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Abstract Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless communication networks. However, the great increase in users and antennas brings challenges for interference suppression and resource allocation for mmWave massive MIMO-NOMA systems. This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping and allocation of subchannel and power. First, an enhanced K-means grouping algorithm is proposed to reduce the multi-user interference and accelerate the convergence. Then, a dueling deep Q-network (DQN) structure is proposed to perform subchannel allocation, which further improves the convergence speed. Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system’s achievable sum-rate. The simulation results demonstrate that our proposed scheme outperforms other neural network-based algorithms in terms of convergence performance, and can achieve higher system capacity compared with the greedy algorithm, the random algorithm, the RNN algorithm, and the DoubleDQN algorithm.

Abstract Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems allow for a data transmission rate of gigabits per second owing to the large bandwidth available in the mmWave spectrum and the antenna gains provided by the massive MIMO system. However, hybrid precoding with high complexity and low spectral efficiency cannot address the challenge of high cost and power consumption of RF chains of multi-user systems. In this paper, we propose a low-complexity hybrid precoding scheme for downlink multi-antenna multi-user mmWave massive MIMO systems, aiming to enhance the sum spectral efficiency (SSE) performance. We first extend the dimension of the analog precoding matrix into a square matrix and find the optimal analog combiner by selecting some of the discrete Fourier transform (DFT) bases, which enhances the equivalent baseband channel matrix gain. Then, we directly aggregate the channel gain through the equal gain transmission (EGT) method to ensure the frequency efficiency performance. Finally, we propose an improved BD scheme to design the digital precoder and combiner to reduce the inter-user interference. We consider both the mmWave channel and the Rayleigh channel to evaluate the performance of the proposed algorithm. The simulation results verify that the proposed scheme enjoys near-optimal achievable sum spectrum efficiency and BER performance in both the mmWave channel and Rayleigh channel and performs even better in Rayleigh channel than in the mmWave channel.

To realize signal classification in impulsive noise environment， a modulation recognition scheme based on polished cyclic correntropy spectrum and shallow residual network was proposed.The proposed scheme not only has low computational complexity but also shows robustness to impulsive noise.Simulation results demonstrate the proposed solution’s superior performance under impulsive noise， even when the generalized signal-to-noise ratio is very low.

Direction of arrival (DOA) estimation is an important research topic in array signal processing and widely applied in practical engineering. However, when signal sources are highly correlated or coherent, conventional subspace-based DOA estimation algorithms will perform poorly due to the rank deficiency in the received data covariance matrix. Moreover, conventional DOA estimation algorithms are usually developed under Gaussian-distributed background noise, which will deteriorate significantly in impulsive noise environments. In this paper, a novel method is presented to estimate the DOA of coherent signals in impulsive noise environments. A novel correntropy-based generalized covariance (CEGC) operator is defined and proof of boundedness is given to ensure the effectiveness of the proposed method in impulsive noise environments. Furthermore, an improved Toeplitz approximation method combined CEGC operator is proposed to estimate the DOA of coherent sources. Compared to other existing algorithms, the proposed method can avoid array aperture loss and perform more effectively, even in cases of intense impulsive noise and low snapshot numbers. Finally, comprehensive Monte-Carlo simulations are performed to verify the superiority of the proposed method under various impulsive noise conditions.

Direction of arrival (DOA) estimation, as the main technology of passive radio monitoring and positioning, has been deeply investigated. However, the DOA for distributed sources is challenging to estimate in environments with impulsive noise. Although many methods have been proposed for DOA estimation, most of them assume that array output signals contain Gaussian noise. Therefore, the performance of these methods is often poor for alpha-stable distributed impulsive noise. Furthermore, subspace-based DOA estimation methods for distributed sources require a two-dimensional (2D) peak search, which increases the consumption of system computing resources. In this paper, a Q-function-based kernel function is proposed, and its properties are derived. On this basis, a novel DOA estimation method is proposed for coherently distributed (CD) sources in impulsive noise. To reduce computational complexity, a Lagrangian quadratic optimization function is derived by approximating the generalized array manifold of the CD source. By solving this optimization function, a 2D peak search can be reduced to several one-dimensional (1D) peak searches. The simulation results illustrate that the accuracy and robustness of the proposed method outperform those of existing methods.

The spectral representation and expansion based on eigen-operators were deeply studied.The relations between Green’s function of characteristic differential equation and Hermitian differential and integral operators were given.The inverse relations between Hermitian differential operator and Hermitian integral operator were also studied.The spectral representation of Hermitian differential operators was given.It was also shown that the S-L eigen-equation cannot be used to realize the spectral representations of infinite dimensions in a finite interval.The method is much simpler and clearer than that of Neumann,and has advantages.The eigen-expansion (eigen-decomposition) of the Hermitian integral operator was given,which has the advantages of theoretical generality and comprehensiveness.The incorrect discussion in Wang et al[2]was correct that it is used to study the representation of characteristic spectrum.The physical and geometric meanings of naming for the long spherical wave function in optimal eigen-expansion were given.

Source location is a constant issue of importance of both theoretical study and practical engineering. Many pioneers have put out the corresponding solutions for near- or far-field signals, and preferred contributions are suggested. To our best knowledge, there are currently few focused approaches to the complicated situation where both near- and far-field signals exist with an unknown number of sources. Additionally, the robustness of the method must be taken into account when the additive background noise does not follow Gaussian or super-Gaussian distribution. To solve these problems, a novel method based on phased fractional lower-order moment (PFLOM) is proposed to simultaneously better preserve the signal and suppress the noise. Secondly, the whole procedure of the method containing direction of arrival (DOA) estimation, range estimation, separation of near-and far-field sources, and crucial parameter settings are studied in detail. Finally, comprehensive Monte Carlo experiments are carried out in the simulation to demonstrate the superiority of the proposed method compared to the existing competitive methods. Due to the novel method’s effectiveness with an unknown number of sources and robustness against various noises, it is believed that it could be fully utilized in more fields.

Multi-sensor recordings are normally used in event-related potential (ERP)-based brain computer interfaces (BCIs), for capturing brain activities widely distributed over the cortical surface. However, this may lead to an increased number of sensors for boosting classification performance, as well as a complicated computational effort for optimizing/reducing sensors, limiting the popularization of mobile/wearable BCIs for the end use. The localization of brain activities may help fix this issue by making useful information concentrated on relatively local brain areas, thus greatly reducing the number of sensors required and computational burden arising from the sensor selection. In the present study, we examined localization of brain activities for an ERP speller, by using novel visual graphic stimuli to induce specific brain responses. Participants were instructed to perform a spelling task under both the graphic stimuli-based and traditional character-flashing-based ERP speller paradigms. Experimental results showed that, compared to character-flashing stimuli, localized brain activities, concentrated over the posterior region, were observed for the graphic stimuli. Classification accuracies and information transfer rates were further evaluated and compared among full- (FS), normal- (NS), and localized- (LS) sensor settings. Effects of PARADIGM, SENSORSETTING, and TRIAL LENGTH were examined by a three-way repeated measure analysis of variance (ANOVA). ANOVA results showed that, the graphic paradigm achieved significantly better performance under LS than those achieved by the traditional paradigm at any of the three sensor settings, indicating that with visual graphic stimuli, localized posterior activities were enough to drive an ERP-based speller to achieve comparable or even better performance, compared to the traditional paradigm using global activities.

Direction of arrival (DOA) estimation has emerged as a promising technology for many military and civilian applications, such as radio astronomy and wireless communications. However, it remains very challenging to estimate DOA for distributed sources within impulsive noise environments. As is widely known, the utilization of the subspace technology in array signal processing can enable high-resolution DOA estimation. In many instances, most of these algorithms assume that the noise is Gaussian. Considering the impulsive noise environments, the derivative of the error function is derived as the iteratively weighted factor in the objective function of low-rank decomposition. Two iterative algorithms are proposed to compute the signal subspace to implement DOA estimation for coherently distributed sources. To further improve the DOA estimation accuracy and increase the number of estimated sources, the virtual data samples are reconstructed from the extended co-prime array. Simulation results confirm that the two proposed impulsive noise suppression algorithms provide better performance than the existing algorithms. The virtual data samples that are reconstructed from the extended co-prime array can be utilized as the effective inputs for subspace-like algorithms.

Abstract The problem of spectrum sensing in multiple-input multiple-output (MIMO) cognitive radio systems using the cyclostationarity property is considered. Since the noise is not a cyclostationary signal and the interference exhibits distinct cyclostationarity as primary user (PU) signals, spectrum sensing based on cyclostationarity is superior to traditional methods. To detect the presence of PU signals, cyclostationarity-based methods tend to use the second-order cyclostationarity property of cyclostationary signals. However, the computation of cyclostationary statistics is complicated. Thus, the complexity of conventional cyclostationary feature detection methods is challenging, especially for MIMO systems. A new improved algorithm that jointly utilizes the cyclostationarity property and the multiple antenna combining technique of MIMO systems is proposed in this paper. The proposed methods simplify the complexity of spectrum sensing and provide robust detection performance. The performance of the proposed schemes compared with conventional cyclic combining methods is evaluated via Monte-Carlo simulation. The simulation results indicate that the proposed method is preferred under some severe noise and interference presence scenarios.

Abstract Estimating direction of arrival (DOA) is important in a variety of practical applications. Conventional cyclostationarity-based coherent DOA estimation algorithms are not robust to non-Gaussian α-stable impulsive noise. Additionally, fractional lower-order statistics (FLOS)-based algorithms are tolerant to impulsive noise; however, they experience performance degradation for coherent signals and interference. To overcome these drawbacks, two types of fractional lower-order cyclostationarity-based subspace DOA estimation methods are proposed for coherent signals in the presence of interference and α-stable impulsive noise. The new proposed algorithms exploit the fractional lower-order cyclostationarity properties of the signals and are immune to the impulsive noise and interference. Moreover, they can provide more accurate DOA estimates of coherent signals than conventional cyclostationarity-based and FLOS-based methods. The simulation results illustrate the robustness and effectiveness of the proposed methods for coherent signals based on a comparison with traditional methods. The new algorithms can be used in the presence of a wide range of interference, Gaussian noise, and α-stable distribution impulsive noise environments.

Obtaining accurate angle parameters using direction-of-arrival (DOA) estimation algorithms is crucial for acquiring channel state information (CSI) in massive multiple-input multiple-output (MIMO) systems. However, the performance of the existing algorithms deteriorates severely due to mutual coupling between antenna elements in practical engineering. Therefore, for solving the array mutual coupling, the array output signal vector is modeled by mutual coupling coefficients and the DOA estimation problem is transformed into block sparse signal reconstruction and parameter optimization in this paper. Then, a novel sparse Bayesian learning (SBL)-based algorithm is proposed, in which the expectation-maximum (EM) algorithm is used to estimate the unknown parameters iteratively, and the convergence speed of the algorithm is enhanced by utilizing the approximate approximation. Moreover, considering the off-grid error caused by discretization processes, the grid refinement is carried out using the polynomial roots to realize the dynamic update of the grid points, so as to improve the DOA estimation accuracy. Simulation results show that compared with the existing algorithms, the proposed algorithm is more robust to mutual coupling and off-grid error and can obtain better estimation performance.

Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise.

Statistical shape models (SSMs) have been established as robust anatomical priors for medical image segmentation, registration and anatomy modelling. To construct an SSM which accurately models the inter-subject anatomical variations, it is crucial to compute accurate shape correspondence between the training samples. To achieve this goal, the state-of-the-art shape correspondence computation methods always require tedious segmentation of the training images, while they seldom pay enough attention to the correspondence accuracy of key anatomical landmarks like the bone joints, vessel bifurcations, etc. As a result, the computation of shape correspondence is time-consuming and the correspondence accuracy is imperfect. To solve these problems, this paper proposes a novel shape correspondence computation approach which eliminates the need for image segmentation by registering an organ shape template to the training images. This method allows the human expert to specify key anatomical landmarks in the training images to define the correspondence of the crucial landmarks. An intensity-and-landmark-combined strategy is implemented to utilized both the image intensity and expert landmarks to obtain accurate shape correspondence. This method is evaluated for the construction of head anatomy SSM and spine SSM based on computed tomography (CT) images. The SSMs constructed using the proposed method demonstrates better shape correspondence accuracy than other state-of-the-arts correspondence methods. In particular, this method obtains pixel-level surface correspondence accuracy (1.38 mm) for the skull and sub-pixel level accuracy (0.92 mm) for the spine. The generalisability and specificity of the SSMs constructed using our method are also superior to SSMs constructed using other compared correspondence methods. With this method, we propose a novel approach which takes less human intervention and produces higher quality SSM with better shape modelling accuracy.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) utilizes large antenna arrays and is considered a promising technology for fifth-generation (5G) and beyond wireless communication systems. However, the high-power consumption of the radio-frequency (RF) chains makes it infeasible. To solve this problem, hybrid precoding is proposed, which is a combination of analog and digital precoding. The fully connected architecture hybrid precoding still requires a large number of phase shifters (PSs). The sub-connected architecture can greatly reduce the required power consumption, and however, it cannot obtain a satisfactory achievable rate. To avoid the high energy consumption and obtain a high resolution, we propose a novel partly connected architecture in this paper. In addition, we propose an energy-efficient successive interference cancelation (SIC) hybrid precoding based on the partly connected architecture, which transforms the problem of maximizing the total achievable rate with non-convex constraints into a series of sub-rate optimization problems. Furthermore, a low-complexity energy-efficient SIC hybrid precoding based on the partly connected architecture is developed, which uses the partial singular value decomposition (SVD) to realize the sub-rate optimization and significantly reduce the complexity. Theoretical analysis demonstrates the superiority of the proposed hybrid precoding in terms of complexity. The simulation results indicate that the proposed hybrid precoding algorithms enjoy better energy efficiency and achievable rate performance than some recently proposed hybrid precoding algorithms.

In this paper, a new direction of arrival (DOA) and the number of signals of interest (SOIs) estimation method is proposed for wideband sources in impulsive noise environments. By evaluating the cyclic correntropy function (CCF) of the received signals at a certain cycle frequency, the impulsive noise and all co-channel interferences with different cycle frequencies can be suppressed. In this approach, a theorem of the CCF is proposed, and a linear prediction (LP) model of the CCF of the array data, which is important for both DOA and number of SOIs estimations, is built by the theorem. Additionally, we introduce multiple regularization parameters into the LP model and derive an analytical expression of the maximum correntropy criterion (MCC) of the parameters for obtaining the optimal regularization parameters expression. Furthermore, by the expression of regularization parameters and the estimation error of the CCF in a limited number of snapshots, an iterative algorithm is proposed for joint estimation of the DOA and the number of SOIs with only knowledge of the cycle frequency of the SOIs.

In signal processing research, cyclostationarity and fractional lower-order statistics (FLOS) are two important solutions to non-stationary signals and non-Gaussian noises, respectively. In the last five years, many methodologies combining the two technologies were proposed to achieve the two tasks simultaneously. Unfortunately, these methodologies need to be based on the Shannon/Nyquist sampling theorem. As phased fractional lower-order cyclic moment (PFLOCM) theoretically cooperates with compressive signal processing (CSP), this paper studies PFLOCM to apply in CSP at sub-Nyquist sampling rates. Using this technical foundation, a complete procedure is novelly proposed to rebuild phased fractional lower-order cyclic moment spectrum (PFLOCMS), which functions as a crucial factor in signal detection, system identification, parameter estimation, and other applications. In addition, various experiments verify the performance of the proposed procedure. It is believed that this paper will have implications for non-stationary and non-Gaussian signal processing at sub-Nyquist sampling rates.

The unified relationship between the signal characteristic spectrum representation and the spectral decomposition for the stationary random signal was deeply studied. By using the relations among the differential operators, the integral operator and the Green's function of the characteristic differential equation, the inverse relationship between the Hermitian differential operator and the Hermitian integral operator were given, the characteristic differential equation and corresponding characteristic integral equation were demonstrated, and the spectral representations of both Hermitian differential and integral operators and the general spectral representations for both operators were provided. Based on the superposition method of the stochastic simple harmonic vibration and the Hilbert space unitary operator method for the stationary random signal spectral decomposition, the connection and unification on mathematics of the signal characteristic spectral representation and the stationary random signal spectral decomposition are revealed.

According to the performance degradation problem of the carrier frequency estimation method of time-frequency overlapped BPSK signals in Alpha-stable distribution noise,a novel carrier frequency estimation algorithm of the time-frequency overlapped BPSK signals based on the cyclic correntropy spectrum was proposed.The relationship between cyclic frequency and carrier frequency on the zero frequency section of the cyclic correntropy spectrum was deduced,which could be utilized for carrier frequency estimation.The proposed method can suppress the impulse noise and has better estimation performance than that of the contrastive methods.Simulation results are presented to demonstrate the effectiveness and robustness of the proposed method.

Many approaches have been studied in the field of array signal processing when impulsive noise is modeled with an alpha-stable distribution. By introducing the correntropy, which exhibits a robust statistics property, this paper defines a correntropy-based (COB) operator that provides a powerful mechanism to eliminate the detrimental effect of outliers in alpha-stable distributed noise environments. To improve computational efficiency, we apply the unitary transformation to the COB upper triangular Toeplitz matrix and construct a novel real-valued approximate estimation matrix with a MUSIC-like algorithm. On the basis of guaranteeing the accuracy of the direction of arrival estimation, the proposed algorithm also significantly reduces the computational complexity of the calculations. Comprehensive Monte Carlo simulation results demonstrate that the proposed algorithm is more robust than the existing algorithms in terms of the probability of resolution and root-mean-square-error, especially in the presence of highly impulsive noise or low generalized signal-to-noise ratio.

Over the past several decades, cyclostationarity has been regarded as one of the most significant theories in the research of non-stationary signal processing; therefore, it has been widely used to solve a large variety of scientific problems, such as weak signal detection, parameter estimation, pattern recognition, and mechanical signature analysis. Despite offering a feasible solution, cyclostationarity-based methods suffer from performance degradation in the presence of impulsive noise, so the methods are less adaptable and practicable. To improve the effectiveness of these algorithms, a nonlinear similarity measurement, referred to as cyclic correntropy or cyclostationary correntropy, was recently proposed that innovatively combines the cyclostationarity technology and the concept of correntropy and successfully changes the signal analysis from the finite dimensional space (Euclidean space) to the infinite dimensional space (Hilbert space). However, to date, the study of cyclic correntropy has been limited, and it needs to be explored further. In this paper, the foundations and theories of cyclic correntropy are elucidated rigorously to complete and develop the methodology, including basic definitions, statistical formalisms, mathematical derivations, convergence theorem, spectrum analysis, and kernel length estimation. It is believed that the cyclic correntropy, a novel methodology equipped with the precise framework of cyclostationarity, can address the problem of impulsive noise in mechanical and communication signals and that its algorithmic idea of crossing spaces will have a far-reaching impact on the development of signal processing.

To date, many digital atlases of the human head have been developed, but few incorporate the anatomical variations of the population. In this paper, for the first time, we constructed two digital head atlases (DHAs) of Chinese male and female adults, which can be deformed to simulate the inter-subject anatomical variations. The atlases were developed to assist with personalized medical treatment, population based radio magnetic simulation, and medical imaging device design. The statistical shape model approach was used to construct the atlases based on a training set of 65 computed tomography (CT) images (including those of 46 males and 19 females). To evaluate the accuracy of anatomy modeling, we compared the atlas morphometry parameters with the reference values of the Chinese population and observed the reasonable agreement between them. Compared with the existing digital human head atlases, a unique feature of the DHA is the capability of emulating individual head anatomy via model deformation. This feature is validated by registering the DHAs to patient CT images. The registered atlases provide complete anatomy of the target subjects, including the structures that are not clearly visible in the CT images.

Motion blur appearing in traffic sign images may lead to poor recognition results, and therefore it is of great significance to study how to deblur the images. In this paper, a novel method for deblurring traffic sign is proposed based on exemplars and several related approaches are also made. First, an exemplar dataset construction method is proposed based on multiple-size partition strategy to lower calculation cost of exemplar matching. Second, a matching criterion based on gradient information and entropy correlation coefficient is also proposed to enhance the matching accuracy. Third, L0.5-norm is introduced as the regularization item to maintain the sparsity of blur kernel. Experiments verify the superiority of the proposed approaches and extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm.

As a passive, harmless, and low-cost diagnosis tool, fetal heart rate (FHR) monitoring based on fetal phonocardiography (fPCG) signal is alternative to ultrasonographic cardiotocography. Previous fPCG-based methods commonly relied on the time difference of detected heart sound bursts. However, the performance is unavoidable to degrade due to missed heart sounds in very low signal-to-noise ratio environments. This paper proposes a FHR monitoring method using repetition frequency of heart sounds. The proposed method can track time-varying heart rate without both heart sound burst identification and denoising. The average accuracy rate comparison to benchmark is 88.3% as the SNR ranges from −4.4 dB to −26.7 dB.

This paper presents an effective optimization method using the Kriging surrogate model combing with modified rectangular grid sampling to reduce the stent dogboning effect in the expansion process. An infilling sampling criterion named expected improvement (EI) is used to balance local and global searches in the optimization iteration. Four commonly used finite element models of stent dilation were used to investigate stent dogboning rate. Thrombosis models of three typical shapes are built to test the effectiveness of optimization results. Numerical results show that two finite element models dilated by pressure applied inside the balloon are available, one of which with the artery and plaque can give an optimal stent with better expansion behavior, while the artery and plaque unincluded model is more efficient and takes a smaller amount of computation.

This paper proposes a geometric method to locate a mobile station (MS) in a mobile cellular network when both the range and angle measurements are corrupted by non-line-of-sight (NLOS) errors. The MS location is restricted to an enclosed region by geometric constraints from the temporal-spatial characteristics of the radio propagation channel. A closed-form equation of the MS position, time of arrival (TOA), angle of arrival (AOA), and angle spread is provided. The solution space of the equation is very large because the angle spreads are random variables in nature. A constrained objective function is constructed to further limit the MS position. A Lagrange multiplier-based solution and a numerical solution are proposed to resolve the MS position. The estimation quality of the estimator in term of “biased†or “unbiased†is discussed. The scale factors, which may be used to evaluate NLOS propagation level, can be estimated by the proposed method. AOA seen at base stations may be corrected to some degree. The performance comparisons among the proposed method and other hybrid location methods are investigated on different NLOS error models and with two scenarios of cell layout. It is found that the proposed method can deal with NLOS error effectively, and it is attractive for location estimation in cellular networks.