Implementation Of Beamforming Research Articles

Adapted Beamforming: A Robust and Flexible Approach for Removing Various Types of Artifacts from TMS-EEG Data.

Electroencephalogram (EEG) recorded as response to transcranial magnetic stimulation (TMS) can be highly informative of cortical reactivity and connectivity. Reliable EEG interpretation requires artifact removal as the TMS-evoked EEG can contain high-amplitude artifacts. Several methods have been proposed to uncover clean neuronal EEG responses. In practice, determining which method to select for different types of artifacts is often difficult. Here, we used a unified data cleaning framework based on beamforming to improve the algorithm selection and adaptation to the recorded signals. Beamforming properties are well understood, so they can be used to yield customized methods for EEG cleaning based on prior knowledge of the artifacts and the data. The beamforming implementations also cover, but are not limited to, the popular TMS-EEG cleaning methods: independent component analysis(ICA), signal-space projection (SSP), signal-space-projection-source-informed-reconstruction method (SSP-SIR), the source-estimate-utilizing noise-discarding algorithm (SOUND), data-driven Wiener filter (DDWiener), and the multiple-source approach. In addition to these established methods, beamforming provides a flexible way to derive novel artifact suppression algorithms by considering the properties of the recorded data. With simulated and measured TMS-EEG data, we show how to adapt the beamforming-based cleaning to different data and artifact types, namely TMS-evoked muscle artifacts, ocular artifacts, TMS-related peripheral responses, and channel noise. Importantly, beamforming implementations are fast to execute: We demonstrate how the SOUND algorithm becomes orders of magnitudes faster via beamforming. Overall, the beamforming-based spatial filtering framework can greatly enhance the selection, adaptability, and speed of EEG artifact removal.

Design and Implementation of Analog-Digital Hybrid Beamformers for Low-Complexity Ultrasound Systems: A Feasibility Study.

Low-complexity ultrasound systems are increasingly desired for both wearable, point-of-care ultrasound and high-end massive-channel ultrasound for 3-D matrix imaging. However, the imaging capabilities, including spatial resolution and contrast, could suffer as low complexity systems are pursued, which remains as an unresolved tradeoff. To mitigate this limitation, this study revisits the general structures of analog and digital beamformers and introduces a hybrid approach, referred to as analog-digital hybrid beamforming, to implement efficient ultrasound systems. The suggested hybrid beamforming takes two stages sequentially, where the first analog stage partially beamforms M-channel RF signals to N sum-out data (i.e., M-to-N beamforming), and the second digital stage beamforms N partial sums to single final beamformed data (i.e., N-to-1 beamforming). Our approach was systematically designed and implemented with only four major integrated circuits, which was capable of driving full 64-channel transmission and reception. The developed system was demonstrated with a customized 64-channel 1-D phased array using a commercial tissue mimicking phantom. From the phantom imaging results, signal-to-noise ratio, contrast-to-noise ratio, and full beam width at half maximum values were quantitatively evaluated. The demonstrated results indicate that the analog-digital hybrid beamforming can be applied to any type of array for sophisticated 3-D imaging and tiny wearable ultrasound applications.

Design and characterisation of a 3-D microphone array for windtunnel measurements

A three-dimensional (3-D) array of GRAS 40PH-10 CCP Free-field Array Microphones has been designed for use in the UNSW anechoic wind tunnel (UAT) for aeroacoustic measurements. The array consists of 192 microphones split into three identical planar arrays of 64 microphones which are placed on both sides and above the potential core of the open-jet that issues into the wind tunnel test section. The planar arrays feature an Underbrink spiral comprised of eight arms of eight microphones each. The spiral configuration was selected to maximize signal-to-noise ratio (SNR) and minimize the beamwidth given the geometrical restrictions of the UAT. A white noise speaker was placed at a range of locations within the testing domain to characterize the source localization abilities of the array as a function of position and frequency. Furthermore, a pair of independent white noise speakers were placed at various separation distances to determine the array’s capability to identify multiple sources within the domain. A 3-D implementation of conventional cross-spectral beamforming and Clean-SC deconvolution algorithms was used, and the performance of each was compared. It was determined that Clean-SC was the preferred method for 3-D beamforming in terms of source localization accuracy, SNR, and beamwidth, despite the higher computational cost.

Bayesian adaptive beamformer for robust electromagnetic brain imaging of correlated sources in high spatial resolution.

Reconstructing complex brain source activity at a high spatiotemporal resolution from magnetoencephalography (MEG) or electroencephalography (EEG) remains a challenging problem. Adaptive beamformers are routinely deployed for this imaging domain using the sample data covariance. However adaptive beamformers have long been hindered by 1) high degree of correlation between multiple brain sources, and 2) interference and noise embedded in sensor measurements. This study develops a novel framework for minimum variance adaptive beamformers that uses a model data covariance learned from data using a sparse Bayesian learning algorithm (SBL-BF). The learned model data covariance effectively removes influence from correlated brain sources and is robust to noise and interference without the need for baseline measurements. A multiresolution framework for model data covariance computation and parallelization of the beamformer implementation enables efficient high-resolution reconstruction images. Results with both simulations and real datasets indicate that multiple highly correlated sources can be accurately reconstructed, and that interference and noise can be sufficiently suppressed. Reconstructions at 2-2.5mm resolution (~150K voxels) are possible with efficient run times of 1-3 minutes. This novel adaptive beamforming algorithm significantly outperforms the state-of-the-art benchmarks. Therefore, SBL-BF provides an effective framework for efficiently reconstructing multiple correlated brain sources with high resolution and robustness to interference and noise.

DIGITAL BEAMFORMING AND MILIMETER WAVE (mmWave) IN 5G NETWORK

This paper gives an overview of the application of digital beamforming in the deployment of mmWave in 5G networks. It x-rays a little insight into the evolution of networks and the main aim of the 5G networks. It also looked at the concept of digital beamforming and the mmwave technology; going further to consider the implementation, benefits and challenges of digital beamforming and milimeter wave (mmwave) in 5G network.

Efficient Algorithms for Constant-Modulus Analog Beamforming

The use of a large-scale antenna array (LSAA) has become an important characteristic of multi-antenna communication systems to achieve beamforming gains such as in designing millimeter-wave (mmWave) systems to combat severe propagation losses. In such applications, each antenna element has to be driven by a radio frequency (RF) chain for the implementation of fully-digital beamformers, significantly increasing the hardware cost, complexity, and power consumption. Therefore, constant-modulus analog beamforming (CMAB) becomes a viable solution. In this paper, we consider the scaled analog beamforming (SAB) or constant-modulus analog beamforming (CMAB) architecture and design the system parameters by solving two variants of beampattern matching problem. In the first case, both the magnitude and phase of the beampattern are matched to the given desired beampattern whereas in the second case, only the magnitude of the beampattern is matched. Both the beampattern matching problems are cast as a variant of the constant-modulus least-squares (CLS) problem. We provide efficient algorithms based on the alternating majorization-minimization (AMM) framework that combines the alternating minimization and the MM frameworks and the conventional-cyclic coordinate descent (C-CCD) algorithms to solve the problem in each case. We also propose algorithms based on a new modified-CCD (M-CCD) based approach. For all the developed algorithms we prove convergence to a Karush-Kuhn-Tucker (KKT) point (or a stationary point). Numerical results demonstrate that the proposed algorithms converge faster than the state-of-the-art solutions. Among all the algorithms, the M-CCD-based algorithms have faster convergence when evaluated in terms of the number of iterations and the AMM-based algorithms offer lower complexity.

An Efficient Beamforming Architecture to Handle the Trade-Off Between Performance and Hardware Complexity in Multiuser Massive MISO Systems

Beamforming is the fundamental concept of wireless communications to serve several users through multiple-antenna transceivers. The advent of massive multiple-input multiple-output (MIMO) systems leads to an investigation into beamforming to reach optimal performance. However, fully-digital beamforming implementation has to face important challenges in terms of consumed power and cost of the RF chains and converters. To solve the problem, some studies focus on reducing hardware complexity by dividing the beamforming into digital and analog parts, known as hybrid beamforming (HBF). In HBF systems, the dominant part of the hardware complexity depends on the architecture of the analog network, which determines the required RF components. In this paper, we categorize the analog beamforming parts based on the connectivity between RF-chains and antennas. Moreover, we show the impacts of the connection on the analog beamforming matrix. To this aim, we propose an efficient connectivity architecture in which the antennas are divided into fully-connected and singly-connected groups so that we can deal with the hardware complexity-performance trade-off. In addition, to achieve further performance improvements, we propose a dynamic architecture that adjusts the connection of the RF-chain antenna to the channel state information (CSI) through a switch network. Analytically, we propose an efficient approach to calculate the zero-forcing precoder, which is proper for both fixed and dynamic architectures. A two-part algorithm based on the greedy search method has also been developed to obtain switch states in dynamic architecture and then implemented by a deep neural network (DNN). The simulation results confirm the theoretical analysis and the suitability of the proposed architecture.

Design and Performance Estimation of a Photonic Integrated Beamforming Receiver for Scan-on-Receive Synthetic Aperture Radar

Synthetic aperture radar is a remote sensing technology finding applications in a wide range of fields, especially related to Earth observation. It enables a fine imaging that is crucial in critical activities, like environmental monitoring for natural resource management or disasters prevention. In this picture, the scan-on-receive paradigm allows for enhanced imaging capabilities thanks to wide swath observations at finer azimuthal resolution achieved by beamforming of multiple simultaneous antenna beams. Recently, solutions based on microwave photonics techniques demonstrated the possibility of an efficient implementation of beamforming, overcoming some limitations posed by purely electronic solutions, offering unprecedented flexibility and precision to RF systems. Moreover, photonics-assisted RF beamformers can nowadays be realized as integrated circuits, with reduced size and power consumption with respect to digital beamforming approaches. This paper presents the design analysis and the challenges of the development of a hybrid photonic-integrated circuit as the core element of an X-band scan-on-receive spaceborne synthetic aperture radar. The proposed photonic-integrated circuit synthetizes three simultaneous scanning beams on the received signal, and performs the frequency down-conversion, guaranteeing a compact 15 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -form factor, less than 6 W power consumption, and 55 dB of dynamic range. The whole photonics-assisted system is designed for space compliance and meets the target application requirements, representing a step forward toward a deeper penetration of photonics in microwave applications for challenging scenarios, like the observation of the Earth from space.

A unified view on beamformers for M/EEG source reconstruction

Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging.

On Analyzing Beamforming Implementation in O-RAN 5G

The open radio access network (O-RAN) concept is changing the landscape of mobile networks (5G deployment and 6G research). O-RAN Alliance’s suggestions that O-RAN can offer openness and intelligence to the traditional RAN vendors will enable the capability for multi-vendors to re-shape the RAN structure and optimize the network. This paper positions the main research challenges of the O-RAN approach in regards to the implementation of beamforming. We investigate the O-RAN architecture and the configurations of the interfaces between O-RAN units and present the split options between the radio and distributing units in terms of O-RAN specification and 3GPP standards. From this point, we discuss the beamforming methods in O-RAN, addressing challenges and potential solutions, and suggest the introduction of the zero-forcing equalizer as a precoding vector in the channel-information-based beamforming method. This may be one of the solutions for achieving flexibility in a high-traffic communication environment while reducing the radio unit interferences caused by implanting the precoding in the open radio unit.

A Corpus-Based Evaluation of Beamforming Techniques and Phase-Based Frequency Masking

Beamforming is a type of audio array processing techniques used for interference reduction, sound source localization, and as pre-processing stage for audio event classification and speaker identification. The auditory scene analysis community can benefit from a systemic evaluation and comparison between different beamforming techniques. In this paper, five popular beamforming techniques are evaluated in two different acoustic environments, while varying the number of microphones, the number of interferences, and the direction-of-arrival error, by using the Acoustic Interactions for Robot Audition (AIRA) corpus and a common software framework. Additionally, a highly efficient phase-based frequency masking beamformer is also evaluated, which is shown to outperform all five techniques. Both the evaluation corpus and the beamforming implementations are freely available and provided for experiment repeatability and transparency. Raw results are also provided as a complement to this work to the reader, to facilitate an informed decision of which technique to use. Finally, the insights and tendencies observed from the evaluation results are presented.

A Low-Complexity Reconfigurable Multi-Antenna Technique for Non-Terrestrial Networks

Multi-Antenna communication techniques are an efficient and relatively simple approach for the performance improvement of wireless communication systems. However, the direct application of multi-antenna techniques to an aerial communication system is not always feasible due to the constraints induced by the aerial platforms. Reconfigurable intelligent antenna technologies could provide an efficient solution to these problems and thus they are considered as ideal candidates for adaption in the aerial communication platforms that will be used in the 5G and beyond communication networks. In this paper, a joint Tx-Rx beamforming with beam selection and combining technique is proposed for improving the performance of an aerial communication system supported by electronically steerable antenna arrays. The main idea of the proposed scheme is to select, using an SNR maximization criterion, a pair of beam patterns between each RF chain of the ground station and the aerial platform, and combine the received SNRs under the maximal ratio principle. Initially, an analytical stochastic framework has been developed that is based on a Markov chain model, which is used to investigate the statistics of the received SNR. Then, an implementation of the novel beamforming and pattern adaptation scheme is presented, with the use of Electronically Steerable Parasitic Array Radiators (ESPAR), properly designed for Ground Station to UAV links. In addition, a realistic simulator is also developed with proper channel model selection, by the aid of which, the performance of the proposed scheme has been evaluated in conjunction with the extracted analytical results.

A review of hybrid couplers: State‐of‐the‐art, applications, design issues and challenges

AbstractIn recent years, the hybrid branch‐line coupler has attracted much attention due to its appealing features such as of low cost and ease in fabrication for wireless communications. The fifth‐generation cellular networks promise to support several wireless technologies by capitalizing a multitude of frequencies and increase data rates. To achieve that, the butler matrix technique can be used to enhance both bandwidth and data rate with the implementation of beamforming. Conventional hybrid couplers are the main component to build a butler matrix, but they are generally bulky in size and narrow in bandwidth. Moreover, requirements imposed by newer wireless technologies makes the efforts in improving size compactness and bandwidth even more challenging. On the other hand, several techniques have been proposed in literature to solve both issues. This study focuses on the design challenges and issues of hybrid coupler designs and technologies, besides underlining their promising potential. In this context, several techniques for hybrid coupler to achieve the required bandwidth and size reduction are highlighted, such as the T‐shape, meander line, two sections, three‐section, and parallel couple lines.

Few-Shot Learning Based Hybrid Beamforming Under Birth-Death Process of Scattering Paths

Data-driven methods provide an efficient solution for the implementation of hybrid beamforming. However, the current data-driven methods do not consider the birth-death process of scattering paths (BDPP) which is very common in the realistic scenario. When the BDPP happens, the statistical characteristics of channel state information change dramatically and suddenly. So, BDPP makes the design model need a lot of updates to adapt to the new channel environment. Therefore, the design model derived by current data-driven methods, which only consider the change of angle range, is not suitable under the BDPP. In this letter, inspired by the few-shot learning, we present the scheme of the weights initialization for the design model to address the problem of hybrid beamforming under BDPP. By the proposed scheme, when the number of paths changes, the design model with derived initial weights is able to quickly adapt to the new number of paths via a rather small number of on-line model updates only. Simulation results demonstrate the proposed method can offer a significant efficiency of adapting to the new number of paths over existing works.

Digitally Reconfigurable Transmitarray With Beam-Steering and Polarization Switching Capabilities

In this paper, a novel digitally reconfigurable unit-cell and $4\times 4$ transmitarray operating at X-band is proposed that allows the implementation of beamforming and polarization conversion operations in a programmable way. The lower layer of transmitarray responsible for beam steering consists of two PIN diodes and a circular type patch which has two states of operations with 0 and $\pi $ phase responses which we name “State1” and “State2”. “State1” and “State2” can be represented by a digital notation as “1” and “0” elements. Similarly, the upper layer of the transmitarray responsible for polarization conversion operation consists of two states of operation which we name as “State3” and “State4”, respectively. “State3” and “State4” also can be represented by digital notations as “1” or “0” elements. By coding “0” and “1” elements for the lower side of the transmitarray in a 1-bit sequence, we can steer the beam in our desired direction. Similarly, by coding “0” and “1” elements in a 1-bit sequence element for the upper layer, we can get X-polarized and Y-polarized beams. Combining the upper layer 1-bit sequence and lower layer 1-bit sequence, we can get both polarization conversion and beam steering simultaneously. A $4\times 4$ array with our proposed unit cell was designed and fabricated. A good agreement between simulated and measured results was observed. The proposed design is a promising concept because it can be fabricated using the conventional fabrication process, it is cost-effective compared to conventional phased array antenna for satellite and radar applications. A $4\times 4$ array was taken just to check the transmission and reflection coefficients of the proposed unit cells. A very small transmitarray was manufactured and experimentally characterized just as a proof of concept, to verify that the unit cells work properly.

Implementation of a Correlator onto a Hardware Beam-Former to Calculate Beam-Weights

One of the main technologies to open up a wider field of view for today’s radio telescopes are phased arrays. This is especially the case for radio astronomy instruments operating below 2[Formula: see text]GHz. Nowadays, the existing dish-type instruments are being upgraded with phased array feeds (PAF) in the focal plane. This increases the field of view at the expense of needing more analog electronics and digital signal processing. One of the digital signal processing functionalities used to combine the digitized signals from the PAF is a beam-former which creates multiple high sensitivity beams within the field of view of the dish. Before beams can be formed, the signals from the PAF need to be calibrated using a correlator. In this paper, we present a solution where these two operations are combined by using the beam-former also as a correlator. The statistics unit used as part of the beam-former implementation, can be used as well for calculating correlation products. With the proper settings of the beam-former weight of each beamlet, a frequency sub-band with a direction, can be used as a single cross correlation product. By implementing the correlator on the beam-former, the digital resources and development time can be reduced. To validate the idea, two versions of the algorithm are implemented in the Apertif PAF system on the Westerbork Synthesis Radio Telescope (WSRT). Results show that two full-bandwidth correlation matrices per beam, needed to determine the static beam weights for the calibration, and a single column of the correlation matrix, used to compensate for any drift between the receiver chains, can be performed.

ERON: an energy-efficient and elastic RF-optical architecture for mmWave 5G radio access networks

This paper proposes an elastic RF-optical networking (ERON) architecture solution for millimeter wave (mmWave) 5G radio access networks. The ERON architecture achieves energy efficiency and throughput elasticity using photonic-enhanced multibeam mmWave spatial multiplexing capability at the radio units. The centralization of the hardware resources and the converged management of the RF and optical resources in the data units offer high resource pooling gain. A numerical study on the energy efficiency of ERON’s photonic-enabled mmWave 5G system reveals that ERON is 5× more energy efficient than both conventional digital and hybrid RF beam-forming implementations. We also conducted a user mobility-aware network resources study, and the results show a 10 dB network resource pooling gain when compared to classic radio access network implementations.

Comparison of beamformer implementations for MEG source localization

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression.We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes.When applied carefully to MEG data with a typical SNR (3–15 ​dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.

Efficient Implementations of First-Order Steerable Differential Microphone Arrays With Arbitrary Planar Geometry

We present a spatial filtering approach to first-order steerable Differential Microphone Arrays (DMAs) with arbitrary planar geometry. In particular, the design of the spatial filter is based on a recently proposed frequency-domain design methodology that approximates, in a least-square sense, a target beampattern using the Jacobi-Anger expansion involving Bessel functions. Despite the generality of that approach, however, its computational cost turns out to be excessive when working with limited processing resources. The beamforming technique proposed in this manuscript overcomes this issue by exploiting the fact that in DMAs the spacing between sensors is typically smaller than the smallest wavelength of audio signals of interest. This allows us to substitute zero- and first-order Bessel functions with their Taylor series approximation truncated to the first order. Moreover, we show that this approximation allows us to derive an efficient discrete-time-domain implementation of first-order steerable differential beamformers based on arrays with arbitrary geometries.

Atom search optimization algorithm based hybrid antenna array receive beamforming to control sidelobe level and steering the null

Modern and upcoming 5G cellular communication networks require large-scale antenna systems to improve gain in order to face the weaknesses in the suggested millimeter-wave (mmWave) signals and to meet the higher capacity demand. These systems implement digital beamforming which requires radio frequency (RF) chain connected to each antenna. Hybrid analog-digital (HAD) beamforming has been proposed as a solution to many of the challenges facing the implementation of fully-digital beamforming, namely, energy consumption, hardware cost, and complexity. The main goals of this paper are to reduce the peak sidelobe level (SLL) of the beam pattern and steering the nulls in the desired directions along with reducing the power consumption, hardware cost, and complexity. To achieve these goals, a novel partial-connected hybrid analog-digital receive beamformer based on an atom search optimization (ASO) algorithm is proposed. ASO is a recently invented metaheuristic optimization algorithm imitating the physical movements of atoms as described in molecular dynamics simulation with the advantage of having few parameters to tune. The desired optimization weights are accomplished by controlling the amplitudes-only of the digital beamformer’s vector along with the phase shifts of the analog beamformer’s vectors. Finally, to demonstrate the effectiveness of the proposed beamformer, a comparison with other state-of-the-art metaheuristic algorithms through several scenarios is conducted, in terms of peak SLL reduction, null depth, and convergence rates. Simulation results show that the proposed algorithm has always been outperforming other types of beamformers.