Higher Order Cumulants Research Articles

A joint STFT-HOC detection method for FH data link signals

The anti-interference, anti-interception, anti-spoofing ability and complex electromagnetic environment of the data link signal of the frequency hopping system pose formidable challenges to the reconnaissance of the frequency hopping signal. Therefore, this paper proposes a joint STFT-HOC detection method for FH data link signals. This method combines time-frequency analysis and high-order cumulant to realize effective detection of frequency hopping data link signals. Theoretical analysis and simulation experiments show that the proposed method can successfully detect the data link signal of the frequency hopping system when the signal-to-noise ratio is greater than −2 dB.

Multiple High-Order Cumulants-Based Spectrum Sensing in Full-Duplex-Enabled Cognitive IoT Networks

With unprecedented progress on Internet of Things (IoT), spectrum scarcity becomes even severe with the explosive growth of wireless smart devices. To deal with spectrum scarcity issues, cognitive radio (CR)-enabled IoT has been emerged as a promising solution, which allows IoT devices reusing the underutilized spectrum bands. In this article, we investigate spectrum sensing in CR-IoT, in which full-duplex CR-IoT node can perform spectrum sensing and data transmission concurrently for reducing sensing delay. Two sensing methods are proposed based on multiple high-order cumulants for excavating rich information of the non-Gaussian transmitted signals. Specifically, for the scenarios with a single sensing antenna, we first propose a multiple high-order cumulants-based sensing method (MCS) derived from the likelihood ratio test, which is assumed to be near optimum. The test statistics are derived, respectively, in two cases, i.e., the case only performing sensing and the case performing sensing and transmission simultaneously. Interestingly, the derived two test statistics have same expression, while the corresponding sensing thresholds are different from each other. For the scenarios with multiple sensing antennas, we propose a multiantenna-assisted multiple high-order cumulants-based sensing method (MMCS), which can provide a tradeoff between the computational complexity and sensing performance. We conduct the hypothesis test with Hotelling's T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -statistic and derive the corresponding sensing threshold. Theoretical performance evaluated by detection probability and computational complexity of the proposed methods are analyzed. Additionally, extensive simulations are provided, which show both the proposed methods can counter the adverse effects of noise uncertainty, and MCS has superiority over MMCS in terms of sensing accuracy.

Algorithms for Modulation Recognition of Narrowband Power Line Carrier Communication Signals

Automatic identification of modulated signals is an important technology of power line communication. Aiming at the problems of low signal recognition rate and characteristic parameter extraction in power line communication channel, this paper uses amplitude variance value of wavelet transform and higher order cumulant as the identification parameter, and designs a signal recognizer based on improved support vector machine. Under the condition of power line channel environment, the recognizer of this paper is less than the existing recognition method in the computational complexity, and has good robustness to power line noise. At the same time, it avoids the shortcomings of traditional neural network such as under-learning and over-learning. The simulation results show that when the SNR is 5 dB, power line communication signals through the recognizer, which the correct identification rate can reach 91%.

Multi-Trek Separation in Linear Structural Equation Models

Building on the theory of causal discovery from observational data, we study interactions between multiple (sets of) random variables in a linear structural equation model with non-Gaussian error terms. We give a correspondence between structure in the higher-order cumulants and combinatorial structure in the causal graph. It has previously been shown that low rank of the covariance matrix corresponds to trek separation in the graph. Generalizing this criterion to multiple sets of vertices, we characterize when determinants of subtensors of the higher-order cumulant tensors vanish. This criterion applies when hidden variables are present as well. For instance, it allows us to identify the presence of a hidden common cause of $k$ of the observed variables.

The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm

With the commercialization of 5G, in order to recognize QAM signals, one of the main modulation modes in 5G communication systems, this paper put forward the BP-BSA network model based on bird swarm algorithm (BSA) and BP neural network. Firstly, the instantaneous features and high-order cumulants were selected as the appropriate feature statistics by analyzing the characteristics of MQAM signals. After that, the structure of the BP neural network model was determined, and the initial parameters of the BP neural network were optimized using BSA to accelerate the convergence speed of the network. Finally, the features processed with signal to noise ratio (SNR) disorder were used to train and test the BP neural network model. The BP-BSA network model proposed in this paper applies the bird swarm algorithm to the field of modulation recognition for the first time. And the simulation results show that the recognition accuracies of 16, 32, 64, 128, 256QAM signals in the SNR range of -5 dB to 20 dB all reach above 98%. Compared with the same type algorithms, the algorithm proposed in this paper has good recognition performance.

DOA Estimation With Nonuniform Moving Sampling Scheme Based on a Moving Platform

The generalized linear moving sampling scheme (MSS) exploiting the second-order statistics and also the high-order cumulants is studied, where the set of MSS is defined as the shifted distance offsets involved in estimation based on a moving platform. Then, sparse physical arrays (SPAs) with nonuniform linear moving sampling schemes (NL-MSS), referred to as SPA-NL-MSS, are proposed to optimize the consecutive difference co-arrays. For the same number of sensors and data samples, better performance in terms of both the number of degrees of freedom (DOFs) and estimation accuracy can be achieved by SPA-NL-MSS than existing array structures exploiting array motions at the second order level.

Research on Adaptive Threshold of Received Signal in Communication System

When the light beam propagates in the atmosphere, the signal will be absorbed and scattered by the gas molecules and water mist in the atmosphere, which will cause the loss of power rate. The complex atmospheric environment will produce a variety of adverse effects on the signal. The interference produced by these effects overlaps with each other, which will seriously affect the strength of the received signal. Therefore, how to effectively suppress the atmospheric turbulence effect in the random atmospheric turbulence channel, ensure the normal transmission of the signal in the atmospheric channel, and reduce the bit error rate of the communication system, is very necessary to improve the communication system. When processing the received signal, it is an important step to detect the transmitted signal by comparing the received signal with the threshold. In this paper, based on the atmospheric turbulence distribution model, the adaptive signal decision threshold is obtained through the estimation of high-order cumulant. Monte Carlo method is used to verify the performance of adaptive threshold detection. The simulation results show that the high-order cumulant estimation of atmospheric turbulence parameters can realize the adaptive change of the decision threshold with the channel condition. It is shown that the adaptive threshold detection can effectively restrain atmospheric turbulence, improve the performance of free space optical and improve the communication quality.

A blind classification method of adaptive coding and modulation signals based on cumulants

This paper proposes a blind modulation classification method for adaptive coding and modulation signals base on the difference in high-order cumulants of different modulation types. Use high-order cumulants to establish features to design modulation classification algorithm and decision thresholds. Because of the time-varying characteristics of adaptive coding and modulation signals, the segmentation classification method is used to realize the modulation classification of each frame in the time dimension. Finally, the designed algorithm is simulated, and the classification performance of the algorithm under different modulation types, segment lengths and signal-to-noise ratios is analyzed.

A New Modulation Recognition Method Based on Wavelet Transform and High-order Cumulants

In order to improve the recognition performance of digital modulated signals under low signal-to-noise ratio, this paper proposes a new method that combines high-order cumulants and wavelet transform to extract transient features, and the recognition of six kinds of modulation signals is realized through the neural network classifier. Simulation results show that the proposed algorithm can achieve good performance under low signal-to-noise ratio. In addition, only three key features are selected in this method, which greatly reduces the complexity of the algorithm. Furthermore, the effects of sample rate, carrier frequency and symbol rate on recognition performance are analyzed and simulated for the sake of better recognition performance.

Recognition Analysis and Simulation Implementation Based on High-Order Cumulants of Wireless Digital Modulation Mode

This paper mainly studies the data characteristics of high order cumulants using digitally modulated signals, and constructs the identification feature parameters that can distinguish the signal modulation type by the high-order cumulants data of the digital modulation signal. Set the identification signal modulation type determination threshold based on the value of the identification feature parameter. The identification feature parameter value of the signal modulation type is compared with the set determination threshold, to realize the recognition of digital modulation signal. This identification method is implemented based on MATLAB design, with a 2ASK (2-ary Amplitude Shift Keying) signal, 4ASK (4-ary Amplitude Shift Keying) signal, 2PSK (2-ary Phase Shift Keying) signal, 4PSK (4-ary Phase Shift Keying) signal, 2FSK (2-ary Frequency Shift Keying) signal, 4FSK (4-ary Frequency Shift Keying) signal. The second, fourth and sixth order cumulants of the six signals were analyzed. Calculate the selected identification feature parameter value and the determination threshold to identify the six signals. The six signals have made MATLAB identification simulation. Simulation results show that this method is feasible and has high recognition rate. Simulation results verify that such recognition methods maintain a high recognition rate under conditions with low signal-to-noise ratio. This identification method can be extended to more MASK (M-ary Amplitude Shift Keying), MPSK (M-ary Phase Shift Keying), MFSK (M-ary Frequency Shift Keying), MQAM (M-ary Quadrature Amplitude Modulation) signal identification.

Hybrid Quark–Hadron Equation of State for Multi-messenger Astronomy

We present the recent progress of the hybrid quark-meson-nucleon model for multi-messenger astronomy that unifies the thermodynamics of quark and hadronic degrees of freedom. The obtained equation of state is in accordance with nuclear matter properties at saturation density and with the flow constraint from heavy ion collision experiments. The mass-radius relations and tidal deformabilities for compact stars are calculated and compared with the latest astrophysical observations. The phase diagram of the isospin-symmetirc matter is studied as well in terms of the higher-order cumulants of the net-baryon number.

A Modulation Recognition Algorithm via Hybrid Feature Analysis in Aeronautical Wireless Channel

When communicating in aeronautical wireless channels, the difficulty of radio modulation recognition increases due to the loss of information caused by noise; particularly in circumstances with low signal-to-noise ratios (SNRs), it is difficult to achieve recognition rates exceeding 90.0%. To improve the radio modulation recognition performances of networks at low SNRs in complex electromagnetic environments, a modulation recognition method based on multidimensional feature analysis is proposed in this paper. It is realized through a cascaded structure including a Deep Cross Network (DCN) and an improved Visual Geometry Group Network 16 (VGG16). Our network framework is divided into two modules. In the one-dimensional data analysis module, we take the high-order cumulant of a transmitted signal as the one-dimensional feature input of the DCN. In the two-dimensional data analysis module, the color constellation density of the signal is extracted as the feature map input of the improved VGG16. Finally, we build a cascaded neural network with hybrid feature inputs for modulation recognition. Experimental results show that the recognition rate of our method is higher than 90.0% at an SNR of -4 dB. Compared with other methods, the proposed method has better recognition performance at low SNRs in aeronautical wireless channels.

A Modulation Recognition Algorithm Based on Wavelet Transform Entropy and High-order Cumulant for Satellite Signal Modulation

调制识别是信号检测与解调的关键环节，针对卫星调制中采用的MAPSK，MQAM，MFSK，MPSK方式，提出了一种计算小波变换熵值并结合高阶累积量的联合调制识别算法.根据小波变换对时频信息敏感的特点，不同调制方式高阶累积量计算结果的区分性以及不同复杂度的调制信号熵值结果不同，分析了以上4类调制信号的计算结果，提出了基于小波变换熵值及高阶累积量联合的卫星信号调制识别算法.计算调制信号小波系数，据此计算熵值，实现对调制信号的类别划分，使用高阶累积量实现类别内的信号分类.经过仿真分析，可实现在8dB以上达到0.9识别率的效果，该方法对高阶（64阶调制）信号识别具有一定借鉴意义.

Characterization of pitting resistance of metal materials with ultrasonic microscope

Pitting resistance of metal materials is usually evaluated by immersion experiments or electrochemical experiments, etc. However, these methods are destructive and easily affected by experimental conditions. In this paper, a method based on the ultrasonic relative attenuation coefficient of high-order cumulant (URA_HOC) is proposed to characterize the pitting resistance of metal materials. The new method can reduce the influence of Gaussian noise and is sensitive to microstructure changes, which enables the new method to evaluate the pitting resistance accurately. In order to verify the effectiveness of proposed method,duplex stainless steel 2205 with different compositions are detected by ultrasonic microscope and electrochemical experiment, respectively. The results shown that the prediction accuracy of URA_HOC on the pitting resistanceis up to 94%. Therefore, the new method canachieve non-destructive, high-efficient and large-scale measurement of pitting resistance.Besides, the new method also provides arapid characterization of the microstructure of materials.

Momentum-dependent flow fluctuations as a hydrodynamic response to initial geometry

We propose a redefinition of the principal component analysis (PCA) of anisotropic flow that makes it more directly connected to fluctuations of the initial geometry of the system. Then, using state-of-the-art hydrodynamic simulations, we make an explicit connection between flow fluctuations and a cumulant expansion of the initial transverse geometry. In particular, we show that the second principal component of elliptic flow is generated by higher-order cumulants, and therefore probes smaller length scales of the initial state. With this information, it will be possible to put new constraints on properties of the early-time dynamics of a heavy-ion collision, including small-scale structure, as well as properties of the quark-gluon plasma.

Measurement of the Sixth-Order Cumulant of Net-Proton Distributions in Au+Au Collisions from the STAR Experiment

Higher-order cumulants of the net-baryon multiplicity distributions are predicted to be sensitive to the properties of the nuclear matter created in high-energy nuclear collisions. In this talk, we present the collision centrality and acceptance (rapidity and transverse momentum) dependence of the ratio of the 6th- to the 2nd-order cumulant ratio (C6/C2) of net-proton in Au+Au collisions at sNN=54.4and200GeV measured by the STAR detector at RHIC. The new results are compared to hadron transport model and lattice QCD calculations.

A New Concept of Fractional Order Cumulant and It-Based Signal Processing in α and/or Gaussian Noise

In this article, the concept and definitions of the Fractional Order Moment (FOM) and Fractional Order Cumulant (FOC) are proposed, which is based on the fractional derivative of the fractional order Moment-generating function and the fractional order Cumulant-generating function of stochastic processes. The moment and cumulant are defined on an expanded set from positive integer to the whole positive real. This development not only provides a new technology for signal processing, also complements the existing theory in the field. The properties of the FOC have been derived, and their uniformity and particularity with the High Order Cumulant are compared and commented. In addition, the transformation between the FOM and the FOC are derived and discussed in detail. As one of the applications of the new concept to the α and Gaussian processes, a new method of suppressing α and Gaussian noise is proposed. Furthermore, a FOC-based parameter estimation algorithm is developed for the non-minimum phase ARMA processes in α and/or Gaussian noise. Simulation examples are used to demonstrate the effectiveness of the proposed parameter estimation algorithm.

Practical AMC model based on SAE with various optimisation methods under different noise environments

Automatic modulation classification (AMC) has recently attracted widespread attention nowadays due to its desirable features of generalisability and requirement of little prior knowledge through artificial intelligence (AI) technology. The authors propose a stacked auto-encoder (SAE) based on various optimisation methods structure to intelligently process a feature space that includes spectral-based features and high-order cumulants. To unify the dimensionality of the features, they apply different normalisation methods to the feature space before training the SAE model to decide corresponding normalisations under different noise environments. Linear normalisation is superior when signal-to-noise ratio (SNR) is low, and standardisation is superior when SNR is between -1 and 4 dB. Regularisation works best when SNR is greater than 5 dB. To increase the recognition accuracy of the proposed model, they introduce the unconstrained optimisation theory to adjust the proposed SAE model, including Nelder-Mead method, Newton optimisation method, conjugate gradient method and quasi-Newton method. They observe that the quasi-Newton method offers desirable performance when optimising SAE model. It is the first time to compare these data normalisation methods and discuss unconstrained optimisation theory together to recognise modulation types. The recognition accuracy of this model for eight modulation types can reach 99.8% when SNR ranges from − 5 to 10 dB.

Extensive evaluation of the MIMO along with sliced multi modulus algorithm and estimate strategies in the direction of MIMO OFDM solutions

A MIMO usage multiple antennas at both sending and receiving ends. These antennas communicate with each various other on the exact same frequency band and also help in linearly raising the channel ability. Because of the multi paths cordless networks face the issue of network fading which cause Inter Sign Interference. Amongst several formulas to blind deconvolution of a multiple-input multiple-output systems, the one that utilizes higher-order cumulants has benefits in pertains to of convergence price. Influenced on this algorithm and on a stochastic gradient technique, we recommend a formula with capability of recovering simultaneously all resources. The crucial feature of this particular DFE is actually a linear-complexity slim stations estimator, which possesses the capacity to see as well as refuse instincts located upon pair of audio variations: infected Gaussian as well as symmetrical alpha constant.

Cumulants of Net-charge Distribution from Particle–Antiparticle Sources

It is shown how high-order cumulants of net-charge distribution in hadronic collisions at LHC energies can be expressed via lower-order terms under the assumption that particle-antiparticle pairs are produced in independent local processes. It is argued and tested with HIJING model that this assumption is typically valid for net-proton fluctuations in the case when no critical behaviour is present in the system. Values estimated in such a way can be considered as baselines for direct measurements of high-order net-charge fluctuations in real data.