Angular Domain Research Articles

A Survey on Beamspace Millimeter-wave Massive Mimo Systems: An Overview of Open Issues, Challenges, and Future Research Trends

Abstract: The channel sparsity is exploited by transforming the channel into an angular domain from the spatial domain at millimeter wave frequencies in Beamspace Multiple-Input Multiple-Output (B-MIMO) systems. Every beam in B-MIMO corresponds to an individual radio frequency (RF) chain, and thus, beam selection techniques can reduce the RF hardware complexity. RF chain limitation is needed for an ideal beamspace precoding scheme to attain good system performance across the entire bandwidth. New methods of beamspace channel estimation with a small amount of estimation error and low computational complexity are challenging in the beam domain due to the larger antenna arrays deployed. A novel beamspace processing technique to improve the direction of arrival estimation performance for different array configurations with improvement in system performance is desired in a 5G system. A detailed review of the B-MIMO system with its architecture and various beam selection techniques is presented in this paper. The scope of the work is elaborated by incorporating various existing beamspace precoding and channel estimation methods. The paper also presents various beamspace processing schemes for parameter estimation with a focus on the direction of arrival estimation performance.

Light field image coding using a residual channel attention network–based view synthesis

PurposeLight field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.Design/methodology/approachThis work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).FindingsIn three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.Originality/value(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

Hemispherical harmonic illumination and reflectance angular spectrum.

Light scattering plays an important role in physics, with wide applications in science and engineering. However, accurate and effective modeling of scattering remains a great challenge. In this study, we exploited the rendering equation using hemispherical harmonics to demonstrate an angular frequency representation that directly depicts scattering in a two-dimensional spectrum, free from any underlying assumptions. This representation offers a compact and intuitive characterization of mirror reflection, isotropic scattering, and anisotropic emission. The robust support of theoretical proofs and data-driven experimental results establishes the broad applicability of our computational model in conducting scattering analyses across diffuse, specular, and glossy materials. With the capability to characterize the scattering in angular frequency domain, we expect our proposed model to emerge as an essential tool in various domains, including surface feature recognition, reflectance data compression, and computer rendering.

Blind Quality Evaluator of Light Field Images by Group-Based Representations and Multiple Plane-Oriented Perceptual Characteristics

Due to the emergency of multi-view cameras and commercial Light Field (LF) cameras, the demand of high-performance LF quality evaluator is of great significance for guiding LF acquisition, processing and application and further promoting the visual perceived quality of LF visualizations. However, LF Images (LFIs), as high-dimensional data, suffer from various quality degradations not only in the spatial domain but also in the angular domain. Therefore, it is of great challenge to predict LF quality accurately. An effective LF evaluator should be able to represent these heterogeneous artifacts. In this paper, we provide a novel No-Reference LF Quality Assessment Evaluator (NR LF-QAE) to tackle this problem. Firstly, to measure angular consistency among viewports, we utilize group-based representations to character information similarity of aligned view stacks. Secondly, to better describe the texture information of LFIs, unifying spatial-angular texture statistic measurement is performed via Local Binary Patterns from Three Orthogonal Planes (LBP-TOP). Thirdly, we design 3D Log-Gabor filters to extract LF global structure information in Sub-Aperture Images (SAIs) as spatial feature characterizations and 2D Log-Gabor filters are adopted to characterize ray direction/depth information in Epipolar Plane Images (EPIs) as angular feature characterizations. By comprehensive LF information analyses in angular consistency and spatial-angular feature extraction with texture and structure descriptors, experimental results demonstrate the superiority of the proposed NR LF-QAE over the state-of-the-art comparative models in predicting the quality of LFIs on three available benchmark databases. The code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zerosola/NR-LF-QAE</uri> .

Fast Planar Near-Field Measurements of Reduced Angular Pattern Domains

Fast Planar Near-Field Measurements of Reduced Angular Pattern Domains

Joint Signature Estimation and Angular Domain Channel Reuse based Random Access for Massive Connectivity

Joint Signature Estimation and Angular Domain Channel Reuse based Random Access for Massive Connectivity

Clutter filtering of angular domain data for contrast-free ultrafast microvascular imaging

Objective. Contrast-free microvascular imaging is clinically valuable for the assessment of physiological status and the early diagnosis of diseases. Effective clutter filtering is essential for microvascular visualization without contrast enhancement. Singular value decomposition (SVD)-based spatiotemporal filter has been widely used to suppress clutter. However, clinical real-time imaging relies on short ensembles (dozens of frames), which limits the implementation of SVD filtering due to the large error of eigen-correlated estimations and high dependence on optimal threshold when used in such ensembles. Approach. To address the above challenges of imaging in short ensembles, two optimized filters of angular domain data are proposed in this paper: grouped angle SVD (GA-SVD) and angular-coherence-based higher-order SVD (AC-HOSVD). GA-SVD applies SVD to the concatenation of all angular data to improve clutter rejection performance in short ensembles, while AC-HOSVD applies HOSVD to the angular data tensor and utilizes angular coherence in addition to spatial and temporal features for filtering. Feasible threshold selection strategies in each feature space are provided. The clutter rejection performance of the proposed filters and SVD was evaluated with Doppler phantom and in vivo studies at different cases. Moreover, the robustness of the filters was explored under wrong singular value threshold estimation, and their computational complexity was studied. Main results. Qualitative and quantitative results indicated that GA-SVD and AC-HOSVD can effectively improve clutter rejection performance in short ensembles, especially AC-HOSVD. Notably, the proposed methods using 20 frames had similar image quality to SVD using 100 frames. In vivo studies showed that compared to SVD, GA-SVD increased the signal-to-noise-ratio (SNR) by 6.03 dB on average, and AC-HOSVD increased the SNR by 8.93 dB on average. Furthermore, AC-HOSVD remained better power Doppler image quality under non-optimal thresholds, followed by GA-SVD. Significance. The proposed filters can greatly enhance contrast-free microvascular visualization in short ensembles and have potential for different clinical translations due to the performance differences.

Guided mode resonances for angular and spectral filtering

The development of devices based on compact waveguides is a rapidly evolving field. A unique variation of such devices is the Fano-like resonance dielectric spectral and spatial filtre, made from a dielectric conformal thin film on a surface relief grating. It can be used in a normal (or oblique) angle-of-incidence configuration. This device facilitates directional selectivity, which is useful for transverse-mode cleaning in short optical cavities. It can also be enhanced by using leaky-mode effects to produce even sharper spectral features. However, a perfect spatial filtre should be as invariant as possible in the spectral and enhanced angular domains. Here, we solved the inverse design problem to produce such an effect. Numerically, we tuned the surface profile of the substrate grating, assuming a conformal layer on top. This resulted in trapezoidal relief patterns followed by narrow flat-top angular features. The combination of leaky mode effect and enhanced device topology will enable efficient and useful devices with a bandwidth of 50 nm and even more in the future.

Meromorphic functions that share one finite value DM with their derivative in an angular domain

Meromorphic functions that share one finite value DM with their derivative in an angular domain

An ensemble deep learning approach for untrained compound fault diagnosis in bearings under unstable conditions

Based on the dimension invariance property of the data-driven bearing fault diagnosis method, unstable condition data can result in the loss of information and reduced diagnostic accuracy due to inconsistent data dimensions. Furthermore, the fixed parameters of the output layer restrict its ability to accurately diagnose faults beyond the training set, particularly compound faults with limited data. To address these challenges, this study proposes an ensemble deep learning approach for identifying untrained compound faults in bearings operating under non-stationary conditions. Firstly, a signal angular domain processing technique is employed to standardize the dimensionality of the bearing’s state information, effectively mitigating information loss. Secondly, a feature extraction model is established to dynamically capture local microscopic and multilevel features utilizing the adaptability of convolutional neural network (CNN), and it can mine the relevant features of compound faults through the single-fault features. In the verification process, the kmeans algorithm with scalable classification is used to optimize the classifier of CNN. Specifically, the number of cluster centers in kmeans is set to exceed the count of training fault categories. Identification of untrained compound faults is achieved by calculating the Euclidean distances between each feature and the cluster centers, based on the principle of minimum distance. It addresses the challenge of inadequate diagnostic rates for untrained compound faults. The diagnostic outcomes prove that the proposed method has a high diagnostic robustness and generalization ability, which can effectively solve the problem of insufficient fault data and provide a new way of diagnosing untrained compound faults.

A truncation error bound for branched continued fractions of the special form on subsets of angular domains

Truncation error bounds for branched continued fractions of the special form are established. These fractions can be obtained by fixing the values of variables in branched continued fractions with independent variables, which is an effective tool for approximating complex functions of two variables. The main result is a two-dimensional analog of the theorem considered in [SCIAM J. Numer. Anal. 1983, 20 (3), 1187$-$1197] for van Vleck's continued fractions. For its proving, the $\mathcal{C}$-figure convergence and estimates of the difference between approximants of fractions in an angular domain are significantly used. In comparison with the previously established results, the elements of a branched continued fraction of the special form can tend to zero at a certain rate. An example of the effectiveness of using a two-dimensional analog of van Vleck's theorem is considered.

The Circular Width of Asymmetric Angular Domain S(α,β) and Its Application

The Circular Width of Asymmetric Angular Domain S(α,β) and Its Application

Multi-scale moving target detection with FDA-MIMO radar

The recently developed frequency diverse array (FDA) radar applies a small frequency increment across the array elements, which provides advantages in clutter suppression, jamming suppression, and target detection for existing radar systems. In this paper, the frequency increment is enlarged to guarantee that the transmit waveforms are orthogonal in frequency domain for multiple-input multiple-output (MIMO) radar. A multi-scale moving target detection method of FDA-MIMO radar is proposed. The FDA radar with a large frequency increment suffers from the deteriorated decorrelation of scatterers within the same range bin and causes the inner-bin range dependence (IRD) property in transmit spatial domain. By taking additional space–time snapshots in transmit domain as training samples, the space–time adaptive processing (STAP) is applied in joint receive and Doppler domain for clutter suppression, which alleviates the requirement of insufficient training samples in non-stationary clutter environment. In the sequel, a set of beamformers within the whole angular domain of interest is utilized for target detection, which achieves target detection under the condition of random target locations and sizes. Numerical simulation results are provided to verify the effectiveness of the proposed method.

A Tacholess Order Tracking Method Based on the STFTSC Algorithm for Rotor Unbalance Fault Diagnosis Under Variable-Speed Conditions

Abstract Due to the fact that rotors usually operate in a non-stationary mode with changing speeds, the conventional rotor unbalance detection method based on the stationary signal will produce a major “spectrum ambiguity issue” and affect the accuracy of rotor unbalance detection. To this end, a tacholess order tracking method based on the STFTSC algorithm is suggested in this study, where the STFTSC algorithm is developed by combining the short-time Fourier transform and the seam carving algorithm. First, the STFTSC algorithm is utilized to accurately extract the instantaneous frequency (IF) of the rotor and calculate the instantaneous phase under variable-speed conditions. Subsequently, the original signal is resampled in the angular domain to transform the non-stationary time domain signal into a stable angle domain signal, eliminating the effect of the speed variations. Finally, the angular domain signal is transformed into the order domain signal, which uses the discrete Fourier transform and the discrete spectrum correction method to identify the amplitude and phase corresponding to the fundamental frequency component of the signal. The simulation results show that the IF extracted by the STFTSC algorithm has higher extraction accuracy compared with the traditional STFT spectral peak detection method and effectively eliminates the effect of speed fluctuations. A rotor dynamic-balancing experiment shows that the unbalance correction effect based on the STFTSC algorithm is remarkable, with the average unbalance amount decrease rate on the left and right sides being 90.02% and 92.56%, respectively, after a single correction.

Solution of a two-dimensional parabolic model problem in a degenerate angular domain

In this paper, the boundary value problem of heat conduction in a domain was considered, boundary of which changes with time, as well as there is no the problem solution domain at the initial time, that is, it degenerates into a point. To solve the problem, the method of heat potentials was used, which makes it possible to reduce it to a singular Volterra type integral equations of the second kind. The peculiarity of the obtained integral equation is that it fundamentally differs from the classical Volterra integral equations, since the Picard method is not applicable to it and the corresponding homogeneous integral equation has a nonzero solution.

PAPR Suppression for Angular-Domain-Based Massive Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing System

In this paper, the precoding-based peak-to-average power ratio (PAPR) reduction methods are studied for the massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system in the angular domain. The expectation maximization generalized approximate message passing algorithm and the proposed optimized alternating direction method of multipliers (OADMM) scheme are adopted here to explore the system characteristics in terms of the symbol error ratio (SER) performance, the PAPR reduction efficiency, and the inter user interference (IUI). Specifically, the high PAPR problem is reduced to no more than 0.18 dB by using the inherent property of the massive MIMO-OFDM angle division multiple access (ADMA) system with relatively reduced computational complexity. Moreover, the value of SER is around 10−5 dB when the proposed technique is performed. Computer numerical simulation results verify the efficiency of the proposed technique from the perspective of SER, PAPR suppression, and IUI.

Visual Security Index Combining CNN and Filter for Perceptually Encrypted Light Field Images

Visual security index (VSI) represents a quantitative index for the visual security evaluation of perceptually encrypted images. Recently, the research on visual security of encrypted light field (LF) images faces two challenges. One is that the existing perceptually encrypted image databases are often too small, which is easy to cause overfitting in convolutional neural network (CNN). The other is that existing VSI models did not take a full account the intrinsic characteristics of the LF images and highly relied on handcrafted feature extraction. In this article, we construct a new database of perceptually encrypted LF images, called the PE-SLF, which is 2.6 times as big as the existing largest perceptual encrypted image database. Moreover, a novel visual security index (VSI) model is proposed by taking into full consideration the intrinsic spatial-angular characteristics of the LF images and the outstanding capabilities of CNN in feature extraction. First, we exploit CNN to detect the texture and structure features of encrypted sub-aperture images in the spatial domain. Second, we apply the Gabor filter to detect the Gabor feature over the epi-polar plane images in angular domain. Last, the spatial and angular similarity measurements are subsequently calculated for jointly yielding the final visual security score. Experimental results on the constructed PE-SLF demonstrate that the proposed VSI model is closer to the perception of HVS in visual security evaluation of encrypted LF images compared to other classical and state-of-the-art models.

Determination of one unknown coefficient in a two-phase free boundary problem in an angular domain with variable thermal conductivity and specific heat

Two different two-phase free boundary Stefan problems in an angular domain with temperature-dependent thermal coefficients are considered. Analytical similarity solutions are obtained imposing a Dirichlet or Neumann type boundary condition, respectively, by solving functional problems. Moreover, formulas are obtained for the determination of one unknown thermal coefficient in the overspecified problem that consists in adding a Neumann condition to the problem with a Dirichlet one if and only if some restrictions on data are verified.

Spectrum Allocation and User Scheduling Based on Combinatorial Multi-Armed Bandit for 5G Massive MIMO.

As a key 5G technology, massive multiple-input multiple-output (MIMO) can effectively improve system capacity and reduce latency. This paper proposes a user scheduling and spectrum allocation method based on combinatorial multi-armed bandit (CMAB) for a massive MIMO system. Compared with traditional methods, the proposed CMAB-based method can avoid channel estimation for all users, significantly reduce pilot overhead, and improve spectral efficiency. Specifically, the proposed method is a two-stage method; in the first stage, we transform the user scheduling problem into a CMAB problem, with each user being referred to as a base arm and the energy of the channel being considered a reward. A linear upper confidence bound (UCB) arm selection algorithm is proposed. It is proved that the proposed user scheduling algorithm experiences logarithmic regret over time. In the second stage, by grouping the statistical channel state information (CSI), such that the statistical CSI of the users in the angular domain in different groups is approximately orthogonal, we are able to select one user in each group and allocate a subcarrier to the selected users, so that the channels of users on each subcarrier are approximately orthogonal, which can reduce the inter-user interference and improve the spectral efficiency. The simulation results validate that the proposed method has a high spectral efficiency.

Evaluation of the Resolution in Inverse Scattering of Dielectric Cylinders for Medical Applications.

The inverse scattering problem has numerous significant applications, including in geophysical explorations, medical imaging, and radar imaging. To achieve better performance of the imaging system, theoretical knowledge of the resolution of the algorithm is required for most of these applications. However, analytical investigations about the resolution presently feel inadequate. In order to estimate the achievable resolution, we address the point spread function (PSF) evaluation of the scattered field for a single frequency and the multi-view case both for the near and the far fields and the scalar case when the angular domain of the incident field and observation ranges is a round angle. Instead of the common free space condition, an inhomogeneous background medium, consisting of a homogeneous dielectric cylinder with a circular cross-section in free space, is assumed. In addition, since the exact evaluation of the PSF can only be accomplished numerically, an analytical approximation of the resolution is also considered. For the sake of its comparison, the truncated singular value decomposition (TSVD) algorithm can be used to implement the exact PSF. We show how the behavior of the singular values and the resolution change by varying the permittivity of the background medium. The usefulness of the theoretical discussion is demonstrated in localizing point-like scatterers within a dielectric cylinder, so mimicking a scenario that may occur in breast cancer imaging. Numerical results are provided to validate the analytical investigations.