient Internet Protocol (IP) lookup with high speed is essential to achieve link-speed packet forwarding in IP routers. The rapid growth of Internet traffic and the development of optical link technologies have made IP lookup a major performance bottleneck in core routers. In this paper, we propose a new IP route lookup architecture based on hardware called Prefix-Route Trie (PR-Trie), which supports both IPv4 and IPv6 addresses. In PR-Trie, we develop a novel structure called Overlapping Hybrid Trie (OHT) to perform fast longest-prefix-matching (LPM) based on Multibit-Trie (MT), and a hash-based level matching query used to achieve only one off-chip memory access per lookup. In addition, the proposed PR-Trie also supports fast incremental updates. Since the memory complexity in MT-based IP lookup schemes depends on the level-partitioning solution and the data structure used, we develop an optimization algorithm called Bitmap-based Prefix Partitioning Optimization (BP2O). The proposed BP2O is based on a heuristic search using Ant Colony Optimization (ACO) algorithms to optimize memory efficiency. Experimental results using real-life routing tables prove that our proposal has superior memory efficiency. Theoretical performance analyses show that PR-Trie outperforms the classical Trie-based IP lookup algorithms.

Low Probability of Intercept (LPI) radar waveform has complex and diverse modulation schemes, which cannot be easily identified by the traditional methods. The research on intrapulse modulation LPI radar waveform recognition has received increasing attention. In this paper, we propose an automatic LPI radar waveform recognition algorithm that uses a multi-resolution fusion convolutional neural network. First, signals embedded within the noise are processed using Choi-William Distribution (CWD) to obtain time-frequency feature images. Then, the images are resized by interpolation and sent to the proposed network for training and identification. The network takes a dual-channel CNN structure to obtain features at different resolutions and makes features fusion by using the concatenation and Inception module. Extensive simulations are carried out on twelve types of LPI radar waveforms, including BPSK, Costas, Frank, LFM, P1~P4, and T1~T4, corrupted with additive white Gaussian noise of SNR from 10dB to -8dB. The results show that the overall recognition rate of the proposed algorithm reaches 95.1% when the SNR is -6dB. We also try various sample selection methods related to the recognition task of the system. The conclusion is that reducing the samples with SNR above 2dB or below -8dB can effectively improve the training speed of the network while maintaining recognition accuracy.

Parameter estimation theorems for LFM signals have been developed due to the advantages of fractional Fourier transform (FrFT). The traditional estimation methods in the fractional Fourier domain (FrFD) are almost based on two-dimensional search which have the contradiction between estimation performance and complexity. In order to solve this problem, we introduce the orthogonal matching pursuit (OMP) into the FrFD, propose a modified optimization method to estimate initial frequency and final frequency of fractional bandlimited LFM signals. In this algorithm, the differentiation fractional spectrum which is used to form observation matrix in OMP is derived from the spectrum analytical formulations of the LFM signal, and then, based on that the LFM signal has approximate rectangular spectrum in the FrFD and the correlation between the LFM signal and observation matrix yields a maximal value at the edge of the spectrum (see Sect.3.3 for details), the edge spectrum information can be extracted by OMP. Finally, the estimations of initial frequency and final frequency are obtained through multiplying the edge information by the sampling frequency resolution. The proposed method avoids reconstruction and the traditional peak-searching procedure, and the iterations are needed only twice. Thus, the computational complexity is much lower than that of the existing methods. Meanwhile, Since the vectors at the initial frequency and final frequency points both have larger modulus, so that the estimations are closer to the actual values, better normalized root mean squared error (NRMSE) performance can be achieved. Both theoretical analysis and simulation results demonstrate that the proposed algorithm bears a relatively low complexity and its estimation precision is higher than search-based and reconstruction-based algorithms.

In this letter, a new scheme for multirate coprime sampling and reconstructing of sparse multiband signals with very high carrier frequencies is proposed, where the locations of the signal bands are not known a priori. Simulation results show that the new scheme can simultaneously reduce both the number of sampling channels and the sampling rate for perfect reconstruction, compared to the existing schemes requiring high number of sampling channels or high sampling rate.

In this paper, we propose a compressed sensing scheme using sparse-graph codes and peeling decoder (SGPD). By using a mix method for construction of sensing matrices proposed by Pawar and Ramchandran, it generates local sensing matrices and implements sensing and signal recovery in an adaptive manner. Then, we show how to optimize the construction of local sensing matrices using the theory of sparse-graph codes. Like the existing compressed sensing schemes based on sparse-graph codes with “good” degree profile, SGPD requires only O(k) measurements to recover a k-sparse signal of dimension n in the noiseless setting. In the presence of noise, SGPD performs better than the existing compressed sensing schemes based on sparse-graph codes, still with a similar implementation cost. Furthermore, the average variable node degree for sensing matrices is empirically minimized for SGPD among various existing CS schemes, which can reduce the sensing computational complexity.

An improved multivariate wavelet denoising algorithm combined with subspace and principal component analysis is presented in this paper. The key element is deriving an optimal orthogonal matrix that can project the multivariate observation signal to a signal subspace from observation space. Univariate wavelet shrinkage operator is then applied to the projected signals channel-wise resulting in the improvement of the output SNR. Finally, principal component analysis is performed on the denoised signal in the observation space to further improve the denoising performance. Experimental results based on synthesized and real world ECG data verify the effectiveness of the proposed algorithm.

In this letter, a novel transmission scheme is proposed to eliminate the polarization dependent loss (PDL) effect in dual-polarized satellite systems. In fact, the PDL effect is the key problem that limits the performance of the systems based on the PM technique, while it is naturally eliminated in the proposed scheme since we transmit the two components of the polarized signal in turn in two symbol periods. Moreover, a simple and effective detection method based on the signal's power is proposed to distinguish the polarization characteristic of the transmit antenna. In addition, there is no requirement on the channel state information at the transmitter, which is popular in satellite systems. Finally, superiorities are validated by the theoretical analysis and simulation results in the dual-polarized satellite systems.

This paper presents a novel MEMD interval thresholding denoising, where relevant modes are selected by the similarity measure between the probability density functions of the input and that of each mode. Simulation and measured EEG data processing results show that the proposed scheme achieves better performance than other traditional denoisings.

In this paper, a dual-polarized phased array based polarization state modulation method is proposed to enhance the physical-layer security in millimeter-wave (mm-wave) communication systems. Indeed, we utilize two polarized beams to transmit the two components of the polarized signal, respectively. By randomly selecting the transmitting antennas, both the amplitude and the phase of two beams vary randomly in undesired directions, which lead to the PM constellation structure distortion in side lobes, thus the transmission security is enhanced since the symbol error rate increases at the eavesdropper side. To enhance the security performance when the eavesdropper is close to the legitimate receiver and located in main beam, the artificial noise based on the orthogonal vector approach is inserted randomly between two polarized beams, which can further distort the constellation structure in undesired directions and improve the secrecy capacity in main beam as well. Finally, theoretical analysis and simulation results demonstrate the proposed method can improve the transmission security in mm-wave communication systems.

This paper proposes an effective continuous super-resolution (CSR) algorithm for the multipath channel estimation. By designing a preamble including up-chirp and down-chirp symbols, the Doppler shift and multipath delay are estimated jointly by using convex programming. Simulation results show that the proposed CSR can achieve better detection probability of the number of multipaths than the eigenvalue based methods. Moreover, compared with conventional super-resolution techniques, such as MUSIC and ESPRIT methods, the proposed CSR algorithm demonstrates its advantage in root mean square error of the Doppler shift and multipath delay, especially for the closely located paths within low SNR.

This paper proposes a chirp-BOK modulation scheme for VLF (Very low frequency, 3-30kHz) communication under symmetric alpha-stable (SαS) noise. The atmospheric noise which is the main interference in VLF communication is more accurately characterized as SαS distribution in the previous literatures. Chirp-BOK, one of the chirp spread spectrum (CSS) technologies is widely used for its anti-interference performance and constant envelope properties. However, up-chirp and down-chirp are not strictly orthogonal, the bit error rate (BER) performance of chirp-BOK system is no longer improved with the increase of time-bandwidth product. So in this paper, the influence of non-orthogonal modulation waveform on the system is considered, and the model of the optimal parameters for chirp-BOK is derived from the perspective of minimum BER under gaussian noise and SαS noise respectively. Simulations for chirp-BOK scheme under gaussian noise and SαS noise with different α validate the effectiveness of the proposed method.

As known to us all, L-shaped co-prime array (LCA) is a recently introduced two-dimensional (2-D) sparse array structure, which is extended from linear co-prime array (CA). Such sparse array geometry can be used for 2-D parameters estimation with higher degrees-of-freedom (DOF). However, in the scenario where several narrowband transmissions spread over a wide spectrum, existing technique based on LCA with Nyquist sampling may encounter a bottleneck for both analog and digital processing. To alleviate the burden of high-rate Nyquist sampling, a method of joint wideband spectrum and direction-of-arrival (DOA) estimation with compressed sampling based on LCA, which is recognized as LCA-based modulated wideband converter (MWC), is presented in this work. First, the received signal along each antenna is mixed to basebands, low-pass filtered and down-sampled to get the compressed sampling data. Then by constructing the virtual received data of 2-D difference coarray, we estimate the wideband spectrum and DOA jointly using two recovery methods where the first is a joint ESPRIT method and the other is a joint CS method. Numerical simulations illustrate the validity of the proposed LCA based MWC system and show the superiority.