Narrowband Beamforming Research Articles

Design of Real-Valued Wideband Beamformers Using an Adaptive-Array-Theory-Inspired WLS: Theory and Algorithm

The weighted least squares (WLS)-based beamformer design can achieve flexible beampattern control via adjusting the WLS weighting function inspired by the adaptive array theory (AAT). Nevertheless, the weighting function is conventionally adjusted in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> way, and precise beampattern control cannot be guaranteed. Although an analytical solution for the weighting function adjustment was proposed very recently, it is only applicable to complex-valued narrowband beamformers. In some applications such as acoustic/seismic array processing, however, real-valued wideband beamformers are required, since acoustic/seismic signals are usually unmodulated, real-valued and wideband. This paper studies the theory and design of real-valued wideband beamformers using an AAT-inspired WLS, which can achieve precise wideband beampattern control. The main contributions of this paper include: i) we theoretically prove the existence of the WLS weighting function for the real-valued wideband beamformers to achieve precise control of wideband beampatterns, and derive the corresponding analytical solutions; ii) we provide the insights into the impact of the weighting function on the cost function, which is useful to find the optimum solution for the weighting function among multiple feasible solutions; and iii) we propose an algorithm for the design of real-valued wideband beamformers using the proposed AAT-inspired WLS, which does not require to fine-tune any user parameters, and can guarantee precise control of wideband beampatterns as demonstrated by the simulation results.

Reconfigurable Intelligent Surfaces for Rich Scattering Wireless Communications: Recent Experiments, Challenges, and Opportunities

Recent advances in the fabrication and experimentation of Reconfigurable Intelligent Surfaces (RISs) have motivated the concept of the smart radio environment, according to which the propagation of information-bearing waveforms in the wireless medium is amenable to programmability. Although the vast majority of recent experimental research on RIS-empowered wireless communications gravitates around narrowband beamforming in quasi-free space, RISs are foreseen to revolutionize wideband wireless connectivity in dense urban as well as indoor scenarios, which are usually characterized as strongly reverberant environments exhibiting severe multipath conditions. In this article, capitalizing on recent physics-driven experimental explorations of RIS-empowered wave propagation control in complex scattering cavities, we identify the potential of the spatiotemporal control offered by RISs to boost wireless communications in rich scattering channels via two case studies. First, an RIS is deployed to shape the multipath channel impulse response, which is shown to enable higher achievable communication rates. Second, the RIS-tunable propagation environment is leveraged as an analog multiplexer to localize non-cooperative objects using wave fingerprints, even when they are outside the line of sight. Future research challenges and opportunities in the algorithmic design and experimentation of smart rich scattering wireless environments enabled by RISs for sixth Generation (6G) wireless communications are discussed.

Frequency-Invariant Beam Steering Based on FIR-Filters

Conventional narrowband beamforming methods are not effective for wideband antenna arrays. Radiation pattern of such array has various beam width over the frequency. Existing frequency-invariant beamforming methods using finite impulse response filters (FIR-filters) can solve this problem. FIR-filters provide frequency response required for frequency-independent properties of the array radiation pattern but their common feature is a high computational complexity. In this paper, we present the fixed frequency-invariant beamforming method that does not require minimizing functions or the Fourier transform. The beam steering is achieved by using FIR-filters with different slope of the frequency response. The proposed approach is based on the FIR-filters synthesis with special frequency characteristics. The numerical results compared with the existing frequency-invariant beamforming method are shown.

Secure Wideband Beamforming Design for Two-Way MIMO Relaying Systems

In this paper, secure wideband beamforming design is investigated for two-way multiple-input multiple-output (MIMO) relaying systems with one eavesdropper. Due to the wideband signals exchanged by two sources via the relay, inter-symbol-interference (ISI) occurs inevitably and makes the beamforming design more challenging than the narrowband beamforming design. To sufficiently combat the ISI, the tapped delay lines are deployed at the relay. Besides, to reduce the computational complexity at the tapped delay lines based relay, the analog beamforming structure is also considered, and thus the MIMO relay beamforming can be distinctly partitioned into a transmit beamforming and a receive beamforming bridged by a single link. Joint optimization of the transmit and receive beamformings at both the relay and two sources is considered to maximize the secrecy sum rate (SSR) of the two-way MIMO relaying systems. Since the joint optimization is nonconvex and intractable, an iterative algorithm is proposed to decompose the original SSR maximization problem into four individual subproblems that can be reformulated as three types of convex problems, i.e., the generalized Rayleigh quotient problems, the polynomial time differential convex problem, and the semidefinite programming problem. A suboptimal solution for the secure transmit and receive beamformings at all nodes can then be obtained iteratively. Finally, numerical experiments are conducted to corroborate the effectiveness of our proposed iterative algorithm in terms of the achievable maximum SSR and ISI suppression.

Narrowband Array Processing Beamforming Technique for Electrical Impedance Tomography.

Electrical impedance tomography (EIT) has a large potential as a two dimensional imaging technique and is gaining attention among researchers across various fields of engineering. Beamforming techniques stem from the array signal processing field and is used for spatial filtering of array data to evaluate the location of objects. In this work the circular electrodes are treated as an array of sensors and beamforming technique is used to localize the object(s) in an electrical field. The conductivity distributions within a test tank is obtained by an EIT system in terms of electrode voltages. These voltages are then interpolated using elliptic partial differential equations. Finally, a narrowband beamformer detects the peak in the output response signal to localize the test object(s). Test results show that the beamforming technique can be used as a secondary method that may provide complementary information about accurate position of the test object(s) using an eight electrode EIT system. This method could possibly open new avenues for spatial EIT data filtering techniques with an understanding that the inverse problem is more likely considered here as a source localization algorithm instead as an image reconstruction algorithm.

Application of waveform weighting for a frequency- invariant transmit beampattern

Advanced radar systems rely on multichannel, wideband operation. NASA's Ecosystem SAR (EcoSAR) and second-generation digital beamforming SAR [1]–[3] use active array architectures, where an active array radar system contains independent transmit and receive chains with unique amplifiers for each antenna element in the multichannel system. These radar systems have an independent arbitrary waveform generator for each channel and are designed to address wideband transmit beamforming. Unlike conventional narrowband beamforming, which only require a unique set of beamsteering coefficients per look angle, wideband beamforming requires beamsteering coefficients that vary with both look angle and frequency.

A 1 MHz BW 34.2 fJ/step Continuous Time Delta Sigma Modulator With an Integrated Mixer for Cardiac Ultrasound.

Fully digitized 2D ultrasound transducer arrays require one ADC per channel with a beamforming architecture consuming low power. We give design considerations for per-channel digitization and beamforming, and present the design and measurements of a continuous time delta-sigma modulator (CTDSM) for cardiac ultrasound applications. By integrating a mixer into the modulator frontend, the phase and frequency of the input signal can be shifted, thereby enabling both improved conversion efficiency and narrowband beamforming. To minimize the power consumption, we propose an optimization methodology using a simulated annealing framework combined with a C++ simulator solving linear electrical networks. The 3rd order single-bit feedback type modulator, implemented in a 65 nm CMOS process, achieves an SNR/SNDR of 67.8/67.4 dB across 1 MHz bandwidth consuming 131 [Formula: see text] of power. The achieved figure of merit of 34.2 fJ/step is comparable with state-of-the-art feedforward type multi-bit designs. We further demonstrate the influence to the dynamic range when performing dynamic receive beamforming on recorded delta-sigma modulated bit-stream sequences.

A High Performance Parallel and Heterogeneous Approach to Narrowband Beamforming

This paper describes a high performing, hybrid parallel, and heterogeneous algorithmic approach to narrowband Delay-Sum Beamforming (DSB) in the frequency domain using a Just-In-Time Asynchronous Data Method (JIT-ADM) parallel pattern. JIT-ADM is a novel asynchronous parallel programming pattern that unifies various levels of asynchronous concurrency available with distributed heterogeneous computing. The computational performance of this DSB algorithm was analyzed on a 50 node Cray XC30 with a single 10-core Intel Xeon E5-2670 v2 and NVIDIA Tesla K20X general purpose Graphics Processing Unit (GPU) on each node. The algorithm exhibits well behaved weak scalability with 92.7 percent parallel efficiency at 50 nodes compared to maximum performance observed. It is also shown that the algorithm efficiently utilizes a large portion of the available hardware. During beamforming the GPU is utilized at 51.8 percent of its maximum double precision floating point throughput whereas a comparable Central Processing Unit (CPU) version utilizes 60.0 percent of its maximum expected floating point throughput. Across the weak scalability study, utilizing GPUs for processing, a 2-5x performance gain is achieved compared to using CPUs. A brief derivation and validation of the implemented DSB is also presented.

Bayesian Deconvolution of Signals Observed on Arrays

Time series data collected from arrays of seismometers are traditionally used to solve the core problems of detecting and estimating the waveform of a nuclear explosion or earthquake signal that propagates across the array. We consider here a parametric exponentially modulated autoregressive model. The signal is assumed to be convolved with random amplitudes following a Bernoulli normal mixture. It is shown to be potentially superior to the usual combination of narrow band filtering and beam forming. The approach is applied to analyzing series observed from an earthquake from Yunnan Province in China received by a seismic array in Kazakhstan.

Performance analysis of narrowband beamforming using fully and partial adaptive beamformers with a spherical array

This research proposes an improved narrowband partial adaptive beamformer analysis using a proposed spherical array. Comparison between the fully and the partial adaptive beamformers is given. The study is performed by investigating performance parameters like the beamformer output signal-to-noise ratio and the beamformer output signal-to-interference-plus-noise ratio both in the steady state and along adaptation. Furthermore, computational complexity and convergence speed of the proposed sensor arrangement are also analyzed and examples are given. The results demonstrate that this beamformer considerably reduces the number of complex operations and features faster convergence speed.

Interference Rejection Degradation in Function of the DOA in a Beamforming System

This paper analyzes the interference rejection degradation in a narrowband beamforming system formed by a Uniform Linear Array (ULA), as a function of the Direction of Arrival (DOA) of the desired signal. To calculate the beamforming weights, the Recursive Least Squares (RLS) algorithm was used. For each DOA of the desired signal, the Signal to Noise plus Interference Ratio (SINR) is estimated. When the DOA of the desired signal is near to the sector edge, the SINR is significantly degraded because of less effectiveness in the interference mitigation algorithm. A quantitative estimation of this degradation is provided in this work. Some possible solutions to cope with this situation are discussed, and also some design criteria are provided.

ROBUST ADAPTIVE WIDEBAND BEAMFORMING USING PROBABILITY-CONSTRAINED OPTIMIZATION

The existing robust narrowband beamformers based on probability-constrained optimiza- tion have an excellent performance as compared to several state-of-the-art robust beamforming algo- rithms. However, they always assume that the steering vector errors are small enough. Without this assumption, we extend the probability-constrained approach to a wideband beamformer. In addition, a novel robust wideband beamformer with frequency invariance constraints is proposed by introducing the response variation (RV) element. Our problems can be reformulated in a convex form as the it- erative second order cone programming (SOCP) problem and solved effectively using well-established interior point method. Compared with existing robust wideband beamformers, a more efficient control over the beamformer's response against the steering vector errors is achieved with an improved output signal-to-interference-plus-noise ratio (SINR).

BEAM STEERING IN SMART ANTENNAS BY USING LOW COMPLEX ADAPTIVE ALGORITHMS

Array antenna systems are often used to enhance the received signal to interference and noise ratio wh en the signal operates in heavily jammed environment. Proper modeling of the received data at different antenna elements is impo rtant when evaluating the performance of this system, especially when both th e signal and interference have wide frequency bands .. The antenna output is the linear combination of data from all the antenna ele ments. In conventional narrowband beam forming, tim e sequences at different antenna elements are related by some fixed phase sh ift. The phase shift is determined by the wave form s direction of arrival (DOA). In this paper, an efficient method for the pattern synthesis of the linear antenna arrays with the pre scribed nulling and steering lobe is presented. The proposed method is based on Least Me an Square (LMS) algorithm provide a comprehensive atreatment of the signal model used for beam forming, as well as, describing adaptive algorithms to adjust the weigh ts of an array. In order to improve the convergence rate of LMS algorithm in sm art antenna system, in this paper we proposes a ne w normalized LMS (NLMS) algorithm, This new algorithm can be treated as a b lock based simplification of NLMS algorithm which gsatisfactory performance in certain applications in comparison w ith conventional NLMS recursion, i.e., BBNLMS algor ithm. By taking advantage of spatial filtering, the proposed scheme promises to reduce the bandwidth required for transmitting d ata by improving convergence rate. The performance of the BBNLMS algorithm in th e presence of Multi-path effects and multiple users is analyzed using MATLAB simulations. The simulations when compared to that of the LMS algorithm, the results suggest that BB NLMS algorithm can improve the convergence rate and lead to better system effi ciency.

Study of Sensor Positions for Broadband Beamforming

Recently, a sensor delay-line (SDL) based array structure was proposed for broadband beamforming. It is similar in form to the traditional narrowband beamformer, the only difference being a re-arrangement of the sensor positions according to the SDL concept. However, it is well-known that the narrowband beamformer can perform broadband beamforming to some degree, which gives rise to the question of whether this new arrangement is superior to the narrowband approach or not. The aim of this work is to answer this question by providing a detailed study of sensor positions for broadband beamforming. We show that when the bandwidth exceeds some threshold value, the SDL beamformer significantly outperforms the narrowband beamformer, which well justifies the new structure and gives a clear endorsement to the SDL concept.

A recursive Bayesian beamforming for steering vector uncertainties

A recursive Bayesian approach to narrowband beamforming for an uncertain steering vector of interest signal is presented. In this paper, the interference-plus-noise covariance matrix and signal power are assumed to be known. The steering vector is modeled as a complex Gaussian random vector that characterizes the level of steering vector uncertainty. Applying the Bayesian model, a recursive algorithm for minimum mean square error (MMSE) estimation is developed. It can be viewed as a mixture of conditional MMSE estimates weighted by the posterior probability density function of the random steering vector given the observed data. The proposed recursive Bayesian beamformer can make use of the information about the steering vector brought by all the observed data until the current short-term integration window and can estimate the mean and covariance of the steering vector recursively. Numerical simulations show that the proposed beamformer with the known signal power and interference-plus-noise covariance matrix outperforms the linearly constrained minimum variance, subspace projection, and other three Bayesian beamformers. After convergence, it has similar performance to the optimal Max-SINR beamformer with the true steering vector.

PERFORMANCE EVALUATION OF DFT BEAMFORMERS FOR BROADBAND ANTENNA ARRAY PROCESSING

Broadband beamforming has been an important issue on antenna array processing due to many practical demands on communication, radar, or sonar applications. Although several effects deteriorating array performance have been addressed for narrowband beamforming, few of them are considered for the broadband scenario. Besides, the definition of output signal-to-interference plus noise ratio (SINR) and the way to simulate broadband signal sources are usually vague, which further obstructs the development of broadband beamforming. In this paper, the performance of discrete Fourier transform (DFT) beamformers operating in block processing and sliding window modes are investigated when the correlation matrices are known or estimated by finite data samples. The output SINR of DFT beamformers is well-defined, and the generation of broadband signals is clearly introduced. Simulation results with respect to the signal bandwidth, the number of frequency bins, and the number of data samples are presented for illustration and comparison.

A Rao–Blackwellized Particle Filter for Adaptive Beamforming With Strong Interference

A particle filter approach is proposed for adaptive narrowband beamforming in the presence of strong interference and uncertain steering vector of interest signal. From the viewpoint of subspace decomposition, we first describe the known subspace projection beamformer as an orthogonal component of the steering vector that is perpendicular to the interference space. Then a Rao-Blackwellized particle filter algorithm is designed to estimate the subspace projection beamforming weights. All steps including the important sampling, weight updating, resampling and analytical computation have been discussed in detail. Finally, the overall adaptive beamforming algorithm is summarized and the computational complexity analysis is presented. The main contribution of this paper is to apply and formulate Rao-Blackwellized particle filtering to estimate the distribution over the subspace projection beamforming weights. Different from other two particle-filter-based beamformers proposed by Li and Chandrasekar, it is a STI-based method with unknown source steering vector. The sampled state variables are the signal power, noise power and a model parameter for beamformer weights transition; the marginalized analytical state variables are the subspace projection beamforming weights; and the measurements are a series of constructed signal samples by using the estimated projection operator and random noise loading. Numerical simulations show that the proposed beamformer outperforms linearly constrained minimum variance, subspace projection, Bayesian and other two particle-filter-based beamformers. After convergence, it has similar performance to the optimal max-SINR beamformer which uses the true steering vector and ideal interference-plus-noise covariance matrix.

Robust Adaptive Beamforming Using Multidimensional Covariance Fitting

Over the last decade, several set-based worst-case beamformers have been proposed. It has been shown that some of these beamformers can be formulated equivalently as one-dimensional (ID) covariance fitting problems. Based on this formulation, we show that these beamformers lead to inherently nonoptimum results in the presence of interferers. To mitigate the detrimental effect of interferers, we extend the ID covariance fitting approach to multidimensional (MD) covariance fitting, modeling the source steering vectors by means of uncertainty sets. The proposed MD covariance fitting approach leads to a nonconvex optimization problem. We develop a convex approximation of this problem, which can be solved, for example, by means of the logarithmic barrier method. The complexity required to compute the barrier function and its first- and second-order derivatives is derived. Simulation results show that the proposed beamformer based on MD covariance fitting achieves an improved performance as compared to the state-of-the-art narrowband beamformers in scenarios with large sample support.

Beamforming Effects on Measured mm-Wave Channel Characteristics

Beamforming is an important feature of 60 GHz communications. We present an analysis of the influence of beamforming in indoor ultrawideband radio channels measured in the mm-wave 60 GHz band. The performance of narrowband and wideband direction-based beamformers is investigated in terms of improving channel metrics such as the delay spread, excess delay, and the signal-to-noise ratio (SNR). The performance of the direction-based beamformers is compared with dominant eigenmode transmission and statistical beamforming. Our analysis reveals that in line-of-sight (LOS) scenarios, the two direction-based beamformers have a similar performance that approaches the upper bound set by dominant eigenmode transmission. In non-LOS (NLOS) scenarios, the direction-based beamformers show a performance degradation in relation to the upper bound, with the narrowband beamformer worse off than the wideband variant. The array gain in our measured NLOS scenarios is observed to exceed the theoretical upper limit valid for a rich scattering environment. We show that this result follows from the spatial structure of the measured NLOS channels that has only a few strong reflected components. We investigate the influence of array size on beamforming performance; 5×5 planar arrays are observed to improve the channel's delay metrics as well as the larger 7×7 planar arrays.

A wideband beamforming method based on directional uniform circular arrays

A new method for solving the wideband beamforming problem is proposed in this paper based on directional uniform circular arrays. The basic idea is designing a narrowband beamforming spatial filter at a specific frequency and using the obtained filter weights directly to wideband signals, which reduces the complexities of designing and processing. Under the premise of knowing signals’ direction information, this paper uses the method of least square with linear constraints to design the narrowband spatial filter. Since the directional uniform circular array is insensitive to the variation of frequency, coincident beams and notches are obtained at different frequencies of the wideband signal by using the obtained narrowband spatial filter weights. The computation complexity of array signal processing can be greatly reduced by using the method proposed in this paper, which is convenient for real-time signal processing.