Beamforming Framework Research Articles

Decentralized Beamforming Design for Intelligent Reflecting Surface-Enhanced Cell-Free Networks

Cell-free networks are considered to be a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems. However, to further enhance network capacity, an increasing number of high-cost base stations (BSs) is required. To address this problem and inspired by the cost-effective intelligent reflecting surface (IRS) technique, we propose a fully decentralized design framework for cooperative beamforming in IRS-aided cell-free networks. We first transform the centralized weighted sum-rate maximization problem into a tractable consensus optimization problem, and then an incremental alternating direction method of multipliers (ADMM) algorithm is proposed to locally update the beamformer. The complexity and convergence of the proposed method are analyzed, and these results show that the performance of the new scheme can asymptotically approach that of the centralized one as the number of iterations increases. Results also show that IRSs can significantly increase the system sum-rate of cell-free networks and the proposed method outperforms existing decentralized methods.

Convolutional Maximum-Likelihood Distortionless Response Beamforming With Steering Vector Estimation for Robust Speech Recognition

Beamforming has been one of the most successful approaches using multi-microphones for robust speech recognition. Although a beamforming method, called the “maximum-likelihood distortionless response (MLDR)” beamformer, was recently presented to achieve promising performance, it requires an accurate steering vector for a target speaker in advance like many kinds of beamformers. In this paper, we present a method for steering vector estimation (SVE) by replacing the noise spatial covariance matrix estimate with a normalized version of the variance-weighted spatial covariance matrix estimate for the observed noisy speech signal obtained by the iterative update rule in the MLDR beamforming framework. In addition, an MLDR beamforming method without a steering vector for a target speaker given in advance is presented where the SVE and the beamforming are alternately repeated. Furthermore, an online algorithm based on recursive least squares (RLS) is derived to cope with various practical applications including time-varying situations, and the power method is introduced for further efficient online processing. We also present batch and online convolutional MLDR beamforming with SVE for simultaneous beamforming and dereverberation where the weighted prediction error (WPE) dereverberation and the MLDR beamforming with the SVE were jointly optimized based on the maximum-likelihood estimation (MLE) for a zero-mean complex Gaussian signal with time-varying variances. Moreover, input signals masked by a neural network (NN) for estimating target speech or noise components can be used to further improve the presented beamformers. Experimental results on the CHiME-4 and REVERB challenge datasets demonstrate the effectiveness of the presented methods.

Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO

Machine learning for hybrid beamforming has been extensively studied by using centralized machine learning (CML) techniques, which require the training of a global model with a large dataset collected from the users. However, the transmission of the whole dataset between the users and the base station (BS) is computationally prohibitive due to limited communication bandwidth and privacy concerns. In this work, we introduce a federated learning (FL) based framework for hybrid beamforming, where the model training is performed at the BS by collecting only the gradients from the users. We design a convolutional neural network, in which the input is the channel data, yielding the analog beamformers at the output. Via numerical simulations, FL is demonstrated to be more tolerant to the imperfections and corruptions in the channel data as well as having less transmission overhead than CML.

Positioning-Aided 3D Beamforming for Enhanced Communications in mmWave Mobile Networks

The extension from centimeter wave frequencies to millimeter wave (mmWave) frequencies has triggered an enormous transformation in terms of radio access architecture for future wireless networks, and it has therefore empowered unlimited opportunities for the user-oriented services and applications. Besides mmWave as a driving element, beamforming (BF) will be incorporated as a key enabling technology for the future wireless networks. In this paper, we propose a positioning-aided beamforming (PA-BF) framework for enhanced downlink communications in a cloud-oriented mmWave mobile networks. We show that the proposed PA-BF achieves a higher effective transmit ratio that is equivalent to a lower initial access latency than the conventional codebook-based BF, which in turn manifests its capability to support high-velocity mobile users. We also analyze the impact of positioning accuracies on the performance of PA-BF and discuss the trade-offs between different BF strategies with varied system parameters. Our simulation results demonstrate that, with a narrow beam phased array and reasonably good positioning accuracy, the PA-BF framework is capable of achieving higher spectral efficiency than the considered codebook-based BF especially at higher velocities.

Beamforming Design for In-Band Full-Duplex Multi-Cell Multi-User MIMO LSA Cellular Networks

The in-band full-duplex (IBFD) mechanism offers many advantages over standard half duplex (HD) systems: it can potentially double the capacity and increase spectrum utilization efficiency. While IBFD renders many conventional research problems irrelevant, it unearths several new problems that urgently need attention. We focus on a the design of a Licensed Shared Access (LSA) based spectrum sharing framework with IBFD multi-cell multi-user multiple-input multiple-output (MIMO) communication network as the licensee, which operates in the service region of a multi-user MIMO incumbent network. We propose a modified LSA controller framework for the protection zone scenario of LSA, whereby the LSA controller ensures that the licensees always have access to data rates above a given threshold, for which aggregate interference towards the incumbents is also kept at minimum. Accordingly, we propose a hierarchical two-step beamforming framework, where i) the LSA controller first seeks the quality of service (QoS) requirements from the licensee network. Based on the QoS requirements, beamformers are designed to minimize the aggregate interference towards the incumbents. ii) In the second step, the interference minimized in the first step is considered as a constraint for the new beamformer design problem that maximizes the aggregate uplink and downlink throughput of the licensee network. Numerical results demonstrate the precedency of the proposed algorithms for IBFD LSA when compared to baseline HD LSA, whereby we observe i) over 60% improvement in throughput performance for the licensee network and ii) a reduction in interference exposure towards the incumbents from the licensees by at least 6.8315dB.

Harmonic beamformers for speech enhancement and dereverberation in the time domain

This paper presents a framework for parametric broadband beamforming that exploits the frequency-domain sparsity of voiced speech to achieve more noise reduction than traditional nonparametric broadband beamforming without introducing additional distortion. In this framework, the harmonic model is used to parametrize the signal of interest by a single parameter, the fundamental frequency, whereby both speech enhancement and derevereration can be performed. This framework thus exploits both the spatial and temporal properties of speech signals simultaneously and includes both fixed and adaptive beamformers, such as (1) delay-and-sum, (2) null forming, (3) Wiener, (4) minimum variance distortionless response (MVDR), and (5) linearly constrained minimum variance beamformers. Moreover, the framework contains standard broadband beamforming as a special case, whereby the proposed beamformers can also handle unvoiced speech. The reported experimental results demonstrate the capabilities of the proposed framework to perform both speech enhancement and dereverberation simultaneously. The proposed beamformers are evaluated in terms of speech distortion and objective measures for speech quality and speech intelligibility, and are compared to nonparametric broadband beamformers. The results show that the proposed beamformers perform well compared to traditional methods, including a state-of-the-art dereverberation method, particularly in adverse conditions with high amounts of noise and reverberation.

Secrecy Energy Efficiency for MIMO Single- and Multi-Cell Downlink Transmission With Confidential Messages

This work develops a beamforming framework for energy efficiency optimization in MIMO multi-user systems with confidentiality constraints. Two channel models are considered, namely, a broadcast channel with confidential messages (corresponding to single-cell downlink) and an interference channel with confidential messages (corresponding to multi-cell downlink), in which multiple messages are transmitted, and it must be ensured that only the intended receiver is able to perform data decoding, thus treating non-intended receivers as potential eavesdroppers. In this multi-user scenario, the new metric global secrecy energy efficiency is introduced and optimized. Moreover, the coupling among the secrecy energy efficiencies of different users is analyzed by providing an efficient way of computing the system secrecy energy efficiency Pareto boundary. Both contributions are achieved by developing an optimization framework which suitably combines fractional programming theory and sequential optimization theory. The proposed framework is provably convergent, enjoys affordable complexity, and fulfills first-order optimality properties. Finally, the closed form conditions are provided to support smart user selection algorithms for downlink transmission.

MUSIC-Like Algorithm for Source Localization in Electrical Impedance Tomography

In electrical impedance tomography (EIT), the noise amplified solution caused during matrix inversion can be avoided with nonparametric spectral-based estimation when the conductivity variation is bounded and spatially sparse. Among many spectral-based algorithms used in direction-of-arrival estimation, an algorithm called multiple signal classification (MUSIC) is one of the most well-known algorithms that has super resolution performance. However, its dependence on the model-order estimation can lead to performance degradation, especially for quasi-static environment, such as EIT application, and this is due to source location changes and conductivity variation. In this paper, the relationship between source position, conductivity variation, ill-conditioned array manifold, and eigenvalues of the covariance matrix are explored. An algorithm called MUSIC-like, which has high resolution performance comparable to MUSIC, is then proposed for EIT application. It is formulated under the beamforming framework and, therefore, does not require an estimation of model order from the covariance matrix. Simulation results show that the proposed method is capable of obtaining high resolution performance under various noise levels. An 8-electrode EIT system prototype was built using the proposed method, and experimental results confirm the high resolution performance capability of the proposed method.

Reducing the Codeword Search Complexity of FDD Moderately Large MIMO Beamforming Systems

It is of great interest to develop larger scale multiple-input multiple-output (MIMO) technology based on frequency division duplexing (FDD) for backward compatibility with existing standards and already deployed cellular networks. One hindrance to the FDD approach is the complexity of the codeword search performed by the receiver, which is known to scale with the number of transmit antennas $N_{t}$ in a system. The codeword search complexity does not scale linearly and soon becomes infeasible even for the not-so-massive MIMO systems expected in the very near future. We address this problem by proposing a natural sequence-based orthogonal codebook and an intra-array interference magnitude (IIM) framework for MIMO beamforming based on quantized equal gain transmission that altogether tremendously simplifies the codeword search problem. We also present an assessment on the quality of the codeword selected by the proposed framework that explains it’s near optimal performance, and, provide detailed codeword search complexity quantification. Finally, we provide simulation analyses of beamforming gain under time selective fading channels and an FDD MIMO system transmit delay model. Our analyses indicate that despite significantly reducing complexity, the proposed IIM framework executed by already commercialized processors is capable of approaching the ideal performance of an static system even in the presence of time-selectivity inherent to high-speed mobile communications. Our analyses also quantify the impact of feedback delay on average beamforming gain of moderately large transmit arrays.

Adaptive beamforming for uniformly-spaced linear hydrophone array using temporal convolutional neural networks

In oceanic remote sensing, large discrete linear hydrophone arrays are usually utilized to enhance the signal-to-noise ratio (SNR) by means of beamforming that attenuates noise coming from the directions outside the target direction. Although the widely adopted delay-and-sum (DS) and filter-and-sum (FS) beamforming techniques can improve the performance of linear hydrophone arrays in some scenarios, both DS and FS methods have limited adaptability to the changing oceanic environments especially with spatially correlated noise. Moreover, these beamforming techniques aims to optimize the SNR, which is not completely consistent with some objectives such as target recognition. In this work, a neural network adaptive beamforming framework for an uniformly-spaced linear hydrophone array is proposed to make use of the large-scale array signals and address the above issues. In particular, an architecture consists of temporal convolutional neural networks is designed to predict the beamforming filter coefficients in time domain by taking the raw multi-channel waveforms of sound pressure as the input. The filter prediction networks are also jointly trained with a convolutional neural network (CNN) based classification model with the purpose of increasing the target recognition accuracy. The proposed approach is validated by experiments carried out in the south coastal waters of China.

Fourier-Domain Beamforming and Structure-Based Reconstruction for Plane-Wave Imaging.

Ultrafast imaging based on coherent plane-wave compounding is one of the most important recent developments in medical ultrasound. It significantly improves the image quality and allows for much faster image acquisition. This technique, however, requires large computational load motivating methods for sampling and processing rate reduction. In this work, we extend the recently proposed frequency-domain beamforming (FDBF) framework to plane-wave imaging. Beamforming in frequency yields the same image quality while using fewer samples. It achieves at least fourfold sampling and processing rate reduction by avoiding oversampling required by standard processing. To further reduce the rate, we exploit the structure of the beamformed signal and use compressed sensing methods to recover the beamformed signal from its partial frequency data obtained at a sub-Nyquist rate. Our approach obtains tenfold rate reduction compared with standard time-domain processing. We verify performance in terms of spatial resolution and contrast based on the scans of a tissue mimicking the phantom obtained by a commercial Aixplorer system. In addition, in vivo carotid and thyroid scans processed using standard beamforming and FDBF are presented for qualitative evaluation and visual comparison. Finally, we demonstrate the use of FDBF for shear-wave elastography by generating velocity maps from the beamformed data processed at sub-Nyquist rates.

Robust Chance-Constrained Secure Transmission for Cognitive Satellite–Terrestrial Networks

Cognitive satellite–terrestrial networks (CSTNs) have been recognized as a promising network architecture for addressing spectrum scarcity problem in next-generation communication networks. In this paper, we investigate the secure transmission for CSTNs where the terrestrial base station serving as a green interference resource is introduced to enhance the security of the satellite link. Adopting a stochastic model for the channel state information uncertainty, we propose a secure and robust beamforming framework to minimize the transmit power, while satisfying a range of outage (probabilistic) constraints concerning the signal-to-interference-plus-noise ratio (SINR) recorded at the satellite user and the terrestrial user, the leakage-SINR recorded at the eavesdropper, as well as the interference power recorded at the satellite user. The resulting robust optimization problem is highly intractable and the key observation is that the highly intractable probability constraints can be equivalently reformulated as the deterministic versions with Gaussian statistics. In this regard, we develop two robust reformulation methods, namely $\mathcal{S}$ -procedure and Bernstein-type inequality restriction techniques, to obtain a safe approximate solution. In the meantime, the computational complexities of the proposed schemes are analyzed. Finally, the effectiveness of the proposed schemes are demonstrated by numerical results with different system parameters.

Computationally efficient direction of arrival estimation with unknown number of signals

In this paper, we investigate the problem of direction of arrival (DOA) estimation with unknown number of signals in the framework of beamforming. We show that the real part of the array output covariance matrix (R-AOCM) can be reformulated as an entire AOCM of a virtual array with available signal model for fast DOA estimate. By introducing an optimization problem to minimize the variance of the weighted output of this virtual array, DOA can be found by a novel real-valued real part Capon (R-Capon) estimator accordingly. Moreover, we prove that the rank of the R-AOCM is always no less than that of the entire AOCM, which suggests that R-Capon outperforms the standard Capon in scenarios with small numbers of snapshots. We also prove that the inverse of the R-AOCM can be equivalently jointed by those of two sub-matrices of about half sizes, and hence R-Capon has a significantly reduced computational complexity. These advantages as well as the theoretical analysis are finally verified by numerical simulations over a wide range of scenarios.

Hybrid Beamforming via the Kronecker Decomposition for the Millimeter-Wave Massive MIMO Systems

Despite its promising performance gain, the realization of mmWave massive MIMO still faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds of RF chains matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of hybrid-beamforming where MIMO processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming, respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients, rendering the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the AoA of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed-form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length and the spatial separation between paths.

DOA Estimation Based on Reduced-rank Multistage Wiener Filter

A new DOA estimator based on the reduced-rank multistage Wiener filter (MWF) is proposed this paper. This method estimates the DOAs of the impinging signals in the framework of beamforming and the number of sources (NOS) is hence not required. Besides improving performance over the classical subspace-based methods under small sample size condition, the new algorithm offers a significant reduction in computational complexity. In addition, a computation-free rank selection criterion is developed for the reduced-rank MWF, which plays the role of determining the NOS in the subspace-based methods. This criterion has better performance than the conventional information theoretic criteria when the sample size is small.

Spoofing mitigation in Global Positioning System based on C/A code self-coherence with array signal processing

Spoofing is a deliberate attack that can coerce Global Positioning System (GPS) receivers into providing false position/navigation solutions. Spoofing is more dangerous than jamming since the target receiver is not aware of this menace. In this paper, a novel signal processing scheme is proposed for rejecting the repeat spoofing, which is based on the strong self-coherence property of the C/A code. Firstly, the number and Direction-of-arrivals (DOAs) of spoofing attacks are estimated by the MUSIC-Like DOA estimation method based on the Framework of Beamforming (MLFB). Then, the constrained version of cross spectral self-coherence restoral (cross-SCORE) algorithm is applied to mitigate the spoofing. The proposed scheme can simultaneously mitigate spoofing and adaptively provide high beamforming gain for authentic signal without the prior information of DOAs of the authentic signal. Moreover, the scheme can be used as an independent spoofing mitigation module that can be embedded in the GPS receiver. Simulation experiments demonstrate the effectiveness of the proposed scheme.

Spatiotemporal-MIMO Channel Estimator and Beamformer for 5G

With requirements of spiraling data rates and limited spectrum availability, there is an increased interest in mm-wave beamformer-based communications for 5G. For upcoming cellular networks, the critical point is to exploit the increased number of employable antennas at both Tx and Rx to: 1) combat increased path loss; 2) tackle higher interference due to higher user density; and 3) handle multipath effects in frequency selective channels. Toward this, a multi-beam spatiotemporal superresolution beamforming framework is proposed in this paper as a promising candidate to design beampatterns that mitigate/suppress co-channel interference and deliver massive gain in the desired directions. Initially, channel and signal models suitable for the mm-wave MIMO system are presented using the manifold vectors of both Tx and Rx antenna arrays. Based on these models, a novel subspace-based channel estimator is employed, which estimates delays, directions, velocities, and fading coefficients of the desired signal paths. This information is then exploited by the proposed spatiotemporal beamformer to provide a massive array gain that combats path loss without increasing the number of antenna array elements and to be tolerant to the near-far problem in a high interference environment. The performance of the proposed channel estimator and beamformer is examined using computer simulation studies.

Hybrid secure beamforming and vehicle selection using hierarchical agglomerative clustering for C-RAN-based vehicle-to-infrastructure communications in vehicular cyber-physical systems

The architecture of C-RAN-based soft-defined vehicular networks can support increasing demands of various applications in the future vehicular cyber-physical systems, but lacks strong and practical security–enhancing mechanisms in the physical layer. In this article, we propose a hybrid beamforming and vehicle-selection framework for vehicle-to-infrastructure communications to broadcast high-speed confidential messages. A one-dimensional roadway scenario having multiple lanes is considered, and the concept of secure region and interference-selection region is, respectively, developed for each roadside unit. We formulate an optimization problem of maximizing secrecy sumrate with the constraint of orthogonality-based vehicle selection. The original problem is then divided into two subproblems subtly. In terms of the first subproblem, we, by exploiting the orthogonality assumption, obtain a corresponding secure beamformer for each vehicle located in the secure region to reduce the interference and confidential information leakage. Based on secure beamformers, the second subproblem is changed to be a vehicle-selection problem, which is then solved by adopting hierarchical agglomerative clustering method and implemented by two proposed novel algorithms blending vehicle selection with beamformer generation. Simulation results verify the effectiveness of the proposed algorithms in the respect of secrecy sumrate compared with the conventional zero-forcing beamforming using semi-orthogonal user selection algorithms. Furthermore, simulations show how our proposed algorithms resist against eavesdropping from collusive vehicles located in the same secure region by flexibly altering the size of secure region and interference-selection according to the traffic density.

Modeling and Analysis of Opportunistic Beamforming for Poisson Wireless Networks

This paper introduces a model to study both single tier and multitier wireless communication systems consisting of a multitude of wireless access points (AP), and operating according to the classical opportunistic beamforming framework. The AP locations in the proposed network model are determined by using planar Poisson point processes. The extreme value distribution of signal-to-interference-plus-noise-ratio ( ${\rm{ SINR}}$ ) on a beam is of fundamental importance for obtaining performance bounds for such an opportunistic communication system. Two tight distribution approximation results are provided for the distribution of maximum ${\rm{ SINR}}$ on a beam, which is hard to obtain due to correlation structure of the underlying inter-AP interference field, using key tools from stochastic geometry. These approximations hold for general path loss models that satisfy some mild conditions. Simulations and numerical evaluations are presented to validate the results, to provide further insights into the derived approximate maximum beam ${\rm{ SINR}}$ distributions, and to illustrate the utility of these approximations in obtaining performance bounds for opportunistic communication systems having multiple interfering APs. In particular, key performance measures such as beam outage probability and ergodic aggregate data rate of an AP are derived by utilizing the approximated distributions.

Cooperative Beamforming for Physical-Layer Security in Power-Constrained Wireless Sensor Networks with Partial Relay Selection

We investigate the physical-layer security of a wireless sensor network comprised of a source-destination pair, multiple decode-and-forward (DF) relays, and an eavesdropper. When the relays forward the source signal to the destination, they should dissipate certain power for driving their physical circuits (e.g., amplifiers and filters), in addition to the transmit power required for emitting radio signals. However, no existing work considers the circuit power when exploiting relays for enhancing the wireless security against eavesdropping. We thus propose the relay-selection based cooperative beamforming framework, where partial relays are selected to forward the source signal in a beamforming manner. We present two relay-selection strategies, namely, the exponential-complexity exhaustive search and linear-complexity relay ordering. For comparison purposes, we also evaluate the conventional all-relay based and best-relay based beamforming schemes, where all relays or only the best one is selected to assist the source-destination transmission. It is shown that the proposed beamforming schemes significantly outperform the conventional all-relay and best-relay based beamforming schemes in terms of secrecy capacity. Additionally, the relay-ordering based beamforming scheme almost achieves the same secrecy capacity as the exhaustive-search method, but with a reduced computational complexity.