Search

This research paper is proposed to cater the design requirements of phased array antenna with side lobes pertaining for radio frequency (RF) wireless domain applications. As wireless communications have become an inevitable part of our lives as-on- date, designing smaller and lighter mobile terminals is one of the critical requirements for the development of wireless systems. A single antenna gives only a small directivity, whereas in terms of increased directivity an antenna array can be a possible candidate. Considerable optimization methods for synthesizing array pattern have started to emerge thereby achieving a pattern of desired one related to the application we work in. The major factor which drives the far field structure in phased array antenna with an allowable side lobe level is the power distribution among the array of elements. This paper presents a novel method for side lobe level (SLL) suppression in amplitude tapered planar array structure and it is compared with different distribution methods relating to array coefficients with prime focus on Taylor, Chebyshev and Gaussian distributions. CST Microwave Studio software is used to simulate various distributions and it is compared with characteristics such as gain, directivity, beam width and side lobe level suppression.

In this article, channel estimation for millimeter-wave massive multiple-input–multiple-output (MIMO) systems with hybrid beamforming is considered. With a large number of antenna elements, a progressive time delay across antenna elements incurs a change in the beam direction as a function of subcarriers in an orthogonal frequency-division multiplexing system. Considering the difference in delay across the entire array, a new channel estimation framework is proposed by using a maximum likelihood criterion as a function of the analog training beam and pilot subcarrier assignment. The resulting design achieves asymptotic orthogonality among the channel paths and minimizes the mean squared error. An extension of the proposed design to a uniform planar array is discussed. The Cramer–Rao lower bound for joint channel estimation with hybrid beamforming is derived to verify the effectiveness of the proposed design.

Terahertz (THz) communication is considered as an attractive way to overcome the bandwidth bottleneck and satisfy the ever-increasing capacity demand in the future. Due to the high directivity and propagation loss of THz waves, a massive MIMO system using beamforming is envisioned as a promising technology in THz communication to realize high-gain and directional transmission. However, pilots, which are the fundamentals for many beamforming schemes, are challenging to be accurately detected in the THz band owing to the severe propagation loss. In this paper, a unified 3D beam training and tracking procedure is proposed to effectively realize the beamforming in THz communications, by considering the line-of-sight (LoS) propagation. In particular, a novel quadruple-uniform planar array (QUPA) architecture is analyzed to enlarge the signal coverage, increase the beam gain, and reduce the beam squint loss. Then, a new 3D grid-based (GB) beam training is developed with low complexity, including the design of the 3D codebook and training protocol. Finally, a simple yet effective grid-based hybrid (GBH) beam tracking is investigated to support THz beamforming in an efficient manner. The communication framework based on this procedure can dynamically trigger beam training/tracking depending on the real-time quality of service. Numerical results are presented to demonstrate the superiority of our proposed beam training and tracking over the benchmark methods., in IEEE Transactions on Wireless Communications, 2021. arXiv admin note: text overlap with arXiv:2104.02885

The matrix enhancement and matrix pencil (MEMP) has been used to reduce the number of array elements in the planar array with a shaped-beam pattern. This work extends the MEMP-based synthesis method to the synthesis of pattern reconfigurable sparse planar arrays in wide angle scanning and wide-band, which exploits a necessary restriction on finding the common element positions and individual element excitations for multiple patterns. Moreover, a unitary transformation is exploited into the multiple-composite equivalent Hankel matrix, which converts the complex computations of singular value decomposition (SVD) and eigenvalue decomposition (EVD) courses into real ones and effectively reduces the computation complexity. A new tactic is also restricted on avoiding too small element spacing with the consideration of the size of array antenna element. A set of numerical experiments is provided to validate the effectiveness and advantages of the proposed method.

Conventional ultrasound probes have inherent limitations in some application scenarios. Because of their fixed shapes and sizes, they can hardly pass through small orifices to examine the target closely. With the recent development of micro-electro-mechanical systems (MEMS) and flexible electronic technologies, collapsible and self-assembling devices are being considered as a potential approach to address this problem. This paper presents an innovative transformable ultrasonic transducer (TUT), which can transform between a 1D linear array and a 2D planar array with the ability of switching between different functions including 2D/3D imaging and high-intensity focused ultrasound beamforming. The transforming process is accomplished with a specially designed flexible printed circuit board inspired by paper-folding mechanism. The electrical and acoustic properties of the developed transducer along with the acoustic field distribution in its different forms have been characterized. The 2D/3D imaging performances of the TUT have been also evaluated under 1D and 2D configurations, respectively. In addition, the performance of a focused concave transforming mode has been also discussed. The evaluation results suggest that TUT can maintain its performance under different structures with precise transforming control. The TUT device has the potential to considerably expand the applications of industrial and clinical ultrasound.

In this paper, we present a hybrid channel estimation algorithm for uniform planar array (UPA)-assisted millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) internet of things (IoT) systems by exploiting the benefits from both the compressed sensing (CS) and the sparse Bayesian learning (SBL). Compared with existing studies, the distribution characteristics and correlations between propagation paths in the elevation (e)-and azimuth (a)-angle domains are considered to enhance the estimation performance. Specifically, we first re-define the e-angles and the a-angles to simplify the system model. Then, a novel autoregressive (AR)-Gaussian channel prior is proposed to capture both the sparsity and the clustering properties of mmWave massive MIMO IoT channels. After that, we provide a channel approximation method to overcome the channel uncertainty by exploiting the structure of the AR-Gaussian channel prior. The hybrid beamforming architecture with limited radio frequency (RF) chains in mmWave IoT systems is also considered. Finally, we propose a hybrid channel estimation algorithm which consists of two stages. Based on the different distribution characteristics in different angle domains, the CS-based channel estimation is performed for e-angles on stage one, while the SBL-based channel estimation is applied for a-angles on stage two. Numerical results reveal that, compared with the existing CS-and SBL-only methods, the proposed hybrid channel estimation algorithm exhibits better performance in terms of computational complexity, sparsity robustness, and estimation accuracy.

The space-time adaptive processing (STAP) technique has achieved good clutter suppression performance for a side-looking airborne radar; however, it suffers from severe performance degradation in nonside-looking airborne radar. This is because the clutter distribution varies considerably with range. The 3D STAP can provide better performance compared with the traditional STAP methods in such a nonstationary clutter environment, but it requires high computational complexity and sample support. In this letter, the characteristic of azimuth-elevation-Doppler 3-D beam pattern for the planar array is explored, and a novel reduced-dimension scheme combined with this characteristic is proposed. We further developed three basic reduced-dimension structures according to this scheme. Furthermore, we also prove that the first and third structures are just the direct extension of the traditional 2-D generalized multibeam and joint-domain localized methods. The second one is an original structure that performs local reduced-dimension processing with three orthogonal planar windows. Numerical results verify that the proposed structures have significant advantages in reducing computational load and training sample numbers compared with existing 3D STAP methods. In particular, the second structure shows the best comprehensive performance in the bad samples environment.

In this letter, we explore the feature of coprime linear array (CLA) motion for enhanced array aperture and higher degree of freedom (DOF) by implementing synthetic aperture processing. Since the traditional fixed CLA cannot achieve two-dimensional (2-D) DOA estimation, we propose a novel approach to construct the virtual coprime planar array (VCPA) just by the CLA motion. Specifically, the CLA moves along the vertical direction at a constant velocity and the two subarrays of CLA are controlled to snap at different time delays related to the coprime number of sensors, by which the received signals at different time delays are synthetically processed to construct a VCPA. Simulation results demonstrate that: a) our proposed approach outperforms the synthetic CLA moving along the direction of layout of the initial arrays on DOF and the dimension of DOA estimation; b) the proposed VCPA can achieve 2-D DOA estimation just by the motive CLA and achieve no worse performance than the practical coprime planar array but by using fewer sensors.

Mobile wireless data collection in beyond 5G (B5G)/6G cellular internet of things (IoT) networks can be achieved by unmanned aerial vehicle (UAV) with high flexible mobility and low cost. In this paper, a half-duplex UAV relay is exploited to improve the achievable rate of downlink millimeter-wave (mmWave) massive multi-user multiple-input and multiple-output (MU-MIMO) networks. To improve the spectrum efficiency and mitigate the inter-beam interference, the hybrid beamforming (HB) designs of the base station (BS), the UAV and multiusers are taken into account simultaneously. To maximize the achievable rate from the BS to the multiusers, a two-stage design approach which tries to design the two stages jointly by avoiding the loss of information is proposed. Assuming multiusers are alignment in different beams, the strong effective channel-based non-orthogonal multiple access (NOMA) in the power domain is employed to assign power efficiently for each user, thereby increasing the number of users. Simulation results validate the proposed HB-NOMA approach can achieve good achievable rate performance in both uniform linear array and uniform planar array.

Adaptive beamformers at the element level usually require a great number of training samples and the computational cost for calculating the weight vector for large phased array antennas is very high, which make it difficult for real‐time applications. To address this problem, a two‐dimensional (2‐D) adaptive beamformer applicable to large planar array antennas that have low computational complexity and low training sample requirement is proposed. In the proposed method, the weight matrix is first reconstructed as a matrix that has the same or close columns and rows by utilising the special Kronecker property of the array steering matrix. Then, the weight vector is determined by adopting a bi‐quadratic cost function and a bi‐iterative algorithm. Experimental results show that the proposed method can achieve fairly good performance even when the training samples are small.

The development of real-time 3-D underwater imaging systems with large planar arrays involves high hardware costs and great computational burden. This study proposes the concept of fine-grid sparse arrays and the pruned distributed and parallel subarray (P-DPS) beamforming algorithm to achieve 3-D narrowband underwater imaging systems with low complexity. Fine-grid sparse arrays refine traditional half-wavelength grids to improve the freedom of the sparse optimization process and reduce the number of active elements. However, grid refinement increases the computational load of conventional fast beamforming algorithms. To solve this problem, a new pruned fast Fourier transform is proposed to eliminate all the redundant operations in the P-DPS beamforming, which is suitable for fine-grid sparse arrays. The computational load of the P-DPS beamforming seldom increases with grid refinement. The validity of the proposed method is verified with a fine-grid sparse planar array designed in the study. The computational load is then analyzed and compared with those of other methods. Results show the notable improvements in array sparsity rate and computational efficiency relative to those in the literature.

A joint processing of direction of arrival (DOA) and signal separation for planar array is proposed in this paper. Through sensor array processing theory, the output data of a planar array can be reconstructed as a parallel factor (PARAFAC) model, which can be decomposed with the trilinear alternating least square (TALS) algorithm. Aiming at the problem of slow speed on convergence for the standard PARAFAC method, we introduce the propagator method (PM) to accelerate the convergence of the TALS method and propose a novel method to jointly separate signals and estimate the corresponding DOAs. Given the initial angle estimates with PM, the number of iterations of TALS can be reduced considerably. The experiments indicate that our method can carry out signal separation and DOA estimation for typical modulated signals well and remain the same performance as the standard PARAFAC method with lower computational complexity, which verifies that our algorithm is effective.

We consider a single-cell massive multi-input multi-output (MIMO) network with uniform planar array (UPA) antennas equipped at the base station that serves a number of single-antenna users. In the overloaded multi-user setting, it is likely that users’ channels are highly spatial-correlated with overlapping spectrum in the angular domain, which imposes challenges on uplink channel estimation and data transmission due to potential pilot contamination during uplink training and multiuser interference during uplink data transmission. To mitigate the effect of multiuser channel spatial correlation, we adopt a recently proposed active channel sparsification strategy, and propose a novel method for joint user and beam selection in the angular domain. In particular, we represent all users’ channels in the angular/beam domain, taking advantage of the doubly block Toeplitz structure of the channel covariance matrix for UPA. Accordingly, we construct a weighted bipartite graph to represent the beam and user association for ease of user/beam selection. By doing so, we reformulate the problems of mean square error minimization for uplink channel estimation and sum rate maximization for uplink data detection as two mixed integer linear programs (MILPs), by which the challenging joint user and beam selection problem can be efficiently solved via off-the-shelf MILP solvers. The simulation results demonstrate the effectiveness of our active channel sparsification strategy for the joint user and beam selection.

Synthesis of sparse arrays is a promising area of research for a wide range of applications including radar and millimeter-wave wireless communication. The design goal of array thinning problems is to reduce the number of elements of an array without significantly affecting its performance. This work presents a technique for synthesizing a sparse phased-array antenna from a 16×16 uniform rectangular array (URA). The proposed approach reduces the number of elements by 50% without any significant increase in the peak sidelobe level (PSLL) for all possible scan angles in the azimuthal and elevation plans within a finite range of scan angles. The synthesis includes an artificial neural network (ANN) model for estimation of the excitation weights of the URA for a given scan-angle. The weights of the sparse array are computed by the Hadamard product of the weight matrix of the URA with a binary matrix that is obtained using particle swarm optimization (PSO) to minimize the PSLL.

This article investigates using a phased linear antenna array instead of the planar array to circumvent the problem that two frequency squint steering main beams cannot cover any two beam directions simultaneously. First, we approximate the donut-shaped main beam of the linear array by means of multiple pencil-shaped main beams of a virtual planar array for matching the steering main beam of the linear array with the multi-path sparse scattering channel model mathematically and give a method for calculating the number of antenna elements of the virtual array. Second, we cope with possible inter-user interference on a single squint main beam of the linear array in some scenarios by means of the power-domain non-orthogonal multiple access (PD-NOMA) technique, making it possible to support communication with two users on a single squint main beam at the base station (BS) side. The feasible domain of PD-NOMA is given when a single antenna is used for both the BS and the user end, assuming a two-user successive interference cancellation (SIC) decoding power ratio limit. Third, three algorithms are given for serving multi-user at the BS via squint beams of the linear array. Finally, numerical results show that the second proposed algorithm supporting PD-NOMA pairing within a single donut-shaped squint main beam significantly increases the number of simultaneous users served within a single cellular system.

In this paper, an antenna synthesis process is investigated for Intelligent Transportation System (ITS) application. The wireless communication between Road Side Units (RSUs) and On Board Units (OBUs) located in the vehicles enables smart mobility. However, to support high speed vehicles and high data rates, a wider coverage area by the RSU antennas projected on the highway is required. This is defined by the radiation patterns of the RSU antennas. On a multi-lane highway, interference from the adjacent lanes is a critical issue which determines the communication reliability in terms of Single-to-Interference plus Noise Ratio (SINR). Hence, in this paper, an interference-aware antenna synthesis process is proposed and a planar array is optimized based on the objective function of obtaining a wide coverage area in terms of SINR distribution for a given modulation scheme. The proposed antenna array enhances the reliable communication area and provides improved SINR distribution across the lane for misaligned vehicles from the center of the lane. The results indicate that the proposed antenna array performs better than the existing designs in terms of communication reliability.

Modern synthetic aperture radar (SAR) systems for Earth observation from space employ innovative hardware concepts. The key idea is to digitize the output of a multielement antenna almost immediately after the receiver and to dynamically process these data either onboard the radar satellite in real time or on the ground. This article addresses the performance of such digital beamforming (DBF) systems in the presence of phase and magnitude errors in the digital channels. For this, analytic expressions for the sensitivity and range ambiguity performance are derived. These equations are kept general so that they are valid for both planar array antennas and array-fed reflector antennas. It is an important objective of this article to compare these two antenna types to each other. A major conclusion from this analysis is that direct-radiating phased arrays are inherently more susceptible to random phase and magnitude errors compared with array-fed reflector antenna-based systems. This manifests itself in a more rapid degradation of the imaging performance with phased array antennas.

Hybrid precoding achieves a compromise between the sum rate and hardware complexity of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, most prior works on multi-user hybrid precoding only consider the full-connected structure. In this paper, a novel multi-user hybrid precoding algorithm is proposed for the sub-connected structure. Based on the improved successive interference cancellation (SIC), the analog precoding matrix optimization problem is decomposed into multiple analog precoding sub-matrix optimization problems. Further, a near-optimal analog precoder is designed through factorizing the precoding sub-matrix for each sub-array. Furthermore, digital precoding is designed according to the block diagonalization (BD) technology. Finally, the water-filling power allocation method is used to further improve the communication quality. The extensive simulation results demonstrate that the sum rate of the proposed algorithm is higher than the existing hybrid precoding methods with the sub-connected structure, and has higher energy efficiency compared with existing approaches. Moreover, the proposed algorithm is closer to the state-of-the-art optimization approach with the full-connected structure. In addition, the simulation results also verify the effectiveness of the proposed hybrid precoding design of the uniform planar array (UPA).

Assuming millimeter wave (mmWave) systems, we consider an analog beamforming-based physical layer security (PLS) technique, which has considerably lower complexity compared to the digital beamforming-based PLS. Motivated by the limitations of the related prior arts assuming a uniform linear array (ULA), we employ a uniform planar array (UPA) to take advantage of both azimuth and elevation domains to realize three-dimensional (3D) beamforming. Also, in contrast to the approximate analysis on the secrecy rate (SR) in the existing studies, we derive the exact SR without any approximation. Based on this analysis, we analyze the optimal size of random antenna subset, which is the key parameter to maximize the SR.

In this paper, we investigate user positioning in massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems where the base station (BS) is equipped with a uniform planar array (UPA). Taking advantage of the UPA geometry and wide bandwidth, we advocate the use of the angle-delay channel power matrix (ADCPM) as a new type of fingerprint to replace the traditional ones. The ADCPM embeds the stable and stationary multipath characteristics, e.g., delay, power, and angles in the vertical and horizontal directions, which are beneficial to positioning. We further exploit the sparsity of the ADCPM to reduce the noise contamination in the ADCPM. Taking ADCPM fingerprints as the inputs, we propose a novel three-dimensional (3D) convolution neural network (CNN) enabled learning method to localize the 3D positions of the mobile terminals (MTs). In particular, such a 3D CNN model consists of a convolution refinement module to refine the elementary feature maps from the ADCPM fingerprints, three extended Inception modules to extract the advanced feature maps, and a regression module to estimate the 3D positions. By intensive simulations, the proposed 3D CNN-enabled positioning method is demonstrated to achieve higher positioning accuracy than the traditional searching-based ones, with reduced computational complexity and storage overhead, and robust to noise contamination.

Effective cost reduction is the primary task of the commercialization of phased array antenna technology. In this communication, a novel array structure composed of irregular and overlapped subarrays is proposed. The advantages of two kinds of subarrays are integrated to achieve the performance requirements while reducing the cost. The basic idea is departing the array into a multilayer hierarchical structure. The first layer is subtly designed by irregular subarray tiles. In this case, the number of active devices can be effectively reduced, thereby reducing the cost. The second layer is an overlapped subarray structure which is able to ensure scanning performance. Both limited field of view and wideband wide-angle scanning applications are discussed to show the compatibility of the proposed structure. The effectiveness is verified by various numerical results.

This paper investigates the effect of antenna array configurations on propagation environment in massive-multiple-input multiple-output (M-MIMO) cellular system with line of sight (LoS) channels. In this study, uniform linear array (ULA) for two-dimensional (2D) and uniform planar array (UPA) for three-dimensional (3D) antenna configurations using multi-cell minimum mean squared error (M-MMSE) detection technique are considered for the uplink scenario. For serving user equipments (UEs) particularly in the azimuth domain, the simulation results shows that the system deploying horizontal uniform linear array (HULA) outperforms in terms of spectrum efficiency (SE) among considered array configurations with the same number of antennas. It is observed that ULA favors the propagation to UEs distributed in the respective domain of 2D plane, and UPA performs better for 3D plane to capture the entire propagation environment than linear ones due to its capability of receiving incident waves from both elevation and azimuth domains.

The two-dimensional (2D) grid-free compressive beamforming based on atomic norm minimization is a promising solution for estimating the source direction-of-arrival and quantifying the source strength. However, the existing method is limited to measuring with rectangular grid arrays. To overcome this limitation, a 2D grid-free compressive beamforming strategy for arbitrary planar array geometries is proposed, where the microphone pressure is transformed into a representation of 2D Fourier series expansion. The term truncation method of Fourier series polynomials is established. The effectiveness and feasibility of the proposed method in the acoustic source identification are preliminarily verified by an experimental case, and topics worthy of further study are discussed based on the numerical simulations.

In order to generate volumetric underwater acoustic images, planar arrays are often used to collect the signals coming from a 3-D scene in underwater environment. These acquired signals are commonly processed using the 2-D conventional beamforming method. The 2-D conventional beamforming is robust and the most commonly used method. However, the 2-D conventional beamforming suffers from fat beams and high-level sidelobes. High-resolution beamforming, based on the inverse of the signal covariance matrix, such as minimum-variance distortionless response, produces narrow beamwidth and low sidelobe levels but is sensitive to signal mismatch and requires many snapshots of data. In order to overcome the above-mentioned limitations, this paper applies a 2-D deconvolution algorithm to the 2-D conventional beamforming result of planar array in order to improve the beamwidth and suppress the high-level sidelobes. The 2-D deconvolved beam power produces narrow beams, and low sidelobe levels without compromising the robustness of 2-D conventional beamforming. The 2-D deconvolution algorithm provides higher output signal-to-noise ratio than the 2-D conventional beamforming for isotropic noise. The vector extrapolation method is improved to make it applicable on two-dimensional data. The detailed implementations of the original 2-D deconvolved beamforming and the efficient 2-D deconvolved beamforming are presented. Simulations and experiments are conducted to validate the performance of the proposed method.

A novel approach for the gain-phase error calibration with a large-scale planar array is proposed in this letter. We show that minimizing the direction-of-arrival (DOA) estimation error is asymptotically equivalent to minimizing the sample second moment of the phase error. Therefore, with the mixing matrix obtained by blind signal separation, the 2-D DOA is estimated by finding the minimum point of a binary function with a large-scale planar array. Subsequently, the gain-phase error is obtained using the estimated DOAs. The proposed noniterative method requires neither prior calibration information nor the DOA of the signal source, and one incident signal is sufficient for the joint estimation of DOAs and gain-phase errors. Finally, the performance of the proposed approach is evaluated via numerical simulations.

In this article, low-sidelobe cavity-backed slot antenna array with simplified feeding structure for vehicular communication is proposed. Conventional low-sidelobe antennas were realized using power dividing network to obtain the in-phase and nonuniform-amplitude excitation, which causes complicated antenna's structure and design. To tackle these challenges, a simplified feeding method without using power dividing network is introduced to design the low-sidelobe slot antenna array. The slots at the top walls serve as the radiation elements, and the slots are directly fed by the electric field of the cavity mode. Nonuniform amplitude is obtained by simply modifying the slots’ sizes and positions, while the in-phase excitation of the elements is remained. The nonuse of power dividers brings out a simpler antenna structure than conventional full-metal waveguide-based antennas. Besides, the full-metal structure introduces a high power-handling capacity. Then, two linear arrays with 1 × 4, 1 × 7 elements and a planar array with 5×4 elements are presented to show the design feasibility. Finally, the 5 × 4 antenna array is fabricated and measured, which can achieve 18.2 dBi gain, −20 dB sidelobe level, 94% radiation efficiency, and -42 dB cross-polarization. Good agreement between measurement and simulation verifies the feasibility of the proposed design concept.

Because of the multiplication of antennas, harmonic beamforming based on the time-modulated planar array (TMPA) requires a much longer modulation period than the time-modulated linear array (TMLA). Two additional constraints should also be fulfilled: 1) avoid aliasing in the frequency domain; the modulation frequency should be larger than the transmitted signal’s bandwidth and 2) accurately extract harmonics; the bandpass filter (BPF) bandwidth should be no less than the modulation frequency. If the bandwidth of the transmitted signal increases, the processor and radio frequency (RF) switch should be accordingly improved, thus increasing the overall costs. In this article, we propose a method based on partition subarrays for TMPA harmonic beamforming. Upon modulating subarrays separately and simultaneously, we obtain an equivalent beam. In our scheme, a TMPA is divided into S subarrays, such that the modulation frequency and the maximum bandwidth of the transmitted signal can be S times the original ones, making filtering much easier. Our approach has three major advantages: 1) it greatly reduces the requirement of BPF by enlarging the frequency interval; 2) it allows the transmission of larger bandwidth signals with the same hardware; and 3) the efficiency of the antenna is improved. Numerical simulations verified the proposed method.

An adaptive beamformer is effective at suppressing interference and noise. However, when the desired signal component is included in the covariance matrix, the beamformer performance becomes seriously degraded. Moreover, while the linear array has been actively researched, few studies have focused on the planar array. In this paper, an adaptive beamformer with more accurate reconstruction of the covariance matrix for a planar array is therefore proposed. The reconstruction is based on the Bayesian compressive sensing (BCS) theory. First, the directions of arrival (DOA) estimation of interferences are conducted. This problem is transformed into that of finding the minimum number of DOAs with a nonzero input because the array output is known. Accordingly, it can be converted into a probabilistic framework using the BCS technique. Then, the interference plus noise covariance matrix is reconstructed by using the DOAs of the interferences and the Capon spatial spectrum estimator. The reconstruction matrix is more accurate than other methods that directly use a data sampling matrix. Further constraints are then added to control the side-lobe level of the beam pattern of the proposed beamformer. Our numerical results confirm the effectiveness of the proposed method in terms of interference suppression, robustness to mismatch errors, and effective side-lobe-level control.

The purpose of this work is to investigate three antenna designs using the main element that provides radiation pattern with diminish side lobes and high gain of interest for the fifth generation. Antenna designs comprises: (1) fully filled 8 × 8 planar array (2) optimized thinned planar array and (3) optimized tapered planar array. The main motive is to inactive a few number of elements of a fully filled 8 × 8 array while keeping the gain and return loss undisturbed ensuring the high reduction in sidelobe levels. GA (Genetic Algorithm) is tested to estimate the optimized amalgamation of inactive and active elements corresponding to reduce sidelobe levels. Optimization techniques such as the Thinning and Tapering of the planar array are considered in this study. The performances of all three types of optimized planar antenna arrays which are designed on RT/Duroid 5880 dielectric are investigated through a finite element study using CST MW Studio ver.2018 software operating at the desired 28 GHz frequency. By comparing with a fully filled planar array of equal size, the simulation results unveil that optimized thinned and tapered planar arrays show better performance in terms of future generation aspects.

A hybrid efficient optimizing method of wide scanning sparse planar array involving mutual coupling is investigated in this letter. The former work of matrix enhancement and matrix pencil (MEMP)-based synthesis method is extended to the design of pattern reconfigurable sparse arrays in wide-angle scanning. The genetic algorithm was utilized to further optimize the reconstructed sparse array including the actual antenna element, which differs from the only array factor synthesis of MEMP. To improve the optimization efficiency and design accuracy, the space mapping technique was exploited to accomplish the optimizing design, which also involves the interactions among elements during the optimizing process. A wide-scanning sparse array can be realized by the new relationship of parameter mapping courses between low-cost coarse model optimizations and high-accuracy fine model validations based on the characteristic mode and multilevel fast multipole algorithm. Numerical examples show the efficiency of the proposed method.

Array diagnosis is an important tool for detecting and correcting array antenna failures. In this letter, a high-precision planar array diagnosis method based on the Bayesian compressive sensing (BCS) theory is proposed. The model of a near-field signal with a spherical wavefront is used to acquire the measured data. Then, the difference between the beam pattern of the reference array and the array under test is obtained. The array diagnosis problem involves finding the difference between the weights of the reference and of the array under test with known differences between patterns. This problem is reformulated in a Bayesian compressive sensing framework and can be efficiently solved using a fast relevance vector machine. Numerical results confirm the superiority of the proposed method in terms of diagnostic accuracy and computational efficiency than those in previous studies.

The challenging problem to realize high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging is the ambiguity suppression in the azimuth and range directions. According to the spatial angle difference of each ambiguity component, the current technical approach is to design the spatial filter for achieving the ambiguity suppression based on the 2-D multichannel system. Along with the increasing of HRWS imaging requirements, the number of system channels also gradually increase and further result in the complex structure design of the phased array antenna system. Meanwhile, the traditional phased array antenna cannot effectively control the direction of the transmit beampattern in range. Unlike the traditional phased array, frequency diverse array (FDA) employs a small-frequency increment across the whole array elements and forms the range-angle-dependent S-shaped transmit beampattern, which can be utilized to separate the different range ambiguous region. Considering the above-mentioned characteristics and the range periodicity problem of transmit beampattern, this letter devises a scheme for spaceborne SAR HRWS imaging mode in the view of transmit beampattern utilizing 2-D planar array, i.e., the FDA in azimuth for removing the range nonperiodicity ambiguity and the conventional phased array in elevation for removing the range periodicity ambiguity. Simulation results have been presented to validate the effectiveness of the proposed scheme.

Temperature monitoring plays a central role in improving clinical effectiveness of adjuvant hyperthermia. The potential of magnetic resonance thermometry for treatment monitoring purposes led to several MR-guided hyperthermia approaches. However, the proposed solutions were sub-optimal due to technological and intrinsic limitations. These hamper achieving target conformal heating possibilities (applicator limitations) and accurate thermometry (inadequate signal-to-noise-ratio (SNR)). In this work, we studied proof of principle of a dual-function hyperthermia approach based on a coil array (64 MHz, 1.5 T) that is integrated in-between a phased array for heating (434 MHz) for maximum signal receive in order to improve thermometry accuracy. Hereto, we designed and fabricated a superficial hyperthermia mimicking planar array setup to study the most challenging interactions of generic phased-array setups in order to validate the integrated approach. Experiments demonstrated that the setup complies with the superficial hyperthermia guidelines for heating and is able to improve SNR at 2-4 cm depth by 17%, as compared to imaging using the body coil. Hence, the results showed the feasibility of our dual-function MR-guided hyperthermia approach as basis for the development of application specific setups.

In this paper, vertical motions of sparse linear arrays (SLAs) are utilized to generate equivalent synthetic planar arrays for two-dimensional (2D) direction-of-arrival (DOA) estimation. The proposed array geometry named vertical synthetic planar array (VSPA) consists of an arbitrary SLA on the x-axis and a series of its time-shifted arrays in the vertical orientation. With the original linear array and the moving trail known, the difference coarray of VSPA can be easily obtained. By utilizing both the synthetic aperture processing and the coarray technique, VSPA has the ability to construct a synthetic planar array with only an SLA used. Compared with the traditional sparse planar arrays (SPAs), such as 2D nested array and 2D coprime array, VSPA can achieve higher degree-of-freedom and improved 2D DOA estimation performance with the same number of sensors. Moreover, as different original linear arrays and moving trails can be chosen for different applications, the construction of VSPA is of high flexibility. Numerical simulations are presented to verify the superiority of the proposed VSPA geometry over other typical SPAs.

A uniform linear array (ULA) or a multi-element planar array composed of uniform linear arrays is the main applied research array for direction-of-arrival (DOA) estimation in array signal processing. In recent years, the uniform circular array (UCA) is getting more attention because of its consistent azimuthal beam resolution, but the DOA estimation algorithm of a UCA is complex and difficult to implement. To solve this problem, a UCA positioning method based on virtual interpolation and subarray rotation (VISAR) is proposed. This method effectively reduces the computational complexity of DOA estimation for a UCA, and the simulation analysis of the algorithm shows that in terms of positioning performance, the proposed algorithm is better than the existing virtual interpolation algorithms in the case of a low signal-to-noise ratio (SNR).

In this article, a novel 3-D image reconstruction method was proposed for planar array ECT by combining depth estimation and sparse representation. The central depth of target in the region of interest is first estimated from an image initially reconstructed by the Tikhonov regularization. Second, sparse representation is employed to depict the permittivity distribution using the Gaussian radial basis functions defined at voxels over the plane in the estimated depth. Then, the 3-D image is reconstructed by solving a dedicated optimization problem using the Gauss–Newton method. The proposed method reduces the dimension of the unknowns, improves the condition number of the inverse problem, and enhances the image quality greatly. Numerical simulations and experiments were carried out to validate the proposed method. Results show that the proposed method can provide more accurate 3-D images of single or multiple inclusions at varying depths than conventional Tikhonov, total variation (TV), and Landweber methods while relieving the adverse effect of the nonuniform sensitivity of planar array ECT with varying depths on image reconstruction. The proposed method is of great potential in detecting and imaging subsurface abnormities.

This article presents a novel optimization algorithm to design a probability density taper for large array thinning. This novel algorithm is based on the iterative Fourier technique (IFT) integrated with an innovative adaptive learning mechanism and denoted as the probability learning IFT (PLIFT) here. With successive forward and backward Fourier transforms, the traditional IFT can be capable of thinning large arrays with high convergence rates, but easily get trapped in local optima. The proposed PLIFT utilizes the probability estimation to describe the elements with possible states of “ON” and “OFF” in a thinned array. A probability model is then adopted to determine the locations of elements in the thinned array featuring minimum sidelobe level. In this PLIFT, on the one hand, the probability model of density taper is learned in optimization process and gradually trained to approach the global optima. With the previous information being honored, the probability model taper proves to be reliable and effective in generating diverse competitive seeds, which, in turn, helps training an optimal probability distribution to best-fit the design goal. Several representative examples of large planar arrays thinning are provided to demonstrate the validity, highlighted global searching ability and increased robustness of the PLIFT.

In order to solve the problem that the gridless DOA estimation algorithms based on generalized finite rate of innovation (FRI) signal reconstruction model are not suitable for two-dimensional DOA estimation using planar array, a separable gridless DOA estimation algorithm exploiting bi-orthogonal sparse linear array (BSLA) structure is proposed in this article, which is called 2D-SGFRI. The 2D-SGFRI algorithm firstly recovers the covariance data of the virtual array formed by BSLA through the matrix completion method, so as to obtain the complete covariance data vectors about two independent parameters respectively. Next, since the covariance data vector satisfies the constraints of annihilation filter equations, the generalized FRI signal reconstruction model can be utilized to retrieve DOA from the covariance data vector. Compared with the existing DOA estimation algorithms based on generalized FRI signal reconstruction model, the 2D-SGFRI algorithm can be can be effectively applied to two-dimensional DOA estimation, and can obtain stable estimation results. At the same time, due to the reduction of the dimension of positive semidefinite matrix, the 2D-SGFRI algorithm can significantly reduce the computational complexity compared with the two-dimensional DOA estimation algorithms based on atomic norm minimization (ANM). A series of simulation experiments are shown to verify the effectiveness and superiority of 2D-SGFRI algorithm.

Focusing of an area array camera is an important step in making a high precision imaging camera. Its testing method needs special study. In this paper, a method of camera focusing is introduced. The defocusing depth of camera is calculated by using the frequency spectrum of defocused image. This method is especially suitable for the focusing of the Planar Array Camera, and avoids the complicated work of adjusting the focus plane of the planar array camera in the focusing process.

In production testing, it is of importance to measure the key radiation parameters of an antenna under test (AUT), e.g., main beam peak and direction, sidelobes, and null depth and direction in a cost-effective setup with a short measurement time. As a result, practical measurement setups are often compact and equipped with only a few probes (or probe locations). However, these system limitations would introduce errors for AUT antenna testing. This problem has become even more pronounced for 5G radios due to utilization of large-scale antenna configuration and high frequency bands. Spherical near-field measurements are nowadays an accurate and mature technique for characterizing AUTs, which however, necessitates a full spherical acquisition, leading to a long measurement time. Single-cut near-to-far-field transformation is a promising strategy since most of the key AUT parameters are available in the single-cut pattern and it requires much reduced measurement time. In this work, a simple and flexible scheme is proposed to evaluate errors introduced by limitations in practical setups for single-cut far-field (FF) antenna radiation pattern reconstruction, where the near-field data can be easily generated and modified according to the limitations introduced in practical multi-probe anechoic chamber setups, e.g., measurement distance, truncation range, and sampling interval. The reconstructed FF pattern is obtained using a commercial near-field to far-field transformation tool, SNIFT. The proposed scheme is numerically validated via comparing the reference FF pattern of a 4×8 uniform planar array composed of ideal Hertzian dipoles and reconstructed FF pattern. With the proposed scheme, the impact of practical system limitations on single-cut reconstruction accuracy can be easily analyzed.

In this article, we investigate physical layer security enhancement methods for millimeter-wave (mmWave) simultaneous wireless information and power transfer (SWIPT) unmanned aerial vehicle (UAV) networks, where the power-limited destination decodes the information and harvests energy from the received radio-frequency signals. Considering the effect of beamforming design and actual 3-D antenna gain, the directional modulation (DM) technique based on random frequency diverse array (RFDA) is adopted to guarantee security. The closed-form expressions of the lower bound of average secrecy rates with uniform linear array (ULA) and uniform planar array (UPA) are derived, respectively. Furthermore, based on the theoretical analysis results, we formulate the secrecy rate maximization problem subject to the energy harvesting constraint at the destination. Then, a suboptimal iterative optimization algorithm is proposed to solve the secrecy rate maximization problem by optimizing the transmit power, power splitting ratio, and UAV trajectory jointly. The simulation results show that the average secrecy rate of RFDA scheme is much larger than the conventional phase array scheme, especially when using ULA. The proposed optimization algorithm can achieve a higher average secrecy rate than other benchmark algorithms.

To reduce the adverse effects of propagation delay uncertainty caused phase error, a pragmatic self-calibration approach for millimeter-wave multiple-in multiple-out (MIMO) imaging is proposed and demonstrated. Based on the analysis of the contributions of delay uncertainty and the assumption of identical channel design, the direct coupling between array elements is used to estimate the hardware-caused inherent channel delay, requiring no additional reference aid. Of the contributions to the delay, the common part and the part due to the interchannel difference are estimated separately. Combined with the array geometrical configuration, the direct coupling reflections are averaged to find the common channel delay, and the interchannel differences are estimated in a reduced geometric average scheme. The delay uncertainty is compensated by performing a phase shift operation to the received echoes for each channel. In addition, the proposed method also allows the accommodation of the slow change of the channel inherent delay automatically and constantly. Numerical analysis and experimental tests demonstrate the projected performance improvement of the proposed scheme as an alternative approach, which can also serve as a benchmark or booster for reference aided phase calibration methods.

This article proposes a novel multistatic scaling algorithm for fast 3-D imaging with an ultrawideband (UWB) multiple-input–multiple-output (MIMO) planar array architecture. The proposed imaging method well addresses the weaknesses of the state-of-the-art imaging techniques, such as heavy computation burden and requirement of regular MIMO topologies. The key range cell migration in MIMO data set is corrected by a multistatic scaling operation. Only the fast Fourier transform and multiplications are employed during the entire imaging procedure that is easy to implement. For an arbitrary MIMO planar array, the computation complexity of the algorithm is $O({N^{5}\text {log}_{2} N})$ , which is lower than the optimum backpropagation and $\omega $ -K techniques. We have designed a $16\times16$ UWB sparse MIMO planar array with nonregular topology in our experiments. Results with both synthetic and practical data demonstrate the accuracy and efficiency of the algorithm, i.e., it obtains images of the same quality with much less time compared with the state-of-the-art other imaging algorithms.

Humanoid robots require to use microphone arrays to acquire speech signals from the human communication partner while suppressing noise, reverberation, and interferences. Unlike many other applications, microphone arrays in humanoid robots have to face the restrictions in size and geometry. To address these challenges, this paper presents an approach to differential beamforming with arbitrary planar array geometries. The major contributions of this work are as follows: (1) a method is presented to design differential beamformers, which works for regular geometries such as linear, circular, and concentric circular ones, as well as irregular geometries, as long as the sensors’ positions are given or can be measured; (2) fundamental requirements for the design of different orders of linear differential microphone arrays (DMAs), partially steerable DMAs, fully steerable DMAs, and robust DMAs are discussed; (3) the validity and limitations of the Jacobi-Anger expansion approximation is analyzed, where we discuss how to achieve an optimal approximation by properly choosing the reference point; and (4) we show how to design an Nth-order DMA with 2N microphones using the Jacobi-Anger expansion.

In this study, a multi-polarised two-dimensional (2D) planar array of sparsely located vector antennas (VAs) is designed for the block sparse representation (SR)-based 2D direction finding and polarisation parameter estimation of wideband signals. In order to alleviate the inter-VA mutual coupling effect, the minimum inter-VA spacing of the 2D sparse array is constrained to be no less than one wavelength that corresponds to the highest signal frequency. To reduce the computational complexity of parameter estimation, the 2D block SR model for the 2D difference coarray output at a certain frequency bin is established, under which the two direction cosine terms for 2D direction-of-arrival (DOA) estimation are decoupled with the polarimetric terms. This enables separated but simultaneous 2D direction finding and polarisation parameter estimation with the newly developed joint 2D block orthogonal matching pursuit (Joint-2D-BOMP) subband fused sparse recovery algorithm. Moreover, with the use of two spatial only (polarisation independent) over-complete dictionaries, the representation dimension of the new 2D block SR model is greatly reduced as compared with the traditional space-polarisation joint SR model. The efficacy of the presented VA array geometry and the associated parameter estimation method is validated by computer simulations.

In this article, a densely integrated and unified paralleled amplifier and antenna architecture is proposed and demonstrated for next-generation millimeter-wave (mmW) and terahertz (THz) front-end modules. It makes use of topologically paralleled transistors cohabitated within a compact rectangular patch antenna (CRPA) as a unified cell. This design simultaneously amplifies and radiates power without resorting to any power divider, input and output matching networks, or power combiner whatsoever, therefore, greatly reducing front-end loss, size, and complexity. Then, the unified unit cell is self-consistently distributed to form a unified planar array with active elements between array elements as power feeders. In this demonstration, the input power is sequentially amplified, radiated through each array element, and spatially added up, resulting in low on-board circuit losses and efficient radiation. In this article, we investigate and present challenges and early results of implementing this unification concept of active circuitry and antenna at mmW frequencies. Using a standard 65-nm CMOS process, a set of chips were designed, fabricated, and tested under different amplifier biasing conditions at 146 GHz for experimental demonstration. The realized low-loss, matched, and compact unified prototypes have demonstrated an amplified radiation, with power enhancement of 3.4 dB through single element and 6 dB through $2 \times 2$ layout compared to their respective passive counterparts. Moreover, frequency tuning is observed varying drain bias and self-matching is improved with increasing gate bias. Therefore, the feasibility of a unique feature in the architectural implementation of low-loss, compact, and densely integrated and topologically cohabitated mmW and THz front-end modules is confirmed.

An improved gravity search algorithm, adaptive gravity search algorithm (AGSA), is proposed to solve the problem that the gravity neutralization caused by the cumulative effect of particle inertia mass at the end of iteration, which will affect the optimization performance. An adaptive decay factor is designed, which can produce different gravitation values at different iteration stages of the algorithm and accelerate the mining ability of the algorithm at the later iteration stage. In order to enhance the memory ability of the algorithm, the influence of elite particles is added to the realization of the speed to expand the exploration ability. The improved algorithm is used to optimize uniform concentric ring array, the main lobe width optimized by the AGSA is 6.7°narrower and the side lobe level is 5.1 dB and 1.8 dB lower than the algorithm in the literature. It is clear that the pattern obtained by AGSA meets the desired pattern very well. Moreover, when the number of iterations is 2 000, the fitness value of the improved algorithm is increased by 30%. It can be seen that AGSA outperforms the algorithm in the literature in evolutionary speed and accuracy. Sparse concentric ring array also has the same optimization results. The effectiveness of the proposed improved algorithm in solving the array pattern synthesis is proved.

In this letter, we develop a three-dimensional (3-D) irregular-shaped semi-ellipsoid channel model for massive multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) communication in street scattering environments. The geometric model is irregular-shaped due to the scattering environments cut by vertical structures along the roadside. With employing a uniform planar array (UPA), we first characterize the geometrical relationships between transmitter and receiver constrained by different boundaries of scattering regions in detail. Then we derive the closed-form expressions for the probability density functions (PDFs) of the angle of departure (AoD) and joint time of arrival (ToA)/AoD in azimuth and elevation planes. Moreover, we discuss the impacts of the street width and large-scale antenna array on channel statistical properties. Numerical results show that our proposed model can capture characteristics of massive MIMO V2V channels while in close agreement with the prior reported results, thereby demonstrating the efficiency of the proposed model that can provide guidance for system design and performance evaluation of massive MIMO V2V communication in street scattering environments.

Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction and multipath clutter in indoor through-wall environments. While several methods have been proposed for removing target independent static and dynamic clutter, there still remain considerable challenges in mitigating target dependent clutter especially when the knowledge of the exact propagation characteristics or analytical framework is unavailable. In this work we focus on mitigating wall effects using a machine learning based solution -- denoising autoencoders -- that does not require prior information of the wall parameters or room geometry. Instead, the method relies on the availability of a large volume of training radar images gathered in through-wall conditions and the corresponding clean images captured in line-of-sight conditions. During the training phase, the autoencoder learns how to denoise the corrupted through-wall images in order to resemble the free space images. We have validated the performance of the proposed solution for both static and dynamic human subjects. The frontal radar images of static targets are obtained by processing wideband planar array measurement data with two-dimensional array and range processing. The frontal radar images of dynamic targets are simulated using narrowband planar array data processed with two-dimensional array and Doppler processing. In both simulation and measurement processes, we incorporate considerable diversity in the target and propagation conditions. Our experimental results, from both simulation and measurement data, show that the denoised images are considerably more similar to the free-space images when compared to the original through-wall images.