Angular Channel Research Articles

FEATURES OF RESTORING MEASUREMENT VALUES OF THE DOPPLER RADAR SYSTEM MFTR-2100/40 DURING ARMAMENT AND MILITARY EQUIPMENT TESTING

During the registration and processing of measurement information obtained by the MFTR-2100/40 radar system, there is a probability of missing values in the trajectory measurement data. Experience with the radar system indicates a high sensitivity of the angular channels to the propagation of the radar signal in the real atmosphere. As a result of these errors, object tracking may be lost over a certain period, causing periodic delays in data registration, necessitating the filling or restoring of missing values with acceptable accuracy. The built-in algorithms and mathematical methods of the WinTrack specialized software (SS) allow for the interpolation and extrapolation of the trajectory measurement data of the MFTR-2100/40 radar system for visual display and plotting of tracks, including ranges with missing values. However, the functional capabilities of the WinTrack SS are not designed for full and accurate restoration of missing values. Additionally, the limited tools of the WinTrack SS necessitate finding alternatives in other software to ensure the reliability of the restored results. Trajectory measurement data is a large dataset, so selecting the right method for filling in missing values is crucial to maintaining a low percentage of deviations in the obtained results. This publication examines the functional capabilities of the WinTrack SS that allow for the restoration of missing trajectory measurement values. The approximation of measured trajectory data values in the WinTrack SS enables partial filling of missing values upon export, but not for all data types. An algorithm for the process of restoring missing values in trajectory measurement data arrays is provided.

The effects of ECAP, Mn and Ca elements on the microstructure and corrosion properties of magnesium alloys were investigated

In this study, through reasonable composition design, three elements of Zn, Mn and Ca, which are harmless to human body, were selected for alloying magnesium. The effects of ECAP treatment on the microstructure and corrosion properties of Mg-4Zn-1Mn-0Ca, Mg-4Zn-1Mn-0.2Ca and Mg-4Zn-0Mn-0.2Ca alloys were studied. The corrosion mechanism of Mg-Zn-Mn-Ca alloys with different components in Simulated. Body fluid (SBF) was analyzed by weight loss method and electrochemical test. The grains and the second phase were refined by Equal Angular Channel Pressing (ECAP). It was found that after 8 ECAP deformations, the microstructure of the alloy was refined and more uniform than that of the extruded alloy. The corrosion resistance of the Mg-Zn-Mn-Ca alloy after ECAP deformation in simulated body fluids was studied by electrochemical testing techniques, immersion experiments and observation of the microstructure and morphology of corrosion products. The relationship between microstructure characteristics, the behavior of the magnesium metal matrix and the properties of corrosion products was revealed. The results show that the microstructure of the alloy is refined and the corrosion resistance is improved with the increase in extrusion passes in the SBF solution. The corrosion resistance of magnesium alloy after 8 ECAP deformation is the best, showing small Icorr and large Rt. With the extension of the soaking time, the surface of the alloy will form a passivation film, which will protect the matrix and avoid further corrosion of the alloy. It is found that in SBF, the corrosion of the alloy surface is mainly pitting, which indicates that Ca2+, HCO3- and HPO42- in SBF can reduce the corrosion rate of magnesium alloy.

Cascaded partitioned phase modulation for cross-connection of orbital angular momentum mode and polarization multiplexing channels.

Multi-dimensional orbital angular momentum (OAM) mode multiplexing provides a promising route for enlarging communication capacity and establishing comprehensive networks. While multi-dimensional multiplexing has gained advancements, the cross-connection of these multiplexed channels, especially involving modes and polarizations, remains challenging due to the needs for multi-mode interconversion and on-demand polarization control. Herein, we propose an OAM mode-polarization cross-transformation solution via cascaded partitioned phase modulation, which enables the divergently separated OAM modes to be independently phase-imposed within distinct spatial regions, leading to the synergistic conversion operation of mode and polarization channels. In demonstrations, we implemented the cross-connection of three OAM modes and two polarization multiplexed channels, achieving the mode purity that exceeds 0.951 and polarization contrast up to 0.947. The measured mode insertion losses and polarization conversion losses are below 3.42 and 3.54 dB, respectively. Consequently, 1.2 Tbit/s quadrature phase shift keying signals were successfully exchanged, yielding the bit-error-rates close to 10-6. Incorporating with increased partitioned phase treatments, this approach shows promise in accommodating massive mode-polarization multiplexed channels, which hold the potential to augment networking capability of large-scale OAM mode multiplexing communication networks.

Compressive Sensing-Based Channel Estimation for Uplink and Downlink Reconfigurable Intelligent Surface-Aided Millimeter Wave Massive MIMO Systems

This paper investigates single-user uplink and two-user downlink channel estimation in reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. Because of the difficulty associated with the estimation of channels in RIS-aided wireless communication systems, channel state information (CSI) is assumed to be known at the receiver in some previous works in the literature. By assuming that prior knowledge of the line-of-sight (LoS) channel between the RIS and the base station (BS) is known, two compressive sensing-based channel estimation schemes that are based on simultaneous orthogonal matching pursuit and structured matching pursuit (StrMP) algorithms are proposed for estimation of uplink channel between RIS and user equipment (UE), and joint estimations of downlink channels between BS and a UE, and between RIS and another UE, respectively. The proposed channel estimation schemes exploit the inherent common sparsity shared by the angular domain mmWave channels at different subcarriers. The superiority of one of the proposed channel estimation techniques, the StrMP-based channel estimation technique, with negligibly higher computational complexity cost compared with other channel estimators, is documented through extensive computer simulation. Specifically, with a reduced pilot overhead, the proposed StrMP-based channel estimation scheme exhibits better performance than other channel estimation schemes considered in this paper for signal-to-noise ratio (SNR) between 0 dB and 5 dB upward at different instances for both uplink and downlink scenarios, respectively. However, below these values of SNR the proposed StrMP-based channel estimation scheme will require higher pilot overhead to perform optimally.

Beamforming Oriented Angular Domain Channel Prediction for Mixed LOS and Non-LOS SIMO Environments With High Mobility

Beamforming Oriented Angular Domain Channel Prediction for Mixed LOS and Non-LOS SIMO Environments With High Mobility

Beam-Space Post-Doppler Reduced-Dimension STAP Based on Sparse Bayesian Learning

The space–time adaptive processing (STAP) technique can effectively suppress the ground clutter faced by the airborne radar during its downward-looking operation and thus can significantly improve the detection performance of moving targets. However, the optimal STAP requires a large number of independent identically distributed (i.i.d) samples to accurately estimate the clutter plus noise covariance matrix (CNCM), which limits its application in practice. In this paper, we fully consider the heterogeneity of clutter in real-world environments and propose a sparse Bayesian learning-based reduced-dimension STAP method that achieves suboptimal clutter suppression performance using only a single sample. First, the sparse Bayesian learning (SBL) algorithm is used to estimate the CNCM using a single training sample. Second, a novel angular Doppler channel selection algorithm is proposed with the criterion of maximizing the output signal-to-clutter-noise ratio (SCNR). Finally, the reduced-dimension STAP filter is constructed using the selected channels. Simulation results show that the proposed algorithm can achieve suboptimal clutter suppression performance in extremely heterogeneous clutter environments where only one training sample can be used.

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

Common structured sparsity based multi-user channel estimation for double-RIS assisted millimeter wave systems

Compared to a single reconfigurable intelligent surface (RIS) system, a double RIS system can achieve better passive beamforming gain, while requiring more accurate channel state information (CSI). However, due to the presence of double-reflection links, channel estimation with low pilot overhead is quite challenging. In this paper, we investigate channel estimation of double-RIS assisted multi-user millimeter wave (mmWave) communication systems. Firstly, the channel estimation problem for double-reflection links is formulated as a sparse signal recovery problem. Then, we analyze and exploit the common structured sparsity of the cascaded channel among multiple users, i.e., the common row and column sparsity in the angular channel matrix, and the corresponding common sparsity after being transformed into the channel vector. Inspired by this, we further propose a novel common structured sparsity based orthogonal matching pursuit (CSS-OMP) algorithm for multi-user joint channel estimation. Simulation results show that CSS-OMP algorithm can provide more accurate channel estimation with reduced pilot overhead compared to the conventional OMP algorithm.

A Robust Scheme for RIS-Assisted UAV Secure Communication in IoT

Reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAV) have been extensively studied on the Internet of Things (IoT) systems to improve communication performance. In this paper, we aimed to counter simultaneous jamming and eavesdropping attacks by jointly designing an active beamforming vector at the base station (BS) and reflect phase shifts at the RIS. Specifically, considering imperfect angular channel state information (CSI), the sum secrecy rate maximization problem in the worst case could be formulated, which is NP-hard and non-convex. To address this problem, we improved the robust enhanced signal-to-leakage-and-noise ratio (E-SLNR) beamforming to reduce the computational complexity and mitigate the impact of interference, eavesdropping and jamming. Furthermore, a genetic algorithm with a tabu search (GA-TS) method was proposed to efficiently obtain an approximate optimal solution. The simulation results demonstrated that the proposed GA-TS method converged faster with better results than conventional GA, while the proposed robust scheme could achieve higher sum secrecy rates than the zero-forcing (ZF) and SLNR schemes.

Deep Learning-Based Channel Estimation for Wideband Hybrid MmWave Massive MIMO

Hybrid analog-digital (HAD) architecture is widely adopted in practical millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to reduce hardware cost and energy consumption. However, channel estimation in the context of HAD is challenging due to only limited radio frequency (RF) chains at transceivers. Although various compressive sensing (CS) algorithms have been developed to solve this problem by exploiting inherent channel sparsity and sparsity structures, practical effects, such as power leakage and beam squint, can still make the real channel features deviate from the assumed models and result in performance degradation. Besides, the high complexity of CS algorithms caused by a large number of iterations hinders their applications in practice. To tackle these issues, we develop a deep learning (DL)-based channel estimation approach where the sparse Bayesian learning (SBL) algorithm is unfolded into a deep neural network (DNN). In each SBL layer, Gaussian variance parameters of the sparse angular domain channel are updated by a tailored DNN, which is able to capture complicated channel sparsity structures in various domains effectively and efficiently. The measurement matrix is jointly optimized for performance improvement. Then, the proposed approach is extended to the multi-block case where channel correlation in time is further exploited to adaptively predict the measurement matrix and facilitate the update of variance parameters. Simulation results show that the proposed approaches outperform existing approaches in terms of both performance and complexity.

Finite element analysis of material deformation behaviour during DRECE: the sheet metal SPD process

The material deformation behaviour during the innovative SPD process called DRECE (Dual Rolls Equal Channel Extrusion) has been analysed by FEM simulations. In the process, a workpiece in the form of a strip is subjected to plastic deformation by passing through the angular channel; however, the workpiece dimensions remain the same after a pass is finished. Performing consecutive passes allow for increasing the effective strain in the material to a required level. In the conducted simulations two various channel angles (108° and 113°) have been taken into consideration, as well as two processing routes, A and C (without and with turning the strip upside-down between consecutive passes, respectively). The analysis of simulation results has revealed that significant strain and stress inhomogeneities across the strip thickness are generated in a single DRECE pass. The die design (the inner and outer corner radius) and friction conditions affect the material flow, reducing significantly the shear strain in the near-surface regions of the strip. The strain inhomogeneity can be effectively reduced by choosing the processing route C. The strain distributions and the corresponding tensile test results have confirmed that the smaller channel die angle allows to generate larger strain and higher strength of the strip but also reduces its ductility more than the die setup with the larger channel die angle.

A Lightweight Neural Network Based on GAF and ECA for Bearing Fault Diagnosis

A lightweight neural network fault diagnosis method based on Gramian angular field (GAF) feature map construction and efficient channel attention (ECA) optimization is presented herein to address the problem of the complex structure of traditional neural networks in bearing fault diagnosis. Firstly, a GAF is used to encode vibration signals into a temporal image. Secondly, the double-layer separation residual convolution neural network (DRCNN) is used to learn advanced features of the sample. The multi-branch structure is used as the receiving domain. ECA learns the correlation between feature channels. The extracted feature channels are adaptively weighted by adding a small additional computational cost. Finally, the method is tested and evaluated using wind turbine bearing data. The experimental results show that, compared with the traditional neural network, the DRCNN model based on GAF achieves higher diagnostic accuracy with less parameter calculation.

Effect of ECAP process on deformability, microstructure and conductivity of CuCoNi alloy

The study deals with the influence of various variants of severe plastic deformation in the Equal Channel Angular Pressing (ECAP) process on the microstructure, microhardness, and conductivity of CuCoNi alloy. The microstructure evolution was studied by microscopic observations and electron back-scattered diffraction (EBSD) in a scanning electron microscope (SEM). The Vickers method was used to test the microhardness of the samples after various variants of the ECAP process. The conductivity was measured with an eddy current electrical conductivity meter based on the complex impedance of the probe. The results indicated the possibility of deformation of CuCoNi alloys in the process of pressing through the ECAP angular channel and developing their microstructure and properties. The method is an effective tool for strengthening the tested copper alloy by refining its microstructure. After the first pass, the grain size was reduced by 80%. Increasing the plastic deformation temperature did not significantly affect the obtained level of microstructure fragmentation - the average grain size is approx. 1.4-1.5 ?m. The fragmentation of the microstructure had a negligible effect on the conductivity of the CuCoNi alloy, which oscillated at the value of 13 MS/m after the ECAP process.

Joint Transmissive and Reflective RIS-Aided Secure MIMO Systems Design Under Spatially-Correlated Angular Uncertainty and Coupled PSEs

This paper investigates a joint transmissive and reflective reconfigurable intelligent surfaces (RIS) -aided secure multiple-input multiple-output (MIMO) system, where both a RIS-assisted transmitter and a RIS-based reflector are deployed to defend against the simultaneous jamming attack and wiretapping threat. Our design focuses on maximizing the sum rate under the unknown jammer’s beamforming, joint RISs’ coupled phase shift errors (PSEs), and spatially-correlated angular channel uncertainties. Besides, we take into account the various quality-of-service (QoS) requirement constraints for guaranteeing the secure performance. Since the problem is non-convex and mathematically intractable, a new optimization framework is established to facilitate the solution development to the formulated problem. Specifically, armed with the Akaike information criterion, a novel diagonalization method is first proposed to estimate the unknown jamming covariance matrix. Then, a series of fractional-eliminated rate expressions is derived that facilitates the application of the proposed Double Deterministic Transformation (DDT) to tackle the coupled stochastic PSEs. Besides, regardless of the spatial correlation matrix, a general discretization method is proposed to convert the e spatially-correlatd angular uncertainties into a worst-case robust one. Subsequently, building upon the above transformations which transform the original problem into tractable one, a two-layer iterative Lagrange multiplier algorithm capitalizing a low-complexity dual method is proposed to obtain the globally optimal solution of the digital precoder, where the multiple QoS constraints are handled without iteration. Meanwhile, we develop a novel polyblock-based multiple penalty method to obtain the globally optimal solutions to RISs’ phase shifts which can simultaneously satisfy the multiple QoS constraints. Moreover, to address the narrow feasibility region induced by the multiple QoS constraints, a heuristic initial optimization method is proposed, which strengthens the existing result. Finally, theoretical analysis and numerical results demonstrate the optimality and the excellent performance of our proposed optimization framework.

Reconfigurable Intelligent Surface-Aided 6G Massive Access: Coupled Tensor Modeling and Sparse Bayesian Learning

This paper investigates a reconfigurable intelligent surface (RIS)-aided unsourced random access (URA) scheme for the sixth-generation (6G) wireless networks with massive sporadic traffic devices. First of all, this paper proposes a novel joint active device separation (the message recovery of active device) and channel estimation architecture for the RIS-aided URA. Specifically, the RIS passive reflection is optimized before the successful device separation. Then, by associating the data sequences to multiple rank-one tensors and exploiting the angular sparsity of the RIS-BS channel, the detection problem is cast as a high-order coupled tensor decomposition problem without the need of exploiting pilot sequences. However, the inherent coupling among multiple sparse device-RIS channels, together with the unknown number of active devices make the detection problem at hand deviate from the widely-used coupled tensor decomposition format. To overcome this challenge, this paper judiciously devises a probabilistic model that captures both the element-wise sparsity from the angular channel model and the low-rank property due to the sporadic nature of URA. Then, based on such a probabilistic model, a iterative detection algorithm is developed under the framework of sparse variational inference, where each update iteration is obtained in a closed-form and the number of active devices can be automatically estimated for effectively avoiding the overfitting of noise. Extensive simulation results confirm the excellence of the proposed URA algorithm, especially for the case of a large number of reflecting elements for accommodating a significantly large number of devices.

RIS-Assisted Robust Hybrid Beamforming Against Simultaneous Jamming and Eavesdropping Attacks

Wireless communications are increasingly vulnerable to simultaneous jamming and eavesdropping attacks due to the inherent broadcast nature of wireless channels. With this focus, due to the potential of reconfigurable intelligent surface (RIS) in substantially saving power consumption and boosting information security, this paper is the first work to investigate the effect of the RIS-assisted wireless transmitter in improving both the spectrum efficiency and the security of multi-user cellular network. Specifically, with the imperfect angular channel state information (CSI), we aim to address the worst-case sum rate maximization problem by jointly designing the receive decoder at the users, both the digital precoder and the artificial noise (AN) at the base station (BS), and the analog precoder at the RIS, while meeting the minimum achievable rate constraint, the maximum wiretap rate requirement, and the maximum power constraint. To address the non-convexity of the formulated problem, we first propose an alternative optimization (AO) method to obtain an efficient solution. In particular, a heuristic scheme is proposed to convert the imperfect angular CSI into a robust one and facilitate the developing a closed-form solution to the receive decoder. Then, after reformulating the original problem into a tractable one by exploiting the majorization-minimization (MM) method, the digital precoder and AN can be addressed by the quadratically constrained quadratic programming (QCQP), and the RIS-aided analog precoder is solved by the proposed price mechanism-based Riemannian manifold optimization (RMO). To further reduce the computational complexity of the proposed AO method and gain more insights, we develop a low-complexity monotonic optimization algorithm combined with the dual method (MO-dual) to identify the closed-form solution. Numerical simulations using realistic RIS and communication models demonstrate the superiority and validity of our proposed schemes over the existing benchmark schemes.

Influence of Processing Route on the Fracture Resistance of Equal Channel Angular Pressing Deformed Iron

The mechanical behavior of pure iron (Armco‐iron) deformed at 200 °C by equal angular channel pressing using processing route BC is investigated with a focus on the fracture properties. In particular, the fracture toughness in terms of elastic–plastic fracture mechanics is evaluated for different specimen orientations with respect to the last deformation step. In addition, the results are comprehensively compared with material processed by route A. The microstructural features of both deformation routes are presented and the underlying microstructure–property relationships are discussed. Both deformation routes (A and BC) lead to a comparable strength even though the microstructural features differ. Route A contains a banded and well‐aligned structure almost parallel to the extrusion direction. For route BC, the microstructural alignment is rotated with respect to the pre‐crack orientation and the grain structure is more heterogeneous. For both deformation routes, the crack plane orientation plays a significant role in the fracture characteristics. The fracture toughness along the extrusion direction, with substantially low fracture toughness for route A, is markedly enhanced for route BC. Consequently, route BC offers a pathway to obtaining severe plastic deformation (SPD) materials with high damage tolerance and a considerably lower extent of anisotropy.

Influence of launch beam distribution on power flow and angular division multiplexing in seven-core silica optical fibers

We analyze the effect of launch beam distribution on space-division multiplexing (SDM) performance in multimode multicore silica optical fibers (MM MC SOF) with seven cores. The time-independent power flow equation (TI PFE) is used to explore the effect of the width of the distribution of the Gaussian launch beam on power flow in each of the seven cores. We show that the optical fiber length at which the equilibrium mode distribution (EMD) and steady-state distribution (SSD) are obtained is greatly influenced by the width of the Gaussian launch beam distribution. We further show that when the width of the Gaussian launch beam distribution widens, the optical fiber length at which angular division multiplexing (ADM) in each of the seven cores can be realized with minimal crosstalk between neighboring angular optical channels decreases. We demonstrate that, for increasing the capacity of an optical fiber transmission system, an SDM system with two- and three-channel ADM and multicore optical fiber multiplexing can be implemented with the proposed seven-core MM MC SOF at optical fiber lengths up to ≈1 km (2 ADM channels × 7 cores) and ≈200 m (3 ADM channels × 7 cores), respectively. Such characterization of MM MC SOFs under various launch conditions is important for building a multicore optical fiber SDM transmission system.

Low-Complexity Compressive Channel Estimation for IRS-Aided mmWave Systems With Hypernetwork-Assisted LAMP Network

Intelligent reflecting surface (IRS) can enhance the wireless communication environment by smartly reflecting the incident signal toward desired directions. However, the acquisition of channel state information (CSI) is challenging since IRS usually consists of a massive number of passive elements that have no capabilities of sensing and processing the pilot signals. Although by exploiting the sparsity of the angular domain channel, the huge pilot overhead can be reduced with conventional compressive sensing algorithms, such as approximate message passing (AMP), these algorithms cannot achieve satisfactory channel estimation performance. Based on the learned AMP (LAMP) network, we propose a hypernetwork-assisted LAMP (HN-LAMP) network with dynamic shrinkage parameters to improve the channel estimation accuracy. Furthermore, a recurrent architecture is adopted to reduce the large memory overhead arising from the LAMP network. Simulation results show that the proposed HN-LAMP network can improve the channel estimation accuracy or reduce the computational complexity under satisfactory estimation performance. Moreover, the proposed hypernetwork-assisted recurrent LAMP (HNR-LAMP) architecture can effectively reduce 50% memory overhead by sharing learnable weights.

Approximate Message Passing for Channel Estimation in Reconfigurable Intelligent Surface Aided MIMO Multiuser Systems

Channel estimation is one of the main challenges for implementing reconfigurable intelligent surface (RIS)-aided communication system with a large number of antennas at the base station (BS) because RIS is equipped with many passive reflective elements without active transmitting/receiving and signal processing abilities. In this paper, we focus on the channel estimation in a general RIS-aided multi-user mmWave communication system. Specifically, a novel two-phase channel estimation scheme consisting of on-line and off-line phases is proposed to estimate the BS-RIS channel, RIS-user channel, and BS-user channel, respectively. Inspired by the characteristics of few scatterers in mmWave communication systems, the antenna domain channels are converted into the virtual angular domain channels by using the discrete Fourier transform (DFT) matrix. Thus, the estimation problem can be formulated as a compressed sensing (CS) problem, and solved by using efficient vector approximate message passing (VAMP) algorithm with expectation-maximization (EM) to learn unknown parameters and obtain the estimates simultaneously. Simulation results show that, the above algorithm with two choices of prior distributions in the proposed mmWave RIS-aided multi-user system has fast convergence speed and desirable estimation performance.