Showing papers on "Adaptive beamformer published in 2020"

Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound

Abstract: In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and the contrast-to-noise ratio of the delay and sum (DAS) beamformers. Unfortunately, the performance of these adaptive beamforming approaches degrades when the underlying model is not sufficiently accurate and the number of channels decreases. To address this problem, here, we propose a deep-learning-based beamformer to generate significantly improved images over widely varying measurement conditions and channel subsampling patterns. In particular, our deep neural network is designed to directly process full or subsampled radio frequency (RF) data acquired at various subsampling rates and detector configurations so that it can generate high-quality US images using a single beamformer. The origin of such input-dependent adaptivity is also theoretically analyzed. Experimental results using the B-mode focused US confirm the efficacy of the proposed methods.

Adaptive Beamforming Design for mmWave RIS-Aided Joint Localization and Communication

Abstract: The concept of reconfigurable intelligent surface (RIS) has been proposed to change the propagation of electromagnetic waves, e.g., reflection, diffraction, and refraction. To accomplish this goal, the phase values of the discrete RIS units need to be optimized. In this paper, we consider RIS-aided millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems for both accurate positioning and high data-rate transmission. We propose an adaptive phase shifter design based on hierarchical codebooks and feedback from the mobile station (MS). The benefit of the scheme lies in that the RIS does not require deployment of any active sensors and baseband processing units. During the update process of phase shifters, the combining vector at the MS is sequentially refined. Simulation results show the performance improvement of the proposed algorithm over the random phase design scheme, in terms of both positioning accuracy and data rate. Moreover, the performance converges to that of the exhaustive search scheme even in the low signal-to-noise ratio regime.

Adaptive Ultrasound Beamforming Using Deep Learning

Abstract: Biomedical imaging is unequivocally dependent on the ability to reconstruct interpretable and high-quality images from acquired sensor data. This reconstruction process is pivotal across many applications, spanning from magnetic resonance imaging to ultrasound imaging. While advanced data-adaptive reconstruction methods can recover much higher image quality than traditional approaches, their implementation often poses a high computational burden. In ultrasound imaging, this burden is significant, especially when striving for low-cost systems, and has motivated the development of high-resolution and high-contrast adaptive beamforming methods. Here we show that deep neural networks, that adopt the algorithmic structure and constraints of adaptive signal processing techniques, can efficiently learn to perform fast high-quality ultrasound beamforming using very little training data. We apply our technique to two distinct ultrasound acquisition strategies (plane wave, and synthetic aperture), and demonstrate that high image quality can be maintained when measuring at low data-rates, using undersampled array designs. Beyond biomedical imaging, we expect that the proposed deep learning based adaptive processing framework can benefit a variety of array and signal processing applications, in particular when data-efficiency and robustness are of importance.

Deep Learning Based Hotspot Prediction and Beam Management for Adaptive Virtual Small Cell in 5G Networks

Abstract: To meet the extremely stringent but diverse requirements of 5G, cost-effective network deployment and traffic-aware adaptive utilization of network resources are becoming essential. In this paper, a hotspot prediction based virtual small cell (VSC) operation scheme is adopted to improve both the cost efficiency and operational efficiency of 5G networks. This paper focuses on how to predict the hotspots by using deep learning, and then demonstrates how the predictions can be leveraged to support adaptive beamforming and VSC operation. We first leverage the feature extraction capabilities of deep learning and exploit use of a long short-term memory (LSTM) neural network to achieve hotspot prediction for the potential formation of the VSCs. To support the operation of VSCs, large-scale antenna array enabled hybrid beamforming is adaptively adjusted for highly directional transmission to cover these hotspot-based VSCs. Within each VSC, an appropriate user equipment is selected as a cell head to collect the intra-cell traffic in the unlicensed band and relays the aggregated traffic to the macro-cell base station by using the licensed band. Our simulation results illustrate that the proposed LSTM-based method can extract spatial and temporal traffic features of hotspot with higher accuracy, compared with some existing deep and non-deep learning approaches. Numerical results also show that VSCs with hotspot prediction and hybrid beamforming can improve the energy efficiency dramatically with flexible deployment and low latency, compared with the scenario of the convolutional fixed small cells.

Maximum Entropy-Based Interference-Plus-Noise Covariance Matrix Reconstruction for Robust Adaptive Beamforming

Abstract: To ensure signal receiving quality, robust adaptive beamforming (RAB) is of vital importance in modern communications. In this letter, we propose a new low-complexity RAB approach based on interference-plus-noise covariance matrix (IPNC) reconstruction and steering vector (SV) estimation. In this method, the IPNC and desired signal covariance matrices are reconstructed by estimating all interference powers as well as the desired signal power using the principle of maximum entropy power spectrum (MEPS). Numerical simulations demonstrate that the proposed method can provide superior performance to several previously proposed beamformers.

Direction of Arrival Estimation and Robust Adaptive Beamforming With Unfolded Augmented Coprime Array

Abstract: An augmented coprime array systematically employs two sparse subarrays to produce a large-scale difference co-array with attractive merits, such as enhanced degrees of freedom (DOFs) and enlarged array aperture, whereas the interleaved subarrays are susceptible to mutual coupling. In this paper, we propose an unfolded augmented coprime array (UACA) obtained by careful crafting of small sparse subarrays to fill the holes in the difference co-array generated by unfolding operation. Specifically, UACA can significantly reduce the number of sensor pairs with small spacing and hence inherently weaken the mutual coupling effect. Meanwhile, an increase of the DOFs and improved direction of arrival (DOA) estimation accuracy can be achieved in the presence of mutual coupling. As an application of UACA, we propose a decoupled interference-plus-noise covariance matrix (INCM) reconstruction method for robust adaptive beamforming (RAB) with UACA. Therein, mutual coupling coefficients are estimated based on the remodeled contaminated steering vector and the noise subspace. The estimated mutual coupling matrix is utilized to reconstruct the decoupled covariance matrix which in turn is used to obtain refined DOA estimates, interferer power estimates, and the desired INCM. Extensive simulation results are provided to verify the effectiveness of UACA and the decoupled INCM reconstruction method for RAB.

Angle of Arrival Estimation Through a Full-Hardware Approach for Adaptive Beamforming

Abstract: This brief introduces a full-hardware approach for Angle of Arrival (AoA) estimation in phased array antennas. The system has a modular structure composed of an analog section to up/down-convert RF signals and a digital section for the AoA estimation. The estimation furnished by the proposed approach can be used to perform adaptive beamforming either to increase the range of operation and/or reduce transmitted power in IoT applications. Some preliminary experimental results are reported validating the proposed strategy for a single narrowband received signal.

The SVD Beamformer: Physical Principles and Application to Ultrafast Adaptive Ultrasound

Abstract: A shift of paradigm is currently underway in biomedical ultrasound thanks to plane or diverging waves coherent compounding for faster imaging. One remaining challenge consists in handling phase and amplitude aberrations induced during the ultrasonic propagation through complex layers. Unlike conventional line-per-line imaging, ultrafast ultrasound provides backscattering information from the whole imaged area for each transmission. Here, we take benefit from this feature and propose an efficient approach to perform fast aberration correction. Our method is based on the Singular Value Decomposition (SVD) of an ultrafast compound matrix containing backscattered data for several plane wave transmissions. First, we explain the physical signification of SVD and associated singular vectors within the ultrafast matrix formalism. We theoretically demonstrate that the separation of spatial and angular variables, rendered by SVD on ultrafast data, provides an elegant and straightforward way to optimize the angular coherence of backscattered data. In heterogeneous media, we demonstrate that the first spatial and angular singular vectors retrieve respectively the non-aberrated image of a region of interest, and the phase and amplitude of its aberration law. Numerical, in vitro and in vivo results prove the efficiency of the image correction, but also the accuracy of the aberrator determination. Based on spatial and angular coherence, we introduce a complete methodology for adaptive beamforming of ultrafast data, performed on successive isoplanatism patches undergoing SVD beamforming. The simplicity of this method paves the way to real-time adaptive ultrafast ultrasound imaging and provides a theoretical framework for future quantitative ultrasound applications.

Fast and Robust Variable-Step-Size LMS Algorithm for Adaptive Beamforming

Abstract: Conventional least-mean-square (LMS) algorithm is one of the most popular algorithms, which is widely used for adaptive beamforming. But the performance of the LMS algorithm degrades significantly because the constant step size is not suitable for varying signal-to-noise ratio (SNR) scenarios. Although numerous variable-step-size LMS (VSS-LMS) algorithms were proposed to improve the performance of the LMS algorithm; however, most of these VSS-LMS algorithms are either computationally complex or not reliable in practical scenarios since they depend on many parameters that are not easy to tune manually. In this letter, a fast and robust VSS-LMS algorithm is proposed for adaptive beamforming. The VSS is obtained based on normalized sigmoid function, where the sigmoid function is calculated by using the mean of instantaneous error first and then normalized by the squared cumulative sum of instantaneous error and estimated signal power. The proposed algorithm can update the step size adaptively without tuning any parameter and outperform state-of-the-art algorithms with low computational complexity. The simulation results show better performance of the proposed algorithm.

Low-Complexity Accurate Mmwave Positioning for Single-Antenna Users Based on Angle-of-Departure and Adaptive Beamforming

Abstract: The problem of position estimation of a mobile user equipped with a single antenna receiver using downlink transmissions is addressed. The advantages of this setup compared to the classical MIMO and uplink scenarios are analyzed in terms of achievable theoretical performance (Cramer-Rao bounds) considering a realistic power budget. Based on this analysis, a low-complexity two-step algorithm with improved localization performance is proposed, which first performs a (coarse) angle of departure estimation and then precodes the down-link signal to introduce beamforming towards the user direction. Results demonstrate that position estimation in downlink can be potentially much more accurate than in uplink, even in presence of multiple users in the system.

Aperture-Level Simultaneous Transmit and Receive With Digital Phased Arrays

Abstract: While purely digital phased arrays were once discarded as simultaneous transmit and receive (STAR) capable platforms, this notion has recently been reconsidered. Previous work demonstrated that adaptive digital beamforming and digital self-interference cancellation (SIC) can enable transmitting and receiving subapertures in an array to operate simultaneously in the same frequency band. This approach, referred to as Aperture-Level Simultaneous Transmit and Receive (ALSTAR), uses only adaptive digital beamforming and digital SIC techniques. The ALSTAR architecture does not require custom radiators or analog canceling circuits that can increase front end losses and add significant size, weight, and cost to the array. This paper extends the previously proposed effective isotropic isolation (EII) metric to account for fixed dynamic range transmit and receive channels. An alternating optimization procedure that exploits the interdependence of the transmit and receive beamformers is proposed based on the symmetry of the EII metric, achieving higher EII than in previous work. This optimization procedure balances the goal of null-placement for interference and noise rejection with the goal of maintaining high transmit and receive gain. Simulated results are presented for a $\mathbf {50}$ -element array that achieves $\mathbf {187.1}$ dB of EII in narrowband operation with $\mathbf {2500}$ W of transmit power. We explore the effectiveness of the architecture and proposed optimization methods by demonstrating the high EII achieved across the full scan space of the array at several transmit power levels. Results are also presented for a regularized version of the beamformer optimization problem that allows the designer to trade EII for array gain.

A Reinforcement Learning based approach for Multi-target Detection in Massive MIMO radar

Abstract: This paper considers the problem of multi-target detection for massive multiple input multiple output (MMIMO) cognitive radar (CR). The concept of CR is based on the perception-action cycle that senses and intelligently adapts to the dynamic environment in order to optimally satisfy a specific mission. However, this usually requires a priori knowledge of the environmental model, which is not available in most cases. We propose a reinforcement learning (RL) based algorithm for cognitive multi-target detection in the presence of unknown disturbance statistics. The radar acts as an agent that continuously senses the unknown environment (i.e., targets and disturbance) and consequently optimizes transmitted waveforms in order to maximize the probability of detection ($P_\mathsf{D}$) by focusing the energy in specific range-angle cells (i.e., beamforming). Furthermore, we propose a solution to the beamforming optimization problem with less complexity than the existing methods. Numerical simulations are performed to assess the performance of the proposed RL-based algorithm in both stationary and dynamic environments. The RL based beamforming is compared to the conventional omnidirectional approach with equal power allocation and to adaptive beamforming with no RL. As highlighted by the proposed numerical results, our RL-based beamformer outperforms both approaches in terms of target detection performance. The performance improvement is even particularly remarkable under environmentally harsh conditions such as low SNR, heavy-tailed disturbance and rapidly changing scenarios.

Joint Adaptive Beamforming Techniques for Distributed Array Radars in Multiple Mainlobe and Sidelobe Jammings

Abstract: In modern electronic warfare, array radar systems apply adaptive beamforming techniques to form nulls in the beams toward the jamming angles for multiple jamming cancellation. However, the nulls lead to distortion, especially within the mainlobe, which severely degrades the target detection ability. To address the degradation, this letter proposes joint adaptive beamforming techniques for distributed array radars by two-stage adaptive processing. First, the linearly constrained minimum variance beamforming is performed for multiple sidelobe jamming cancellation with mainlobe maintenance within each array radar. After that, joint beamspace processing with multiple radars is carried out for multiple mainlobe jamming cancellation using adaptive–adaptive algorithm. Excellent performance in both jamming cancellation and target detection is attained at very low computation and data transmission cost between multiple radar sites. Detailed evaluation and simulation results are provided to validate the proposed technique.

Robust Null Broadening Beamforming Based on Covariance Matrix Reconstruction via Virtual Interference Sources

Abstract: When jammers move rapidly or an antenna platform travels at high speed, interference signals may move out of the null width in the array beampattern. Consequently, the interference suppression performance can be significantly degraded. To solve this problem, both the null broadening technique and robust adaptive beamforming are considered in this paper. A novel null broadening beamforming method based on reconstruction of the interference-plus-noise covariance (INC) matrix is proposed, in order to broaden the null width and offset the motion of the interfering signals. In the moving case, a single interference signal can have multiple directions of arrival, which is equivalent to the existence of multiple interference sources. In the reconstruction of the INC matrix, several virtual interference sources are set up around each of the actual jammers, such that the nulls can be broadened. Based on the reconstructed INC and signal-plus-noise covariance (SNC) matrices, the steering vector of the desired signal can be obtained by solving a new convex optimization problem. Simulation results show that the proposed beamformer can effectively broaden the null width and deepen the null depth, and its performance in interference cancellation is robust against fast-moving jammers or array platform motion. Furthermore, the null depth can be controlled by adjusting the power parameters in the reconstruction process and, if the direction of interference motion is known, the virtual interference sources can be set to achieve better performance.

Self-Interference Cancellation and Channel Estimation in Multicarrier-Division Duplex Systems With Hybrid Beamforming

Abstract: Design of full-duplex (FD) wireless systems faces many challenges, including self-interference cancellation (SIC), capability to provide high capacity, high flexibility for operation, best usage of resources, etc. In this paper, we propose and investigate a multicarrier-division duplex (MDD) based hybrid beamforming system operated in FD mode, which is endowed with the advantages of both time-division duplex and frequency-division duplex. It also shares some merits of FD and allows to be free of self-interference (SI) in digital domain, but faces the same challenge of SI as the FD in analog domain. Hence in this paper, we first propose an adaptive beamforming assisted SI cancellation scheme with taking into account the practical requirement of analog-to-digital conversion (ADC). It can be shown that the proposed approach is capable of jointly coping with the desired signals' transmission and SI suppression. Then, channel estimation (CEst) in MDD/MU-MIMO system is proposed by exploiting the reciprocity between the uplink and downlink subcarrier channels that is provided by MDD. Correspondingly, the orthogonality-achieving pilot symbols are designed, and the least-square (LS) CEst as well as linear minimum mean-square error (LMMSE) CEst are derived. Finally, the performance of MDD/MU-MIMO systems employing the proposed SIC method is investigated, with respect to the SI cancellation capability, sum-rate potential, CEst performance, and the effect of CEst on the achievable performance. Our studies show that MDD/MU-MIMO provides an effective option for design of future wireless transceivers.

Multi-Stage Antenna Selection for Adaptive Beamforming in MIMO Radar

Abstract: Increasing the number of transmit and receive elements in multiple-input-multiple-output (MIMO) antenna arrays imposes a substantial increase in hardware and computational costs. We mitigate this problem by employing a reconfigurable MIMO array where large transmit and receive arrays are multiplexed in a smaller set of $k$ baseband signals. We consider four stages for the MIMO array configuration and propose four different selection strategies to offer dimensionality reduction in post-processing and achieve hardware cost reduction in digital signal processing (DSP) and radio-frequency (RF) stages. We define the problem as a determinant maximization and develop a unified formulation to decouple the joint problem and select antennas/elements in various stages in one integrated problem. We then analyze the performance of the proposed selection approaches and prove that, in terms of the output SINR, a joint transmit-receive selection method performs best followed by matched-filter, hybrid and factored selection methods. The theoretical results are validated numerically, demonstrating that all methods allow an excellent trade-off between performance and cost.

Robust Adaptive Null Broadening Method Based on FDA-MIMO Radar

Abstract: Beamforming plays an important role in the array radar anti-interference. However, the interference targets with fast speed may move out of the narrow null area of the beampattern. A null broadening method with robust adaptive beamforming for frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) systems is proposed in this paper. The projection preprocessing and windowing functions are applied to obtain stable beampatterns. Then, a novel null broadening method based on the covariance matrix reconstruction is realized by setting artificial fluctuations around each actual jammer. Finally, a new convex optimization model is established and solved to estimate the desired steering vector. Numerical simulations results verify the effectiveness of the proposed method.

Robust and steerable kronecker product differential beamforming With rectangular microphone arrays

Abstract: Differential microphone arrays (DMAs), a class of welldesigned small-size arrays combined with differential beamforming, are very useful for processing broadband acoustic, audio, and speech signals in a wide range of applications. However, most efforts in the literature so far have been devoted to linear, circular, and spherical arrays. In this paper, we consider rectangular shapes of planar microphone arrays. Instead of adopting the traditional differential beamforming methods developed in the literature, we present a differential beamforming method based on the so-called Kronecker product. We first decompose the entire rectangular array into two virtual rectangular sub-arrays so that the steering vector of the entire array is the Kronecker product of the steering vectors of the two smaller virtual rectangular sub-arrays. We use the first virtual rectangular array, which is much smaller in size than the entire array but well satisfies the basic requirements for differential beamforming, to design a steerable differential beamformer. For the second virtual rectangular array, we can design either the delay-and-sum (DS) beamformer, which helps to improve the robustness of the global differential beamformer, or an adaptive beamformer, which makes the global differential beamformer adaptive. This method has many interesting properties, particularly the designed beamformer is fully steerable, and its robustness and the array gain can be easily controlled.

Robust Adaptive Beamforming Based on Linearly Modified Atomic-Norm Minimization With Target Contaminated Data

Abstract: In practice, adaptive beamforming usually faces non-ideal situations where a limited number of snapshots are available, the training data are corrupted by desired target signals, and the array mismatches exist. Traditional methods often degrade significantly under the above situation. In order to solve this problem, a new adaptive beamforming method based on atomic-norm optimization technique is proposed in this paper. In the proposed method, the interference subspace is estimated by minimizing the rank of interference data matrix while making the signals bounded within a ball of Frobenius norm around the observed data. This non-convex problem is solved using alternative optimization which decomposes it into two iterative steps. Each step can be formulated as semi-definite programming, and solved efficiently. Unlike traditional methods, the proposed method can estimate the target signals, target directions, and interference subspace simultaneously. This property guarantees that the proposed beamformer is free from the influence of target signals, and able to adjust pointing direction adaptively. Then it is derived theoretically that the estimation of interference subspace in the proposed method is consistent, and bounded. A fast implementation algorithm based on alternating direction method of multipliers is also derived. Compared with traditional methods, the proposed method not only performs better with target-contaminated training data, and erroneous prior of target direction, but also requires much less snapshots to work. The effectiveness of the proposed method, and its advantages over traditional methods are verified based on simulated, and actual measured radar data.

A Pipelined Reduced Complexity Two-Stages Parallel LMS Structure for Adaptive Beamforming

Abstract: In this paper, we propose a reduced complexity parallel least mean square structure (RC-pLMS) for adaptive beamforming and its pipelined hardware implementation. RC-pLMS is formed by two least mean square (LMS) stages operating in parallel (pLMS), where the overall error signal is derived as a combination of individual stage errors. The pLMS is further simplified to remove the second independent set of weights resulting in a reduced complexity pLMS (RC-pLMS) design. In order to obtain a pipelined hardware architecture of our proposed RC-pLMS algorithm, we applied the delay and sum relaxation technique (DRC-pLMS). Convergence, stability and quantization effect analysis are performed to determine the upper bound of the step size and assess the behavior of the system. Computer simulations demonstrate the outstanding performance of the proposed RC-pLMS in providing accelerated convergence and reduced error floor while preserving a LMS identical $O(N)$ complexity, for an antenna array of $N$ elements. Synthesis and implementation results show that the proposed design achieves a significant increase in the maximum operating frequency over other variants with minimal resource usage. Additionally, the resulting beam radiation pattern show that the finite precision DRC-pLMS implementation presents similar behavior of the infinite precision theoretical results.

A fast and high frame rate adaptive beamforming using DCT‐based RF‐line recovery in line‐by‐line ultrasound imaging

Abstract: Ultrasound imaging is an important modality used in medical imaging. One of the significant stages in the ultrasound imaging is the beamforming process. This article proposes a new technique for reducing the overall computational time of adaptive linear ultrasound imaging. The method uses the discrete cosine transform‐based reconstruction for missing data imputation. The novelty of the paper is that we do not need to beam‐form the total scan lines, so the time of image construction can be saved significantly. In other words, a fraction of the total scan lines is selected for beamforming and the others are assumed to have values as Not‐a‐Number (NaN). The proposed reconstruction technique tries to assign appropriate values to the NaN ones. We applied the proposed method to the simulated and experimental radio frequency (RF) datasets for resolution and contrast evaluation. Results showed that the proposed technique is near to the minimum variance (MV) method in terms of resolution and contrast, and has less computational time for image formation compared to the MV. As some quantitative examples in some experiments we have formed only 50% and 33% of the total lines and reconstructed the rest, then we have been able to increase the frame rate twice and three times, respectively, which can be very useful in many applications, especially in echocardiography imaging. In addition, since the execution time of the reconstruction algorithm is not very significant, we were also able to increase the speed by two and three times while achieving an error of less than 10% compared to the case of using all image lines.

Robust adaptive beamforming for coprime array with steering vector estimation and covariance matrix reconstruction

Abstract: Coprime array exhibits many advantages over the uniform linear array (ULA) with the same number of physical sensors in resolution performance and interference suppression capability. In this study, the authors take the advantages of coprime array to improve the robustness of adaptive beamformer. In the coprime virtual ULA (CV-ULA), they prove that a constructed Toeplitz matrix can be taken as the sample covariance matrix from the perspective of virtual signal characteristics. The CV-ULA Capon spectrum estimator is modified to obtain the directions and powers of all impinging signals. Since the real directions of all impinging signals are located at different angular sectors, they form independent signal subspace for each impinging signal. They also assign independent steering vector mismatches for different impinging signals to obtain their real steering vectors. The steering vector mismatch of each impinging signal is independently obtained by solving its own convex optimisation problem. They reconstruct the interference-plus-noise covariance matrix (INCM) with precise steering vectors and powers of interference signals. The proposed weight vector is computed by combining the desired signal steering vector and the reconstructed INCM. Extensive simulations show that the proposed algorithm provides robustness against many types of model mismatches.

Improvement of performance of minimum variance beamformer by introducing cross covariance estimate.

Abstract: The delay-and-sum beamformer is widely used in clinical ultrasound systems to obtain ultrasonic images. To improve image quality, the minimum variance (MV) beamformer was introduced in medical ultrasound imaging. The MV beamformer determines beamformer weights from ultrasonic echo signals received by individual transducer elements in an ultrasonic probe. In the present study, the MV beamformer was investigated to improve its performance. In MV beamforming, a covariance matrix of echo signals received by individual elements needs to be estimated to obtain adaptive beamformer weights. To obtain a stable estimate, a total receiving aperture is divided into subarrays, and a covariance matrix is obtained using echo signals from each subarray to average covariance matrices from all subarrays. This procedure is called “subarray averaging.” In the present study, a new method for estimation of the covariance matrix was proposed. In the proposed method, a covariance matrix, namely, a cross covariance matrix, is obtained using echo signals from different subarrays. Multiple covariance matrices are obtained from all different pairs of subarrays and averaged. In the present study, the performance of the proposed method was evaluated by basic experiments on a phantom. Lateral spatial resolutions obtained by MV beamforming with conventional subarray averaging and the proposed method were similar. However, contrast obtained by MV beamforming with the proposed method was − 0.56 dB, which was significantly better than the − 5.06 dB obtained by MV beamforming with conventional subarray averaging. Image contrast in MV beamforming could be improved significantly by estimating “cross” covariance matrices.

Quadratic Matrix Inequality Approach to Robust Adaptive Beamforming for General-Rank Signal Model

Abstract: The worst-case robust adaptive beamforming problem for general-rank signal model is considered. This is a nonconvex problem, and an approximate version of it (obtained by introducing a matrix decomposition on the presumed covariance matrix of the desired signal) has been well studied in the literature. Different from the existing literature, herein however the original beamforming problem is tackled. Resorting to the strong duality of linear conic programming, the robust adaptive beamforming problem for general-rank signal model is reformulated into an equivalent quadratic matrix inequality (QMI) problem. By employing a linear matrix inequality (LMI) relaxation technique, the QMI problem is turned into a convex semidefinite programming problem. Using the fact that there is often a positive gap between the QMI problem and its LMI relaxation, an approximation algorithm is proposed to solve the robust adaptive beamforming in the QMI form. Besides, several sufficient optimality conditions for the nonconvex QMI problem are developed To validate our results, simulation examples are presented, which demonstrate the improved performance of the new robust beamformer in terms of the output signal-to-interference-plus-noise ratio.

Robust Wideband Adaptive Beamforming Based on Focusing Transformation and Steering Vector Compensation

Abstract: The wideband adaptive beamforming method would deteriorate severely when the desired signal is contained in the training snapshots, especially the mismatched signal steering vector existed simultaneously. Therefore, a robust wideband adaptive beamforming method is proposed based on focusing transformation and steering vector compensation. In proposed method, the received wideband signals are first decomposed into narrow sub-bands. Then, based on the direction of arrivals of signals, the interference plus noise covariance matrix of each sub-band is reconstructed to remove the desired signal and transformed into the reference frequency by the focusing transformation. Subsequently, the mismatched signal steering vector is compensated based on the error vector decomposition and the optimization of output power of adaptive beamformer. Finally, the adaptive weight vector of robust wideband beamforming is calculated based on the minimum variance distortionless response criterion. The effectiveness of proposed method is verified by numerical simulations.

Adaptive Beamformers for High-Speed Mobile Communication

Abstract: In this work, we propose a fast conjugate gradient method (CGM) for beamforming, after thoroughly analyzing the performances of the least mean square (LMS), the recursive least square (RLS), and the sample matrix inversion (SMI) adaptive beamforming algorithms. Various experiments are carried out to analyze the performances of each beamformer in detail. The proposed conjugate gradient method does not use the Eigen spread of the signal correlation matrix as in the case of the LMS and the RLS methods. It computes antenna array weights orthogonally for each iteration. Hence the convergence rate and the null depths of the proposed method are much better than the LMS, the SMI the RLS and the classical CGM. Also, the simulation results confirm that this method has a speed improvement of about 60% over the classical conjugate gradient method. This aspect significantly reduces the processor burden and saves a lot of power during the beamforming process. Hence the proposed method is superior compared to the LMS, the RLS, the SMI, and classical CGM and most suitable for high-speed mobile communication.

RIS-Aided Offshore Communications with Adaptive Beamforming and Service Time Allocation.

Abstract: Reconfigurable intelligent surfaces (RISs), which can deliberately adjust the phase of incident waves, have shown enormous potentials to reconFigure the signal propagation for performance enhancement. In this paper, we investigate the RIS-aided offshore system to provide a cost-effective coverage of high-speed data service. The shipborne RIS is placed offshore to improve the signal quality at the vessels, and the coastal base station is equipped with low-cost reconfigurable reflect-arrays (RRAs), instead of the conventional costly fully digital antenna arrays (FDAAs), to reduce the hardware cost. In order to meet the rate requirements of diversified maritime activities, the effective sum rate (ESR) is studied by jointly optimizing the beamforming scheme and the service time allocated to each vessel. The optimal allocation scheme is derived, and an efficient fixed-point based alternating ascent method is developed to obtain a suboptimal solution to the non-convex beamforming problem. Numerical results show that the ESR is considerably improved with the aid of the RIS, and the proposed scheme using the hardware-efficient RRAs has only a slight performance loss, compared to its FDAA-based counterpart.