Beamforming Techniques Research Articles

Performance Analysis of Reconfigurable Intelligent Surface (RIS)-Assisted Satellite Communications: Passive Beamforming and Outage Probability

Reconfigurable intelligent surfaces (RISs), which consist of numerous passive reflecting elements, have emerged as a prominent technology to enhance energy and spectral efficiency for future wireless networks. RISs have the capability to intelligently reconfigure the incident wave, reflecting it towards the intended target without requiring energy for signal processing. Consequently, they have become a promising solution to support the demand for high-throughput satellite communication (SatCom) and enhanced coverage for areas inaccessible to terrestrial networks. This paper presents an asymptotic analysis of an RIS-assisted SatCom system. In this system, an unmanned aerial vehicle equipped with an RIS operates as a mobile reflector between a satellite and users. In particular, a passive beamformer is designed with the aim of asymptotically attaining optimal performance, considering the limitations imposed by practical SatCom systems. Moreover, the closed-form expressions for the ergodic achievable rate and outage probability are derived considering the proposed passive beamforming technique. Furthermore, we extend the system model to a multicast system and asymptotically analyze the optimality of the proposed scheme, leveraging the derived asymptotic results in the unicast system. The results of the simulations confirm that our analyses can precisely and analytically assess the performance of the RIS-assisted SatCom system, confirming the asymptotic optimality of the proposed scheme.

Zero-feedback open-loop distributed transmit beamforming

This paper presents a novel open-loop distributed transmit beamforming technique, where no collaboration or feedback from the receiver is required. In general, distributed transmit beamforming without receiver collaboration can be performed by observing a reference signal, together with positional information of individual array nodes. This approach is usually impractical due to the stringent accuracy requirement on positional information. We propose a practically feasible approach to substantially relax this requirement by combining observations from multiple reference signals. The scheme is highly practical since reference signals can be observed from existing broadcast stations, while the beamforming solution can be computed with low complexity. Our numerical results show that significant beamforming gains are achievable with relatively imprecise positional information.

A Software-Defined Radio Platform for Teaching Beamforming Principles

This paper presents the development and validation of a hybrid beamforming system based on software-defined radio (SDR), designed for telecommunications engineering education. The system provides an agile and user-friendly platform that allows students to observe, test, and evaluate beamforming techniques in real time. The platform integrates a multichannel SDR device (USRP N310) with traditional radiofrequency equipment and open-source software, facilitating hands-on learning experiences. The paper details the proposed hardware and software architecture and documents the calibration and validation phases. The testing and validation processes were conducted using a 3.5 GHz antenna array in both indoor and outdoor environments. The results demonstrated the system’s effectiveness in achieving the desired beam orientations, with experimental results aligning closely with simulation and theoretical predictions. Significant differences in the radiation patterns observed between the indoor and outdoor measurements were documented, highlighting the impact of environmental factors on beamforming performance. The insights gained from this research provide valuable contributions to the education of future telecommunications engineers, enhancing their understanding of practical beamforming applications and the integration of modern SDR technology.

Low-resolution phase shifter-based hybrid precoding for mmWave massive MIMO

As the demand for high-capacity and energy-efficient wireless communications grows, the exploration of cost-effective and power-optimized hybrid digital-analog beamforming techniques for millimeter-wave massive MIMO systems has gained substantial traction. Adopting low-resolution phase shifters offers a more practical solution in terms of reducing power consumption and implementation costs. We investigate hybrid beamforming strategies for mmWave massive MIMO systems that employ low-resolution phase shifters, with a specific focus on designing and optimizing the analog beamforming component. To exploit the full potential of analog phase shifter network (APSN) in mmWave massive MIMO system while maintaining low-power consumption, we propose a two-layer APSN with low-resolution PSs to replace the conventional single-layer APSN with high-resolution PSs. Under the constraints imposed by the two-layer APSN hardware architecture, we propose a two-level codebook structure and an adaptive-weighted cross-entropy optimization algorithm that offers superior performance while substantially reducing the computational burden compared to the conventional cross-entropy optimization algorithm. We further develop two alternative schemes through the maximal gain harvest method to reduce complexity. We also investigate the case that only one steering vector is shared in the inner beamformer and present two low-complexity beamforming algorithms. Through simulations, the proposed two-layer APSN structure and the proposed algorithms have been shown to outperform existing methods, with notable effectiveness in scenarios where low-resolution phase shifters are utilized.

Low-rank rotation matrices-based DOA estimation with a moored circular vector sensor array

Abstract Water currents affect circular vector sensor arrays (CVSAs) suspended from a moored platform, causing them to rotate underwater. This rotation alters the direction of sources within the array coordinate system over time. Traditional methods that rely on numerous snapshots often yield inaccurate results, particularly for faint sources. To improve the accuracy, we introduce a direction-finding technique for CVSAs that employs low-rank rotation matrices (RM). The low-rank RM are constructed using the heading information of the CVSAs and the subregion array manifold vector matrices to achieve spatial focusing. When these matrices are applied to the measurement data, the resulting covariance matrix displays subspace characteristics similar to that of a stationary CVSA. Our performance analysis revealed that low-rank RM offer higher focusing gains than conventional RM. The method proposed in this study effectively improves the direction estimation performance for weak targets and extends the practical applicability of measurement techniques for rotating platforms. Both simulations and experiments confirm that our approach outperforms the modified traditional beamforming and other spatial focusing techniques in terms of resolution and precision. Notably, when the signal-to-noise ratio is below −4 dB, the resolution for distinguishing between the two sources increases by more than 50%.

Evaluation of Norm-Constrained Capon Method on Improving Doppler Peak Localization of Antenna-Arrayed High-Frequency Coastal Radar

Abstract Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method. Significance Statement We have recently implemented a high-frequency coastal radar equipped with an antenna array to monitor metocean variables within the offshore waters adjacent to Taichung Harbor in Taiwan. Our study focuses on enhancing the Doppler spectral resolution of radar sea echoes by employing an adaptive beamforming technique known as the norm-constrained Capon method. In comparison with the traditional linear beamforming approach, the norm-constrained Capon method demonstrates superior noise resilience. We anticipate that the norm-constrained Capon method holds promise as a dependable approach for refining the accuracy of metocean parameters obtained from antenna-arrayed high-frequency coastal radar systems.

Acoustic imaging of geometrically shielded sound sources using tailored Green's functions.

In light of the growing market of urban air mobility, it is crucial to accurately detect the stationary or moving noise sources within the complex scattering environments caused by aircraft structures such as airframes and engines. This study combines conventional and wavelet-based beamforming techniques with an acoustic scattering prediction method to develop an acoustic imaging approach that considers scattering effects. Tailored Green's function is numerically evaluated and used to compute the steering vectors and the specific delayed time used in those beamforming methods. By examining common scenarios where a scatterer is positioned between the source plane and the array plane, it is observed that beamforming in a scattering environment differs from that in free space, leading to improved resolution alongside scattering-induced side lobes. The effectiveness of the developed method is validated through numerical simulations and experimental studies, confirming its improved ability to localize both stationary and rotating sound sources in a shielded environment. This advancement offers effective techniques for acoustic measurement and fault monitoring in the presence of structural scatterers.

Reducing Successive Interference Cancellation Iterations in Hybrid Beamforming Multiuser Massive Multiple Input Multiple Output Systems Through Grouping Users with Symmetry Channels

This paper presents a comprehensive exploration of advanced beamforming techniques tailored for millimeter-wave (mm-Wave) communication systems. In response to the burgeoning demand for higher data rates, coupled with the constraints of power consumption and hardware complexity, this study focuses on developing a hybrid beamforming framework optimized for downlink scenarios, specifically targeting groups of users based on the approximate symmetry of their channels. The primary innovation of this research lies in leveraging the symmetry of channels among near users to develop a group-based successive interference cancellation (SIC) algorithm. Unlike traditional approaches that address interference on a per-user basis, this algorithm utilizes channel symmetry within clusters of users to reduce computational complexity and improve the efficiency of SIC. By grouping users with symmetrical channel characteristics, the algorithm simplifies the interference management process while maintaining system performance. The proposed system demonstrates notable advantages over existing non-linear algorithms through extensive simulations and performance evaluations, particularly in terms of spectral efficiency and computational complexity. In this study, we further emphasize the importance of balancing spectral efficiency improvements with reduced computational demands, offering a nuanced trade-off that accommodates various operational requirements. The flexible optimization framework provided showcases the system’s adaptability to diverse deployment scenarios and network configurations.

IRS assisted anti jamming and beamforming technique

Recently, the emerging intelligent reflecting surfaces (IRS) has been leveraged to establish a smart and controllable wireless environment. This paper addresses the challenge of maximizing spectral efficiency in an IRS-aided downlink multi-user system while facing jammer attacks. The optimization problem focuses on improving the available transmission rate and limiting the impact of malicious jammer signals. The efficiency of the IRS in anti-jamming communications is achieved by determining the optimal phase shifts of the IRS elements and the optimal beamforming at the base station (BS). The optimization problem is non-convex. To tackle this non-convexity, an alternating optimization algorithm is proposed, dividing the main problem into two sub-problems which are solved using the semi-definite relaxation (SDR) and Gaussian randomization techniques. The simulation results confirm the enhanced spectral efficiency achieved by the proposed anti-jamming method in an IRS-assisted system. The optimal adjustment of the phase shifts in the IRS elements has resulted in a significant enhancement of about 25% in spectral efficiency compared to the random IRS phase case.

Accelerating the clean-SC and CMF beamforming deconvolution methods using neural gridcompression

Deconvolution algorithms can considerably improve the spatial resolution of sound sources when applied to beamforming techniques. However, they are often associated with additional computation time, which can be prohibitive for real-time applications, especially when using high-resolution scanning grids. Additionally, depending on the size of the problem, full deconvolution may not even be possible due to memory constraints. Recently, it has been shown that a neural network approach to compress the possible source directions can speed up the results of the DAMAS deconvolution algorithm by orders of magnitude. In this paper, this neural grid compression preprocessing is evaluated for the CLEAN-SC and CMF deconvolution techniques, and it is shown that it is possible to achieve dramatic speedups without significant loss of accuracy and even improve the results in some examples, especially for the CLEAN-SC technique.

Inducing alternating magnetic fields for real-time non-contact fault localization within electric energy storage component arrays.

With the wide application of electric energy storage component arrays, such as battery cell arrays, capacitor arrays, and inductor arrays, their potential safety risks have gradually drawn the public attention. However, existing technologies cannot realize rapid, precise, and nondestructive localization of the faulty component within these large-scale arrays, especially for a component with an early stage short-circuit fault. To address this challenge, this paper proposes a magnetic field based method and realizes precise fault localization by inducing an alternating magnetic field from the target array, unlike previous research where a static magnetic field was induced. Through establishing a physical model of the short-circuit component as well as the whole array, a spatial filtering algorithm based on beamforming techniques is utilized to process the measured magnetic field data in real time. Both the simulation and experimental results demonstrate the capability of the proposed method in enhancing the security of electric energy storage component arrays. Within an imaging area of 80 × 80 mm2, the proposed method can accurately locate the faulty component out of a nine-component array, with an error of only 0.72mm for capacitors and 0.91mm for battery cells.

An Ultralow-Power Closed-Loop Distributed Beamforming Technique for Efficient Wireless Power Transfer

An Ultralow-Power Closed-Loop Distributed Beamforming Technique for Efficient Wireless Power Transfer

Adaptive Hybrid Deep Learning Based Effective Channel Estimation in MIMO-Noma for Millimeter-Wave Systems with an Enhanced Optimization Algorithm

The channel estimation is crucial in the “millimeter Wave (mmWave) Massive Multiple-Input MultipleOutput (MIMO) and Non-Orthogonal Multiple Access (NOMA)” devices. Hybrid beamforming techniques are employed nowadays to minimize the complexity and equipment price. However, the absence of digital beam forming in mmWave affects the dynamic range and accuracy of the channel estimation. Previous research has concentrated mainly on predicting narrow-band mmWave channels using deep learning networks, as the wideband channels of mmWave create a considerable range and noise issue. Accurate channel estimation in the MIMO system is challenging because of the increased number of antennas and radio frequency (RF) chains. MIMO system communications using mmWave are frequently chosen because of their massive spectrum resources. Therefore, it is essential to tackle the obstacles obtained in the standard channel estimation framework by developing a MIMO-NOMA network with the help of deep learning methods Hence, this paper proposes an efficient hybrid deep learning model for channel estimation in MIMO-NOMA for mmWave systems. At first, the channel estimation is carried out using the Adaptive Hybrid Deep Learning (AHDL) model, where it combines both Autoencoder and Recurrent Neural Network (RNN). Here, the parameters are optimized using the Improved Red-tailed Hawk Algorithm (IRHA). Later the hardware cost and system complexity are reduced by performing the hybrid beam-forming process. Numerical results show that the proposed channel estimation and pilot estimation process outperforms the state-of-the-art approaches.

High-Resolution Power Doppler Using Null Subtraction Imaging.

To improve the spatial resolution of power Doppler (PD) imaging, we explored null subtraction imaging (NSI) as an alternative beamforming technique to delay-and-sum (DAS). NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. NSI uses a null in the beam pattern to improve the lateral resolution, which we apply here for improving PD spatial resolution both with and without contrast microbubbles. In this study, we used NSI with three types of singular value decomposition (SVD)-based clutter filters and noise equalization to generate high-resolution PD images. An element sensitivity correction scheme was also proposed as a crucial component of NSI-based PD imaging. First, a microbubble trace experiment was performed to evaluate the resolution improvement of NSI-based PD over traditional DAS-based PD. Then, both contrast-enhanced and contrast free ultrasound PD images were generated from the scan of a rat brain. The cross-sectional profile of the microbubble traces and microvessels were plotted. FWHM was also estimated to provide a quantitative metric. Furthermore, iso-frequency curves were calculated to provide a resolution evaluation metric over the global field of view. Up to six-fold resolution improvement was demonstrated by the FWHM estimate and four-fold resolution improvement was demonstrated by the iso-frequency curve from the NSI-based PD microvessel images compared to microvessel images generated by traditional DAS-based beamforming. A resolvability of [Formula: see text] was measured from the NSI-based PD microvessel image. The computational cost of NSI-based PD was only increased by 40 percent over the DAS-based PD.

Enhancing 5G massive MIMO systems with EfficientNet‐B7‐powered deep learning‐driven beamforming

AbstractThe development of wireless communication systems is a challenging and constantly evolving field and the issue of gaining optimal performance is of utmost importance. This work intends to give a thorough and detailed description of massive MIMO technology and its properties, with a significant emphasis on digital beamforming (FDB) and hybrid beamforming (HBF) techniques and the potential of combining them with the most recent and exciting frontier of research: deep learning. On one hand, FDB provides accurate signal control but, on the other hand, it deals with substantial needs like high‐power consumption. This challenge makes the focus shift to HBF—the innovative technology successfully coupled with deep learning's powerful potential. The chosen research explores extensively the major areas of application and compatibility of this operating mode in a diverse range of operational situations in the interference environment as well as in different levels of noise conditions. Moreover, the study offers a comprehensive comparison, which is highly effective in exploring further methods that focus on improving spectral efficiency. Significantly, the “Proposed Method” is suggested to be at the leading position, which demonstrates superior performance. Showing outstanding generalization capability, versatile robustness, and efficiency of usage in the proposed framework rely on EfficientNet‐B7 as the major portion. This makes it adaptive to its dynamic surroundings and puts it as a powerful tool in the world of advanced connectivity and massive MIMO technology. Due to its core ability to respond to changes in conditions effectively and efficiently, the proposed framework is seen as one of the most powerful approaches that could be used to change wireless communication systems.

TDOA-based localization of cracking sound events with minimal-error microphone subsets

We present a method based on the time difference of arrival (TDOA) and index minimal-error subsets of microphones to localize sudden cracking sound events, which appear somewhere in flax-fiber reinforced concrete specimens. Validation tests with small-scale pendulum impacts and known impact locations were carried out. Error estimation was performed and error ellipses were calculated. Microphone subsets leading to the smallest localization error were indexed. We validated the localization accuracy against localization results calculated using the delay-and-sum beamforming technique. Further, tension tests on concrete specimens were performed until failure; crack patterns were recorded by photogrammetry. The cracking sound events were localized. With the good match between TDOA-based localization results and crack patterns, we demonstrate that the proposed localization procedure is reliably applicable for real-time localization of concrete cracking.

Analysis of ultrasonic measurement data from medical models

This study's primary goal is to improve the accuracy and efficiency of acoustic wave propagation simulations using circular probe models in ultrasonography. This research aims to develop a detailed understanding of how variations in boundary conditions and wave characteristics influence the fidelity of ultrasound imaging. A range of simulation techniques were employed, focusing on non-dispersive and dispersive scenarios, to model acoustic wave behavior comprehensively. The study utilized Gaussian beamforming techniques and improved kernel functions to refine the resolution and decrease computational overhead. Various scenarios were simulated to analyze the impact of wave scattering and dispersion on imaging outcomes. The simulations demonstrated significant improvements in image resolution and accuracy. The refined methods allowed for more apparent distinctions in wave behavior under different boundary conditions, providing deeper insights into wave propagation dynamics. The results confirmed that controlling dispersion and scattering is critical for enhancing imaging quality. This research contributes to the field of ultrasonic imaging by presenting advanced simulation methods that offer more accurate and efficient imaging solutions. The study provides valuable insights into optimizing ultrasonic probes and imaging techniques by focusing on the impact of wave characteristics and boundary conditions. The findings have significant implications for medical diagnostics and material characterization, suggesting potential improvements in ultrasound technology for better patient outcomes and more precise material assessments.

Automatic relocation of intermediate-depth earthquakes using adaptive teleseismic arrays

SUMMARY Intermediate-depth earthquakes, accommodating intraslab deformation, typically occur within subduction zone settings at depths between 60–300 km. These events are in a unique position to inform us about the geodynamics of the subducting slab, specifically the geometry of the slab and the stress state of the host material. Improvements in the density and quality of recorded seismic data enhance our ability to determine precise locations of intermediate-depth earthquakes, in order to establish connections between event nucleation and the tectonic setting. Depth phases (near-source surface reflections, e.g. pP and sP) are crucial for the accurate determination of earthquake source depth using global seismic data. However, they suffer from poor signal-to-noise ratios in the P wave coda. This reduces the ability to systematically measure differential traveltimes to the direct P arrival, particularly for the frequent lower magnitude seismicity which highlights considerable seismogenic regions of the subducted slabs. To address this limitation, we have developed an automated approach to group globally distributed stations at teleseismic distances into ad-hoc arrays with apertures of 2.5$^\circ$, before optimizing and applying phase-weighted beamforming techniques to each array. Resultant vespagrams allow automated picking algorithms to determine differential arrival times between the depth phases and their corresponding direct P arrival. Using these differential times we can then determine the depths of earthquakes, which in turn can be used to create a catalogue of relocated events. This will allow new comparisons and insights into the governing controls on the distribution of earthquakes in subducted slabs. We demonstrate this method by relocating intermediate-depth events associated with northern Chile and the Peruvian flat slab regions of the subducting Nazca plate. The relocated Chilean catalogue contains comparable event depths to an established catalogue, calculated using a semi-automated global methodology, which serves to validate our fully automatic methodology. The new Peruvian catalogue we generate indicates three broad zones of seismicity approximately between latitudes 1–7$^\circ$S, 7–13$^\circ$S and 13–19$^\circ$S. These align with flat to steep slab dip transitions and the previously identified Pucallpa Nest. We also find a regionally deeper slab top than indicated by recent slab models, with intraslab events concentrated at points where the slab bends, suggesting a link between slab flexure and intermediate-depth earthquake nucleation.

Mitigation of Suppressive Interference in AMPC SAR Based on Digital Beamforming

Multichannel Synthetic Aperture Radar (MC-SAR) systems, such as Azimuth Multi-Phase Centre (AMPC) SAR, provide an effective solution for achieving high-resolution wide-swath (HRWS) imaging by reducing the pulse repetition frequency (PRF) to increase the swath width. However, in an Electronic Countermeasures (ECM) environment, the image quality of multichannel SAR systems can be significantly degraded by electromagnetic interference. Previous research into interference and counter-interference techniques has predominantly focused on single-channel SAR systems, with relatively few studies addressing the specific challenges faced by MC-SAR systems. This paper uses the classical spatial filtering technique of adaptive digital beamforming (DBF). Considering the Doppler ambiguity present in the echoes, two schemes—Interference Reconstruction And Cancellation (IRC) and Channel Grouping Nulling (CGN)—are designed to effectively eliminate suppressive interference. The IRC method eliminates the effects of interference without losing spatial degrees of freedom, ensuring effective suppression of Doppler ambiguity in subsequent processing. This method shows significant advantages under conditions of strong Doppler ambiguity and low jammer-to-signal ratio. Conversely, the CGN method mitigates the effect of interference on multichannel imaging at the expense of degrees of freedom redundant to Doppler ambiguity suppression. It shows remarkable interference suppression performance under weak-Doppler-ambiguity conditions, allowing for better image recovery. Simulations performed on point and distributed targets have validated that the proposed methods can effectively remove interfering signals and achieve high-resolution wide-swath (HRWS) SAR images.

An application of a beamforming technique, linear acoustic array and robot for pipe condition localization

Pipe inspection robots with sensors significantly enhance the maintenance of water systems by enabling early defect detection and reducing the risks of leaks and environmental damage. This paper introduces a sparse representation acoustic beamformer designed to locate artefacts within pipes using a short linear microphone array on a robot. The primary contributions include: (i) a new acoustic beamforming approach based on sparse representation that accurately predicts pipe conditions with a localization error within 3 % of the sensing distance; (ii) an enhanced beamforming algorithm combining plane wave and first non-axisymmetric mode, extending the frequency range from < 1300 Hz to < 2200 Hz in a 150 mm diameter pipe (an increase by 1.69 times), effectively managing unwanted acoustic reverberations and distinguishing blockages from lateral connections; and (iii) a novel robust algorithm predicting pipe length with an error margin within 2 %. This research advances acoustic sensing technologies for autonomous mobile robots inspecting buried pipes.