Performance Of Beamformer Research Articles

Analysis of scalable channel estimation in FDD massive MIMO

One of the key ideas for reducing downlink channel acquisition overhead for FDD massive MIMO systems is to exploit a combination of two assumptions: (i) the dimension of channel models in propagation domain may be much smaller than the next-generation base-station array sizes (e.g., 64 or more antennas), and (ii) uplink and downlink channels may share the same low-dimensional propagation domain. Our channel measurements demonstrate that the two assumptions may not always hold, thereby impacting the predicted performance of methods that rely on the above assumptions. In this paper, we analyze the error in modeling the downlink channel using uplink measurements, caused by the mismatch from the above two assumptions. We investigate how modeling error varies with base-station array size and provide both numerical and experimental results. We observe that modeling error increases with the number of base-station antennas, and channels with larger angular spreads have larger modeling error. Utilizing our modeling error analysis, we then investigate the resulting beamforming performance rate loss. Accordingly, we observe that the rate loss increases with the number of base-station antennas, and channels with larger angular spreads suffer from higher rate loss.

Loudspeaker array beamforming for sound projection in a half-space with an impedance boundary.

Sound projection is a method of using highly focused sources to project sound onto reflective surfaces to create the impression of surround sound, which can be realized via beamforming using loudspeaker arrays. Generally, these devices are mounted near a surface with a finite acoustic impedance, such as a TV accent wall. The beamforming performance degrades in this half-space with an impedance boundary when the filters are designed using transfer functions measured in the free field. This paper proposes a model for predicting the beamforming performance in a half-space with an impedance boundary in terms of the transfer functions in the free field. A mathematical model is presented, and the results are compared with practical measurements for a 16-driver line array in real-use environments. The results show that reflections from the boundary lead to "dark bands" in the beam, causing the beam to deviate from the designed direction. Using filters designed with transfer functions calculated by the proposed model yields improved beamforming performance.

DeepTx: Deep Learning Beamforming With Channel Prediction

Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it to provide considerable performance gains. In this study, we focus on machine learning algorithms for the transmitter. In particular, we consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The CNN is trained in a supervised manner considering both uplink and downlink transmissions with a loss function that is based on UE receiver performance. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase. The provided numerical experiments demonstrate the improved beamforming performance.

Residual Hearing Does Not Influence the Effectiveness of Beamforming when Using a Cochlear Implant in Conjunction with Contralateral Routing of Signals

Introduction: Contralateral routing of signals (CROS) overcomes the head shadow effect by redirecting speech signals from the contralateral ear to the better-hearing cochlear implant (CI) ear. Here we tested the performance of an adaptive monaural beamformer (MB) and a fixed binaural beamformer (BB) using the CROS system of Advanced Bionics. Methods: In a group of 17 unilateral CI users, we evaluated the benefits of MB and BB for speech recognition by measuring speech reception threshold (SRT) with and without beamforming. MB and BB were additionally evaluated with signal-to-noise ratio (SNR) measurements using a KEMAR manikin. We also assessed the effect of residual hearing in the CROS ear on the benefits of MB and BB. Speech was delivered in front of the listener in a background of homogeneous 8-talker babble noise. Results: With CI-CROS in omnidirectional settings with the T-mic active on the CI as a reference, BB significantly improved SRT by 1.4 dB, whereas MB yielded no significant improvements. The difference in effects on SRT between the two beamformers was, however, not significant. SNR effects were substantially larger, at 2.1 dB for MB and 5.8 dB for BB. CI-CROS with default omnidirectional settings also improved SRT and SNR by 1 dB over CI alone. Residual hearing did not significantly affect beamformer performance. Discussion: We recommend the use of BB over MB for CI-CROS users. Residual hearing in the CROS ear is not a limiting factor for fitting a CROS device, although a bimodal option should be considered.

Fast Wideband Beamforming Using Convolutional Neural Network

With the wideband beamforming approaches, the synthetic aperture radar (SAR) could achieve high azimuth resolution and wide swath. However, the performance of conventional adaptive wideband time-domain beamforming is severely affected as the received signal snapshots are insufficient for adaptive approaches. In this paper, a wideband beamformer using convolutional neural network (CNN) method, namely, frequency constraint wideband beamforming prediction network (WBPNet), is proposed to obtain a satisfactory performance in the circumstances of scanty snapshots. The proposed WBPNet successfully estimates the direction of arrival of interference with scanty snapshots and obtains the optimal weights with effectively null for the interference by utilizing the uniqueness of CNN to extract potential nonlinear features of input information. Meanwhile, the novel beamformer has an undistorted response to the wideband signal of interest. Compared with the conventional time-domain wideband beamforming algorithm, the proposed method can fast obtain adaptive weights because of using few snapshots. Moreover, the proposed WBPNet has a satisfactory performance on wideband beamforming with low computational complexity because it avoids the inverse operation of covariance matrix. Simulation results show the meliority and feasibility of the proposed approach.

A novel beamforming technique using mmWave antenna arrays for 5G wireless communication networks

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) and beamforming technologies are most likely the integral parts and the key enablers of 5G-and-Beyond (5G-B) wireless communication systems. From that perspective, this manuscript proposes an efficient digital beamforming technique for 5G applications. The suggested approach is formulated based on a reliable sampling technique that effectively samples the interference-plus-noise covariance matrix (IPNCM) to construct an efficient projection matrix (PM). The proposed technique is therefore named interference-plus-noise PM (IPNPM) beamformer. Next, a compact-size rectangular microstrip antenna operating at 49.3 GHz having 1.7 GHz potential bandwidth is designed using CST microwave studio. The designed antenna is subsequently used to model a 32-element uniform linear array (ULA) representing a typical 5G base station. The elements of the modeled ULA are then manually fed with the complex-weights generated from the proposed beamformer to produce high-power beams and deep nulls toward the desired and undesired locations, respectively. To further improve the beamformer performance in terms of minimizing radiating power within undesired regions, an iterative sidelobe level (SLL) suppression technique is developed and integrated with the proposed beamformer. The conducted theoretical analysis justifies that the IPNPM beamformer requires less arithmetic operations than the well-known beamforming approaches. To reveal the benefits of the proposed technique, the array radiation pattern based on the proposed beamformer is plotted and compared with those for the three widely used approaches. An intensive Monte Carlo simulation is also carried out in which the IPNPM beamformer performance is systemically compared with the popular approaches based on the output signal to interference-plus-noise ratio (SINR) criterion. The achieved results demonstrate that the proposed approach is superior and surpasses the rival algorithms in terms of generating deeper nulls, enhanced output SINR, and lower execution time. It is also demonstrated that, unlike the existing SLL reduction techniques, the newly developed SLL suppressor reduces the SLLs of the proposed beamformer to a predefined threshold value (i.e., −24 dB) after few iterations.

Robust adaptive beamforming based on covariance matrix reconstruction using Gauss–Legendre quadrature and steering vector estimation

The performance of adaptive beamforming is considerably affected by system errors in the gain and phase perturbation errors, direction of arrival mismatch, and incoherent local scattering, especially when the sample data contains the signal of interest (SOI) component. In this study, a robust adaptive beamforming approach based on interference plus noise covariance matrix (INCM) reconstruction using Gauss–Legendre quadrature (GLQ) and steering vector (SV) estimation is developed. The proposed algorithm incorporates the GLQ with the integral over the spherical uncertainty set and uses a linear combination of the integral at several angular nodes to substitute the integral of the entire interference region; consequently, the computational efficiency of reconstructing the INCM is enhanced. The SV of the SOI is represented as a linear combination of several principal eigenvectors of the SOI covariance matrix; thus, the double-constrained problem corresponding to the noise subspace is transformed into a single-constrained model, and its solution can be gained by utilizing the Lagrange multiplier method. Subsequently, the weight vector of the proposed beamformer can be calculated. Numerical simulations indicate that the proposed approach can effectively suppress interferences and exhibits superior overall performance under system errors.

Performance Analysis on Eigenmode Beamforming for Reduced Capability Device in Massive MIMO Systems

Recently, a new type of device that enables reduced capability (RedCap) has been identified and standardized in the fifth generation (5G) New Radio (NR). Because RedCap user equipment (UE) is designed to have low device cost and complexity compared with high-end devices, single-layer transmission is mainly considered for RedCap UE. For single-layer transmission, traditional eigenmode beamforming, which transmits a data stream through the maximum eigenmode direction, is applicable for base stations (BSs), and a simple matched filter can be used on the UE side. Under this operation scenario, the performance of eigenmode beamforming is analyzed in this study, and the effect of the number of UE antennas on the achievable rate is investigated. The asymptotic achievable rate for eigenmode beamforming with practical channel training for both the uplink and downlink is derived as a closed-form expression for any number of UE antennas <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> . For the analysis, the statistics of the maximum eigenvalue are derived using the Tracy-Widom distribution. The results of the analysis show that the maximum eigenvalue is a concave function in terms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> , and there is an optimal number of UE antennas <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> that maximizes the achievable rate. Simulations verify the analytical results and show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> maximizing the achievable rate decreases as the numbers of BS antennas and co-scheduled UEs increase.

Broadband Spatial Spectrum Estimation Based on Space-Time Minimum Variance Distortionless Response and Frequency Derivative Constraints

The two-dimensional optimization capability of the space-time minimum variance distortionless response (STMVDR) can improve the spatial spectrum estimation performance of the broadband beamformer with limited samples, but its high sensitivity to frequency errors may lead to the suppression of some narrowband signals not at the grid point of the frequency response calculation. In order to reduce the sensitivity of STMVDR to frequency errors, a broadband spatial spectrum estimation method based on STMVDR and frequency derivative constraints is proposed in this paper. First, based on the beam response of the STMVDR beamformer, an arbitrary order frequency derivative constraint is derived. Second, combining the gain constraint and frequency derivative constraint, the linear constrained minimum variance problem is established to solve the optimal weight of the beamformer. Finally, the calculation method of the broadband spatial spectrum is derived. Numerical simulations show that the proposed method not only solves the problem of frequency mismatch caused by the insufficient number of points for calculating frequency response, but also does not cause the spatial response related peaks to deviate from the true direction due to the broadening of the main lobe of frequency response. In addition, the proposed method can reduce the computational complexity of the algorithm by more than half by increasing the interval of the frequency calculation grid points, and does not significantly loss in the angular resolution capability and estimation accuracy of the algorithm.

Spherical Random Arrays With Application to Aerial Collaborative Beamforming

Spherical volumetric arrays with randomly distributed elements are studied using geometrical probability theory. The array elements are considered as points of an isotropic homogeneous binomial point process (BPP), randomly located within a 3-D ball. The presented results are based on the distance distribution of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> th nearest element from the center of the ball. These elements are assumed to be the randomly distributed nodes of a wireless aerial network that cooperate to produce a beam toward a desired distant receiver. An alternative array elements’ distribution is also examined by placing a constraint to the distance of the farthest element and providing novel results on the average array factor, the average power pattern, and the directivity of the array. The half power beamwidth (HPBW), the peak sidelobe level (SLL), and the location of the peaks and nulls of the power pattern are examined in terms of the number of the array elements and the radius of the sphere. Finally, the impact of the location estimation errors on the performance of open-loop collaborative beamforming is examined both theoretically and using empirical simulation results.

Joint Beamforming Optimization Design and Performance Evaluation of RIS-Aided Wireless Networks: A Comprehensive State-of-the-Art Review

Joint Beamforming Optimization Design and Performance Evaluation of RIS-Aided Wireless Networks: A Comprehensive State-of-the-Art Review

Mask-Based Neural Beamforming for Moving Speakers With Self-Attention-Based Tracking

Beamforming is a powerful tool designed to enhance speech signals from the direction of a target source. Computing the beamforming filter requires estimating spatial covariance matrices (SCMs) of the source and noise signals. Time-frequency masks are often used to compute these SCMs. Most studies of mask-based beamforming have assumed that the sources do not move. However, sources often move in practice, which causes performance degradation. In this paper, we address the problem of mask-based beamforming for moving sources. We first review classical approaches to tracking a moving source, which perform online or blockwise computation of the SCMs. We show that these approaches can be interpreted as computing a sum of instantaneous SCMs weighted by attention weights. These weights indicate which time frames of the signal to consider in the SCM computation. Online or blockwise computation assumes a heuristic and deterministic way of computing these attention weights that, although simple, may not result in optimal performance. We thus introduce a learning-based framework that computes optimal attention weights for beamforming. We achieve this using a neural network implemented with self-attention layers. We show experimentally that our proposed framework can greatly improve beamforming performance in moving source situations while maintaining high performance in non-moving situations, thus enabling the development of mask-based beamformers robust to source movements.

Substrate-Integrated Coaxial Line (SICL) Rotman Lens Beamformer for 5G/B5G Applications

High-band allocations in the millimeter-wave (mm-Wave) frequency spectrum offer high-capacity wireless information transmission as required by fifth generation (5G) communication standards. Among different beamforming structures, the Rotman lens (RL) is an attractive passive-microwave-lens-based beamforming network due to its low fabrication cost, reliability, design simplicity and wide-angle scanning capabilities. Conventionally, the RL is implemented using microstrip line (MSL) technology for which there are inherent radiation losses that become severe when operating in mm-Wave 5G frequency bands. In this context, a novel substrate-integrated coaxial line (SICL)-based RL is designed, fabricated and tested, for accurate beamforming with extremely low feed line insertion loss. This article presents a complete design, development and performance analysis of an SICL-based RL beamformer. By using an SICL, isolation of up to 15 dB is achieved between the input beam ports of the RL, while the mutual coupling is kept at less than 20 dB. The SICL design shows a −10 dB insertion loss between the array and beam ports when compared to the same RL developed using MSL technology having an insertion loss of −15 dB. Due to the use of low-loss SICL technology, a realized gain of up to 14.2 dBi is achieved with an excellent scanning capability of −30 to 30 degrees, verifying for the first time the beamforming capabilities associated with SICL technology. The operational frequency band is 20–45 GHz, while the center operating frequency is 26 GHz making it appropriate for above 6-GHz 5G New Radio (NR) operating bands n257 (26.5 GHz to 29.5 GHz), n258 (24.25 GHz to 27.5 GHz), n261 (27.5 GHz to 28.35 GHz) and n260 (37 GHz to 40 GHz). Owing to the low-loss and stable beamforming performance, the SICL RL is suitable for mm-Wave 5G and is extendable to B5G applications.

Simultaneous Execution of Dereverberation, Denoising, and Speaker Separation Using a Neural Beamformer for Adapting Robots to Real Environments

It remains challenging for robots to accurately perform sound source localization and speech recognition in a real environment with reverberation, noise, and the voices of multiple speakers. Accordingly, we propose “U-TasNet-Beam,” a speech extraction method for extracting only the target speaker’s voice from all ambient sounds in a real environment. U-TasNet-Beam is a neural beamformer comprising three elements: a neural network for removing reverberation and noise, a second neural network for separating the voices of multiple speakers, and a minimum variance distortionless response (MVDR) beamformer. Experiments with simulated data and recorded data show that the proposed U-TasNet-Beam can improve the accuracy of sound source localization and speech recognition in robots compared to the conventional methods in a noisy, reverberant, and multi-speaker environment. In addition, we propose the spatial correlation matrix loss (SCM loss) as a loss function for the neural network learning the spatial information of the sound. By using the SCM loss, we can improve the speech extraction performance of the neural beamformer.

Fast Near-Field Beam Training for Extremely Large-Scale Array

In this letter, we study efficient near-field beam training design for the extremely large-scale array (XL-array) communication systems. Compared with the conventional far-field beam training method that searches for the best beam direction only, the near-field beam training is more challenging since it requires a beam search over both the angular and distance domains due to the spherical wavefront propagation model. To reduce the near-field beam-training overhead based on the two-dimensional exhaustive search, we propose in this letter a new two-phase beam training method that decomposes the two-dimensional search into two sequential phases. Specifically, in the first phase, the candidate angles of the user are determined by a new method based on the conventional far-field codebook and angle-domain beam sweeping. Then, a customized polar-domain codebook is employed in the second phase to find the best effective distance of the user given the shortlisted candidate angles. Numerical results show that our proposed two-phase beam training method significantly reduces the training overhead of the exhaustive search and yet achieves comparable beamforming performance for data transmission.

Multiple‐input multiple‐output sonar adaptive beamforming using transmission diversity smoothing and backward processing

AbstractBenefit from the transmission diversity smoothing (TDS) effect upon coherent targets decorrelation, the kind of adaptive beamformers can be directly applied for multiple‐input multiple‐output (MIMO) sonar applications. Increasing the number of transmitted signals helps improve the TDS‐based decorrelation performance. However, a large number of transmitters is not feasible for those sonar systems mounted on small platforms with limited space. To address the lack of TDS‐based degrees of freedom for smoothing, an improved TDS decorrelation method is proposed for MIMO sonar in this study. With the conjugated MIMO data covariance matrix being left‐multiplied and right‐multiplied, respectively, by the exchange matrix, the authors obtain a backward‐smoothed data covariance matrix. Afterwards, the authors average it with the original MIMO receiving data covariance matrix to reconstruct a new data covariance matrix. Compared with TDS, the improved TDS strengthens the decorrelation performance by increasing the number of covariance matrix for smoothing and thus enables the new rank restored signal covariance matrix to have better non‐singularity. Without increasing any aperture loss and system complexity, a lower correlation between echoes is obtained, and the performance of the minimum variance distortionless response beamformer is hence improved in the presence of coherent targets. The effectiveness of the proposed decorrelation method is verified by numerical simulations and real data from a water tank experiment.

DNN Beamforming for LEO Satellite Communication at Sub-THz Bands

The 6G communication system will be designed at sub-THz frequencies due to increasing demand in data rates, emerging new applications and advanced communication technologies. These high-performing systems will heavily rely on artificial intelligence (AI) for efficient and robust design of transceivers. In this work, we propose a deep neural network (DNN) beamformer that will replace the use of phase shifters for a massive array of antenna elements employed at the ground station for wideband LEO satellite communication at sub-THz bands. We show that the signal processing algorithm employed using DNN is capable to match the performance of a true-time delay beamformer as the angle of arrival of the received wideband signal at the ground station is changing due to rapid movement of the LEO satellite. The implementation of DNN beamformer will be able to reduce the cost of receiver and provide a way for the efficient and compact design of the massive array beamforming for wideband LEO satellite applications.

A Next-Generation Codebook Evolution Strategy for Massive Arrays Using Deep Neurals Networks

The discrete Fourier transform (DFT)-based codebook is currently among the mostly commonly adopted codebooks for beamforming using arrays of different shapes and sizes, including the large-scale two-dimensional uniform planar array (UPA). DFT-based codevectors can be easily generated in arbitrary angle resolutions and apply well to millimeter-wave (mmWave) channels due to their directive nature of resulting beams. However, a fixed set of codevectors is applied regardless of the user distributions and the propagation environment, which may exhibit limited beamforming performance under certain transmission scenarios. In this paper, we propose a new way of generating a set of beamforming vectors for multiple-input multiple-output (MIMO) transmission using massive arrays under the limited feedback of the channel state information (CSI). Precoder matrix indicator (PMI) and channel quality indicator (CQI) reports from the users have become the sources for the generation of a new set of codevectors, which are autonomously determined by the deep learning (DL) module at the base station (BS). The process is operated in an iterative fashion to produce updated versions of the codebook with the reduced return of the loss function at the deep neural network (DNN). The time-varying codebook for each BS automatically reflects the characteristics of a given wireless environment to adapt to its channel and traffic conditions. The reference signal (RS) at the BS is periodically transmitted in the form of beamformed CSI-RS, thus the operation is transparent to the users of the system and no significant specification changes are necessary. A simple plug-and-play type of BS installation suffices to achieve the potential gain of the proposal, which is demonstrated by the implementation details of the DL engine and the corresponding performance simulation results.

Design of Sparse Uniform Linear Array Beamformer using Modified FRM Structure for Varied Applications

This paper presents a method to generate antenna patterns for a Uniform Linear Array (ULA) having narrow beamwidth and low sidelobe levels (SLL) using the recently proposed Modified FRM (ModFRM) architecture. This allows it to direct the beams to specific ground cells for communications while mitigating inter-cell interference. The sharpness of the beam pattern defines the spatial discriminating performance of a ULA beamformer, while the SLL dictates the interference and noise suppression capabilities. Typically, a conventional ULA beamforming will demand high computational complexity and a large number of sensors to satisfy these requirements. Hence to reduce the system cost, using the ModFRM technique a sparse array is developed. With this strategy, the total number of sensors is drastically reduced compared to conventional ULA beamformers. The designed beamformers can be used in applications with stringent requirements where cost and size are concerned.

Intelligent Omni-Surfaces: Reflection-Refraction Circuit Model, Full-Dimensional Beamforming, and System Implementation

The intelligent omni-surface (IOS) is a dynamic metasurface that has recently been proposed to achieve full-dimensional communications by realizing the dual function of anomalous reflection and anomalous refraction. Existing research works provide only simplified models for the reflection and refraction responses of the IOS, which do not explicitly depend on the physical structure of the IOS and the angle of incidence of the electromagnetic (EM) waves. Therefore, the available reflection-refraction models are insufficient to characterize the performance of full-dimensional communications. In this paper, we propose a complete and detailed circuit-based reflection-refraction model for the IOS, which is formulated in terms of the physical structure and equivalent circuits of the IOS elements, as well as we validate it with the aid of full-wave EM simulations. Based on the proposed circuit-based model for the IOS, we analyze the asymmetry between the reflection and transmission coefficients. Moreover, the proposed circuit-based model is utilized for optimizing the hybrid beamforming of IOS-assisted networks and hence improving the system performance. To verify the circuit-based model, the theoretical findings, and to evaluate the performance of full-dimensional beamforming, we implement a prototype of IOS and deploy an IOS-assisted wireless communication testbed to experimentally measure the beam patterns and to quantify the achievable rate. The obtained experimental results validate the theoretical findings and the accuracy of the proposed circuit-based reflection-refraction model for IOSs.