Conventional Beamforming Methods Research Articles

Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks

Conventional beamforming methods for reconfigurable reflector antennas assume full control over the amplitude and phase of the reflected field. Here, we develop a novel beamforming methodology for reflecting Programmable Metasurfaces (PMS) with capacitive memory. Although utilizing such fully reactive PMS simplifies antenna design and reduces energy consumption, the PMS reflection magnitude is unity and thus a global optimization of the reflection phases over the PMS unit cells is required in each beamforming scenario. We propose an implementation of such an optimization method rooted in the traditional Fourier transform-based beamforming and evaluate its performance. Additionally, we show that a pair of trained feed-forward neural networks (FFNN) with one input, one hidden, and one output layer can replace time-consuming global optimizations in the case of a PMS comprising 3×10 unit cells. We train the FFNNs on a dataset obtained for typical single- and dual-beam beamforming scenarios. After training, the FFNNs perform requested beamforming tasks within a fraction of second and with about the same accuracy as the original optimization algorithm. The proposed methodology may find applications in future mobile telecommunication systems that require real-time beamforming on low-end hardware. The same beamforming methodology can be also employed in short-range wireless power transfer systems.

Identification of broadband acoustical sources in the cylindric duct using a hybrid reconstruction method

Beamforming and inverse methods are widely applied to the localization and reconstruction of acoustical sources inside ducts. Identification of in-duct sources using the conventional beamforming method would lead to poor spatial resolution and even fail at some specific frequencies with multiple virtual sources. Employing the inverse method would improve the accuracy of inner duct source localization, however, severe deviation of source intensity results calculated by the inverse method from actual results would occur due to tremendous dimensions of inversion and regulation process of the transfer function matrix. To reduce the dimensions of inversion and further improve the accuracy of source identification, a combined imaging methodology is proposed. The beamforming method is firstly applied to locate regions containing sound sources preliminarily, then an inverse method is applied to regions contributed by beamforming to remove ghosts and side lobes. Numerical simulations are implemented to validate the method. Azimuthal and radial localization results indicate that the combined imaging method is with better spatial resolution than beamforming or inverse methods. Reconstructed results of the source's volumetric velocity demonstrate that the proposed method has a lower deviation of source intensity than the inverse method.

A novel audio-visual based beamformer using a 360° camera and planar microphone array system

Beamforming systems that rely only on microphone arrays may suffer from poor performance in complex acoustic environments with multiple sources and room reverberation. To overcome this limitation, we propose a novel method that combines a planar microphone array and a 360° camera to steer the beamformer. The camera provides visual information about the location and orientation of sound sources, which can be used to adjust the beamformer parameters. We will present results that compare the signal-to-noise ratio (SNR) of the proposed method with a conventional beamforming microphone-based method in a simulated room with different source positions, orientations, and noise levels to investigate the benefit of visual information for beamforming in different environments. The proposed method has the potential to improve the performance of beamforming systems in applications such as speech enhancement, speech recognition, and speaker localization.

Localization of cyclostationary acoustic sources via cyclostationary beamforming and its high spatial resolution implementation

The localization of cyclostationary sound sources is important for rotating machinery noise source identification and fault diagnosis. Cyclostationary sound sources are a special class of nonstationary sound sources, which exhibit periodic changes in statistics. Most of the conventional sound source identification methods are based on the stationary sound source assumption, which do not consider the cyclostationarity of the radiated sound field by rotating machinery. Thus, they are hard to localize the acoustic sources with different cyclic frequencies. In this paper, a novel technique of localizing the cyclostationary acoustic sources with different cyclic frequencies is proposed. First, the cyclostationary conventional beamforming (CSCBF) method is deviated. In CSCBF, the cyclic-cross-spectral matrix (CCSM) is defined considering all the cross cyclic spectral correlation (CSC) between different microphones in the array. The left steering vector and right steering vector are constructed to focus the CCSM on each scanning grid point at a given combination of cyclic frequency and spectral frequency. Then, the high resolution cyclostationary beamforming (HR-CSBF) is further developed to improve the spatial resolution of CSCBF. In HR-CSBF, two linear equations according to the property of CSC is established as the acoustic inverse problems. With the sparse distribution of the cyclostationary sources in the space as the prior knowledge, the iterated Bayesian focusing (IBF) is utilized to obtain a robust and sparse solution in HR-CSBF. Numerical simulations and experiments are conducted to validate the proposed methods. To explore the potential applications of the proposed methods, the localization of high pressure pump (HPP) and rolling bearing with outer-race failure are also used to evaluate the effectiveness of the proposed methods. It turns out that both CSCBF and HR-CSBF can localize the cyclostationary sources with different cyclic frequencies. Compared with CSCBF, HR-CSBF can achieve a higher spatial resolution in the obtained acoustic map.

Acoustic imaging of the broadband noise arising in contra-rotating co-axial propellers

The interaction between two contra-rotating co-axial propellers leads to variations in noise strength during the rotation and presents challenges for traditional acoustic measurement methods. In this work, we propose using a wavelet-based beamforming method to study the transient noise generation characteristics of the contra-rotating co-axial propellers. Compared to the conventional beamforming method, the wavelet-based beamforming method can address unsteady and high-speed moving noise sources in the time-frequency domain. A representative set-up is considered herein. The acoustic imaging results show that the transient noise sources increase after the overlap of the propellers and are dominant on the aft propeller, particularly at high frequencies and low rotating speeds. At relatively high rotating speeds, the results show that the contra-rotating propellers noise can be generated at the trailing edge near the hub of the propellers. Overall, the wavelet-based beamforming method deepens the physical understanding of transient aerodynamic and aeroacoustic processes, and could find applications in the design of low-noise contra-rotating propellers.

Compressive frequency-difference direction-of-arrival estimation

Direction-of-arrival estimation is difficult for signals spatially undersampled by more than half the wavelength. Frequency-difference beamforming [Abadi, Song, and Dowling (2012). J. Acoust. Soc. Am. 132, 3018–3029] offers an alternative approach to avoid such spatial aliasing by using multifrequency signals and processing them at a lower frequency, the difference-frequency. As with the conventional beamforming method, lowering the processing frequency sacrifices spatial resolution due to a beam broadening. Thus, unconventional beamforming is detrimental to the ability to distinguish between closely spaced targets. To overcome spatial resolution deterioration, we propose a simple yet effective method by formulating the frequency-difference beamforming as a sparse signal reconstruction problem. Similar to compressive beamforming, the improvement (compressive frequency-difference beamforming) promotes sparse nonzero elements to obtain a sharp estimate of the spatial direction-of-arrival spectrum. Analysis of the resolution limit demonstrates that the proposed method outperforms the conventional frequency-difference beamforming in terms of separation if the signal-to-noise ratio exceeds 4 dB. Ocean data from the FAF06 experiment support the validity.

Design and Development of Multibeam Antenna Technologies for 5G Wireless Communications

Multibeam antenna (MBA) systems operating in the millimeter-wave frequency bands have drawn significant attention from researchers and are being effectively studied due to the demanding system needs for the fifth-generation (5G) wireless communication systems and the drastic spectrum scarcity at the existing cellular frequency range. To enable a greater transmission speed, an enhanced signal-to-interference-plus-noise ratio, an enhanced spectral and energy performance, and flexible beam shaping, they serve as the primary antenna technology. They offer tremendous potential for serving as the crucial infrastructure for facilitating beamforming and massive multiple-input multiple-output (MIMO) which uplift the 5G. To achieve this, a novel beamforming design and optimization approach based on the Convex Time Modulated Particle Swarm Optimization (CTM-PSO) algorithm is suggested for 5G communication networks, offering multiple concurrent transceivers to support efficient beamformed interactions whereas a second beam concurrently detects the atmosphere around the base-station. The suggested beamforming method combines the optimization of the transmitter and receiver beamforming weights to increase sensor performance, reduce potential intervention resulting from the communication beams, and ensure the communications link's target beamforming gains. Comprehensive numerical assessments are used to evaluate the effectiveness of the recommended method, showing that it may provide significant improvements over more conventional beamforming methods.

Performance Evaluation of Hybrid Precoder Design for Multi-User Massive MIMO Systems with Low-Resolution ADCs/DACs

This paper presents a comprehensive analysis and design of a hybrid precoding system tailored for mmWave multi-user massive MIMO systems in both downlink and uplink scenarios. The proposed system employs a two-stage precoding approach, incorporating UQ and NUQ techniques, along with low-resolution DACs in downlink and ADCs in uplink to address hardware limitations. The system considers Zero Forcing and Minimum Mean Square Error algorithms as digital precoding methods for the uplink scenario, while exploring the impact of different DAC resolutions on system performance. Extensive simulations reveal that the proposed system surpasses conventional analog beamforming methods, particularly in multi-user scenarios involving inter-user interference. In downlink, the system demonstrates a trade-off between SE and EE, achieving higher Energy Efficiency with NUQ. In uplink, NUQ and UQ converters exhibit similar performance trends regardless of the chosen combiner algorithm. The proposed system attains enhanced Spectral and Energy Efficiency while maintaining reduced complexity and overhead. The study significantly contributes to the advancement of efficient and effective mmWave multi-user massive MIMO systems by providing a thorough analysis of various quantization schemes and precoding techniques. The findings of this research are expected to aid in the optimization of 5G and beyond technologies, particularly in high-density deployment scenarios.

An improved deconvolution beamforming algorithm for acoustic imaging of low signal-to-noise ratio sound sources in reverberant field

Most of the existing acoustic imaging studies in reverberant field ignore the influence of signal-to-noise ratio. As a result, commonly used beamforming algorithms in reverberant backgrounds have poor imaging accuracy for low signal-to-noise ratio sound sources. In response to that problem, an improved adaptive beamforming algorithm called SC-DAMAS is put forward in this paper. The algorithm replaces the free-field Green's function with the impulse response function, making the algorithm more suitable for acoustic imaging of low signal-to-noise ratio in a reverberant environment. Besides, the comparative simulation results with the conventional beamforming method and orthogonal matching pursuit algorithm-based DAMAS, as well as sound source acoustic imaging experiments are carried out to analyze its effectiveness. It is indicated that, in the reverberation field, the SC-DAMAS has no obvious sidelobes and achieves higher positioning accuracy for acoustic imaging of low signal-to-noise ratio sound source than the abovementioned counterparts, and its imaging test result is consistent with the actual situation, which verifies the effectiveness of the algorithm.

Partially Distributed Beamforming Design for RIS-Aided Cell-Free Networks

Cell-free networks are regarded as a promising technology to meet higher rate requirements for beyond fifth-generation (5G) communications. Most works on cell-free networks focus on either fully centralized beamforming to maximally enhance system performance, or fully distributed beamforming to avoid extensive channel state information (CSI) exchange among access points (APs). In order to achieve both network capacity improvement and CSI exchange reduction, we propose a partially distributed beamforming design algorithm for reconfigurable intelligent surface (RIS)-aided cell-free networks. We aim at maximizing the weighted sum-rate of all users by designing active and passive beamforming subject to transmit power constraints of APs and unit-modulus constraints of RIS elements. The weighted sum-rate maximization problem is first transformed into an equivalent weighted sum-mean-square-error (sum-MSE) minimization problem, and then alternating optimization (AO) approach is adopted to iteratively design active and passive beamformer. Specifically, active beamforming vectors are obtained by local APs and passive beamforming vector is optimized by central processing unit (CPU). Numerical results not only illustrate the proposed partially distributed algorithm achieves the remarkable performance improvement compared with conventional local beamforming methods, but also further show the considerable potential of deploying RIS in cell-free networks.

Deblurring of Sound Source Orientation Recognition Based on Deep Neural Network.

Underwater target detection and identification technology are currently two of the most important research directions in the information disciplines. Traditionally, underwater target detection technology has struggled to meet the needs of current engineering. However, due to the large manifold error of the underwater sonar array and the complexity of ensuring long-term signal stability, traditional high-resolution array signal processing methods are not ideal for practical underwater applications. In conventional beamforming methods, when the signal-to-noise ratio is lower than −43.05 dB, the general direction can only be vaguely identified in the general direction. To address the above challenges, this paper proposes a beamforming method based on a deep neural network. Through preprocessing, the space-time domain of the target sound signal is converted into two-dimensional data in the angle-time domain. Subsequently, we trained the network with enough sample datasets. Finally, high-resolution recognition and prediction of two-dimensional images are realized. The results of the test dataset in this paper demonstrate the effectiveness of the proposed method, with a minimum signal-to-noise ratio of −48 dB.

Reconfigurable reflective metasurface reinforced by optimizing mutual coupling based on a deep neural network

Mutual coupling between different states of active unit cells is a challenging factor that has not been considered in the design of reconfigurable transmissive or reflective metasurface antennas. In this work, we propose a gain-predicting deep neural network (GPDNN) that predicts the radiation patterns of a reconfigurable reflective metasurface (RRM) composed of a 12-by-12 one-bit active unit cell array and is used to search for the best combination of unit cell on-or-off states for beam forming. First, the GPDNN is trained to accurately predict the radiation pattern based on the combination of unit cells. Second, it is merged with a search algorithm that retrieves the best on-or-off states near the boundary of the two states determined using the conventional beam-forming calculation method. As proof of concept, the proposed scheme is employed to find the highest realized gain in five directions: (θ, φ) = (0°, 0°), (−60°, 0°), (60°, 0°), (−60°, 90°), and (60°, 90°). The proposed deep neural network–based search algorithm takes 3.27 × 10−7 seconds per design, which is considerably faster than that based on full-wave simulation (1.5 h per design). The accuracy of the proposed method is verified by comparing the predicted results with those of the full-wave simulation. Finally, the best combination of on-or-off states for each beam-forming case is experimentally verified by measuring the radiation pattern. Compared with the conventional design, the maximum gain increases up to 0.771 dB at (θ, φ) = (−60°, 0°), and the side lobe levels decrease substantially in the other cases.

선체 부착 음향 센서를 이용한 함정 추진기 소음 예측에 대한 실험적 연구

In this study, the propeller noise source level of a naval vessel was predicted using onboard hydrophones through model-scaled noise measurements. A scaled model ship and a set of propellers were installed in a cavitation tunnel, with onboard sensors at the hull above the portside propeller. A conventional beamforming method in the frequency domain was used to calculate the source level. For comparison, the source level was measured using a hydrophone in the tunnel using the International Towing Tank Conference method. The propeller cavitation characteristics and noise of the naval vessel at the model scale were analyzed, and the measured and predicted source levels were compared. Finally, a sensitivity analysis was conducted to verify its feasibility in ship onboard Under-water Radiated Noise monitoring.

An adaptive beamforming algorithm for sound source localisation via hybrid compressive sensing reconstruction

In this study, a hybrid compressive sensing reconstruction algorithm called SAMP-CoSaMP is proposed. The unique combination maintains the speed advantage of CoSaMP and the adaptive sparsity searching ability from the SAMP. Afterwards, an improved beamforming algorithm named SC-DAMAS for sound source localisation is created by integrating our hybrid algorithm with the classic DAMAS. Lastly, the reconstruction accuracy is compared between the SAMP-CoSaMP, SAMP, and CoSaMP algorithms in different signal-to-noise ratio scenarios. The results show that the SAMP-CoSaMP is balanced between running efficiency and reconstruction error. In addition, we perform comparative sound source localisation simulations and experiments by our SC-DAMAS with those of the conventional beamforming method and orthogonal matching pursuit algorithm-based deconvolution approach. SC-DAMAS is superior to the aforementioned counterparts in localisation performance without the need to predetermine the sparsity value.

Wideband Receive Beamforming Based on 4-D Antenna Arrays With Postmodulation

A wideband receive beamforming configuration and method based on four-dimensional (4-D) antenna arrays with postmodulation is proposed. Two pairs of single-pole-triple-throw switches are utilized for beamforming at the center frequency. Postmodulation is introduced to increase the degree of freedom of 4-D antenna arrays and achieve a deep null point at the steering direction of sidebands power pattern. The reception of 16 quadrature amplitude modulation signal, whose bandwidth is larger than the time modulation frequency, is implemented by a 16-element 4-D dipole array. The results of wideband beamforming show that the sidebands power at the steering direction can be eliminated ideally, whereas the sidelobes level of averaged radiated power pattern is suppressed to a low value. The 4-D array with postmodulation shows obviously improved bit error rates performance as compared to the 4-D array with conventional beamforming method and phased arrays, which confirms that the 4-D array with postmodulation has potential advantages in the application of wideband communication systems.

Super-resolution imaging using conventional and non-conventional beamforming

Super-resolution ultrasound surpasses diffraction limits by localizing spatio-temporally separable contrast agents and generates images that significantly exceed the resolution of conventional B-mode imaging methods. However, the ability to detect contrast agents and to separate their signal from the underlying tissue and from sources of image degradation such as phase aberration or reverberation clutter remains a process that is governed by fundamental wave propagation and beamforming. Here, super-resolution imaging and improvements in contrast detection, imaging depth, resolution, and registration accuracy are demonstrated using conventional and non-conventional beamforming methods in 2D and 3D. Three different imaging schemes: (a) single plane-wave, (b) three steered plane-wave compounding, and (c) 256 focused transmits are compared in vivo to quantify the improvements in contrast detection. Wide-beam 3D transcranial super-resolution and power Doppler images through a human and macaque skull are demonstrated using a 1.5 MHz sparse matrix array. These partially and fully focused methods are also demonstrated transcranially in rodents using 2D imaging at 15 MHz and volumetric imaging at 8 MHz. Finally super-harmonic super-resolution imaging approaches are demonstrated for stationary and moving bubbles in murine tumors. These imaging methods extend the capabilities of super-resolution imaging and may improve the clinical translatability of the technique.

Robust Suppression of Deceptive Jamming with VHF-FDA-MIMO Radar under Multipath Effects

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar has received a lot of attention due to the advantages of waveform diversity. Suppression of mainlobe deceptive jamming can be effectively achieved with the degree of freedom (DOF) in the range domain. However, the existing research mainly focuses on non-coherent signals. The echo signal of VHF-FDA-MIMO radar for low elevation has its own unique characteristics. False targets cannot be suppressed with conventional beamforming methods. Thus, a signal model for VHF-FDA-MIMO radar subjected to deceptive jamming is established. The reconstruction of the covariance matrix and the estimation of the steering vector are implemented with the generalized MUSIC algorithm. In addition, a matching Capon reconstruction method is proposed to finish the robust suppression of false targets for the problem of self-cancellation in the absence of a priori information. Finally, the beampattern and performance curves of different methods are compared. The simulation results show that the methods can be effectively applied to the suppression of deceptive jamming in VHF-FDA-MIMO radar under the multipath effect.

Transient analysis of trailing edge noise assisted by wavelet-based beamforming and flow visualisation

This work focuses on the trailing edge flow-induced noise by studying the associated transient noise generation physics. A wavelet-based beamforming method is first performed to analyse the dynamics and correlations of the trailing edge noise problems in the time–frequency domain. The wavelet-based beamforming method is an extension of the conventional beamforming method in which the Fourier transform is substituted by the wavelet transform to perform the transient acoustic imaging. The transient process is interpretable by using this method, which is impossible for the previous conventional acoustic imaging techniques. To verify and showcase the proposed method, the velocity field obtained by particle image velocimetry is provided along with the time–frequency analysis of acoustic measurements. Next, a particular problem of trailing edge noise from an aerofoil is experimentally studied at the Reynolds number of O(105), where the intermittent nature of the underlying aerodynamic flow structures can be inferred by the proposed wavelet-based beamforming. To provide the complementary flow-field results, a secondary experiment is performed in a different wind tunnel around the same Reynolds number. The finding of the correlation between the flow field and acoustic imaging results constitutes the key contribution of this work. Finally, the analysis of the velocity flow field and of noise source image is used to elucidate the associated flow physics. Overall, the proposed method can help to achieve deepened physical insights into various transient and moving systems in underwater and aerospace systems.

The separation and identification of distributed noise sources with local coherence by a subspace equivalent-source method

Identification of distributed and locally coherent aerodynamic noise sources, such as airfoil noise and blade noise, attracts considerable attention these years. When using beamforming techniques, errors in source positions may be caused by the assumption of incoherent sources. A subspace equivalent-source method is proposed to decompose and identify different components of sources. Sound pressure radiated from each component is represented by coherent structures of the overall sound field, which are calculated by using the Proper Orthogonal Decomposition. The equivalent-source model is modified for the influence of the flow field and the shear layer by using a geometry method. Truncated Singular Value Decomposition is applied to avoid singularities generated by the inverse problem and reduce errors of source identification. The proposed algorithm is verified by numerical simulations and a benchmark experiment of airfoil noise in the wind tunnel. Leading edge noise, trailing edge noise and tip noise are successfully separated and located. The distribution of each source is estimated more accurately than Conventional Beamforming and deconvolution methods. Source decomposition and intrinsic coherence among equivalent sources both account for better performance.

Channel Estimation-Free Deep Direct Beamforming With Low Complexity in mmWave Massive MIMO

Hybrid precoding is used in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems instead of full digital precoding to reduce the cost and power consumption. Conventional hybrid precoding however requires complex channel estimation before beamforming. In this paper, we propose a low complexity low-cost channel estimation-free deep direct beamforming method composed of two basic steps-beam impulse and beamforming for time division duplex (TDD). This method is built upon randomized singular value decomposition (RSVD) and can directly complete the process of beamforming. Furthermore, a realistic dataset of mmWave channel-DeepMIMO is adopted to evaluate and determine the practicability of this method. The simulation results demonstrate that the proposed deep direct beamforming achieves high performance while its complexity and power consumption are very low. Compared with other methods without perfect channel status information (CSI), the proposed method costs less and requires lower computational resources while achieves higher spectral efficiency. Furthermore, the RSVD can be easily combined with conventional beamforming methods hence the proposed method is easily extendable to other applications.