Digital Beamforming Algorithm Research Articles

Improved Convolutional Neural Network for Wideband Space-Time Beamforming

Wideband beamforming technology is an effective solution in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems to compensate for severe path loss through beamforming gain. However, traditional adaptive wideband digital beamforming (AWDBF) algorithms suffer from serious performance degradation when there are insufficient signal snapshots, and the training process of the existing neural network-based wideband beamforming network is slow and unstable. To address the above issues, an AWDBF method based on the convolutional neural network (CNN) structure, the improved wideband beamforming prediction network (IWBPNet), is proposed. The proposed method increases the network’s feature extraction capability for array signals through deep convolutional layers, thus alleviating the problem of insufficient network feature extraction capabilities. In addition, the pooling layers are introduced into the IWBPNet to solve the problem that the fully connected layer of the existing neural network-based wideband beamforming algorithm is too large, resulting in slow network training, and the pooling operation increases the generalization ability of the network. Furthermore, the IWBPNet has good wideband beamforming performance with low signal snapshots, including beam pattern performance and output signal-to-interference-plus-noise ratio (SINR) performance. The simulation results show that the proposed algorithm has superior performance compared with the traditional wideband beamformer with low signal snapshots. Compared with the wideband beamforming algorithm based on the neural network, the training time of IWBPNet is only 10.6% of the original neural network-based wideband beamformer, while the beamforming performance is slightly improved. Simulations and numerical analyses demonstrate the effectiveness and superiority of the proposed wideband beamformer.

A framework for preventing unauthorized drone intrusions through radar detection and GPS spoofing

The increasing use of Global Positioning System (GPS)-based autonomous drones in various civilian and military applications has raised concerns about malicious or unintentionally harmful activities that can be carried through them. It is necessary to detect these intruding drones within protected areas and prevent their unauthorized access by denying them entry. We propose a framework that combines the detection of intruding drones using an L-band radar and then counters by transmitting fake GPS coordinates toward the drones, effectively redirecting them. This article explains the setup required to add to an existing monostatic radar that provides two-dimensional information, i.e., range and azimuth information, to enable the proposed setup to get the elevation angle of the drone. We propose a linear array design using digital receive-only beamforming techniques in the elevation domain to compute the elevation angle in addition to the range, velocity, and azimuth information being provided by the monostatic radar to get complete information about the intruding drone. The simulation of drone detection is followed by an examination of the impact of transmitting fabricated GPS coordinates to the drone. Experimental verification has been conducted to validate both the digital beamforming algorithm and the spoofing technique. This approach blocks the reception of actual GPS signals in the drone and replaces the drone's GPS coordinates with alternative, desired coordinates. The proposed framework can be used to prevent unauthorized drone intrusions in the protected area.

Design of a Digital Array Signal Processing System with Full Array Element

For the application of a digital array signal processing system with a high sampling rate and large bandwidth, and considering the constraints of limited resources (performance, volume, power consumption, weight, etc.), this paper proposes a design and implementation method of broadband digital array signal processing system based on radio frequency system on a chip. The system focuses on multi-channel synchronization under a high sampling rate and multi-channel amplitude and phase calibration under large bandwidth to form an all-digital, low-power, multi-channel, high-speed, large bandwidth hardware platform suitable for broadband digital array signal processing. Through the implementation of the Multiple-Input Multiple-Output phased-array digital beamforming algorithm, the test conducted verifies that the system has superior signal processing performance of the broadband digital array.

Hybrid Beamforming for Multi-User Millimeter-Wave Heterogeneous Networks

Millimeter-wave (mmWave) communications are a critical technique with next-generation network characteristics such as ultra-dense small cells can meet the skyrocketing demand for mobile data. Hybrid precoding, which combines analog and digital processing to provide both spatial diversity gains and beamforming, is commonly studied for mmWave communications to lower the power and cost consumption of radio frequency (RF) networks. However, the combination of ultra-dense small cells and ever-increasing data traffic results in massive interference. In this paper, we propose a minimum mean square error (MMSE)-based hybrid beamforming strategy for downlink mmWave MIMO two-tier heterogeneous networks (HetNets). The analog beamforming is generalized by an orthogonal matching pursuit technique. The analog beamforming problem is formulated as a sparse signal recovery problem. An MMSE-based digital beamforming algorithm is proposed to minimize the sum MSE of the user-intended data streams so that the inter- and intra-tier interferences are mitigated iteratively. The simulation results demonstrate the advantageous performance of the proposed hybrid beamforming schemes under different cellular cooperation and data transmission scenarios when hardware constraints are taken into account.

Attitude Information Aided Digital Beamforming in Millimeter-Wave MIMO Systems

Attitude information can be utilized for beam tracking in millimeter-wave (mmWave) analog beamforming. It provides the direction of arrival (DOA) information of the user equipment (UE) when it rotates, and thus, significantly reduces the overhead of beam searching. However, in digital beamforming (DBF), the transceivers are not designed based on DOAs, which makes it more challenging to utilize attitude information to reduce system overhead. In this article, we propose an attitude information aided DBF algorithm. Specifically, based on the clustered channel model, we derive the relationship between channel variation and the attitude of UE during the rotation. Furthermore, it is proved that only the transmitter at UE requires to be redesigned after the rotation, while the receiver at base station does not. Compared with the joint transceiver design, the performance loss is generally trivial. Therefore, we propose an AIA digital transmitter design algorithm for uplink transmission with equal power allocation. The spectral efficiency performance can approximate the optimal joint transceiver design performance without real-time channel state information. We also extend the method to unequal power allocation case and propose the algorithm with water-filling power allocation. Finally, simulation results are presented to examine the effectiveness of proposed algorithms.

Spatial deception suppression for wideband linear frequency modulation signals based on fractional Fourier transform with robust adaptive beamforming

Wideband linear frequency modulation (LFM) signal is popular for target detection and imaging in wideband array radar systems. In this paper, a novel and robust adaptive wideband digital beamforming (DBF) algorithm is proposed for suppressing the deception jamming of wideband LFM signals. Firstly, wideband LFM echoes are treated as narrowband signals using fractional Fourier transform (FrFT) with a specific order. Secondly, a robust and adaptive narrowband DBF method based on steering vector estimation, covariance matrix reconstruction, and low-pass filtering are applied to suppress deception jamming. Finally, the inverse FrFT is performed to transform the processing results back to the time-domain wideband signals. Compared with the conventional time- and frequency-domain wideband beamformers, the proposed algorithm significantly reduces the amount of calculation and computational complexity. The simulation results indicate that the proposed algorithm can effectively suppress deception jamming, and its interference suppression capability is approximately like that of an optimal beamformer. Furthermore, the validity and effectiveness of the proposed algorithm are verified with the experimental data in a microwave anechoic chamber.

Two-Timescale Hybrid Analog-Digital Beamforming for mmWave Full-Duplex MIMO Multiple-Relay Aided Systems

Due to the severe pathloss experienced by electromagnetic waves in the millimeter wave (mmWave) band, a substantial challenge in their design is to have an adequate coverage area. With the objective of improving the coverage area and the sum rate attained, we conceive new full-duplex (FD) mmWave multiple-input multiple-output (MIMO) multiple-relay systems. Specifically, we propose a novel two-timescale analog-digital hybrid beamforming scheme for maximizing the sum rate, while reducing the system's complexity and the channel state information (CSI) signalling overhead, as well as mitigating both the effects of self-interference and that of outdated CSIs caused by the associated delays. In the proposed scheme, the long-timescale analog beamforming matrices are designed based on the available channel statistics and updated in a frame-based manner, where a frame contains a fixed number of time slots. By contrast, the short-timescale digital beamforming matrices are optimized more frequently - namely for each time slot - based on the low-dimensional effective CSI matrices available on a real-time basis. We develop both an efficient analog beamforming algorithm based on the cut-set bound as well as on stochastic successive convex approximation (SSCA) and an innovative digital beamforming algorithm that relies on the theory of penalty dual decomposition (PDD), where our design objective is to maximize the system's sum rate. Both the convergence properties and the computational complexity of the proposed algorithms are also examined. Our simulation results show that the proposed two-timescale hybrid beamforming design significantly outperforms the conventional beamformers both in terms of requiring a lower CSI-signalling overhead and a higher sum rate in the face of realistic outdated CSIs.

A Minimum-Energy-Based Algorithm for Multichannel Reconstruction

For the azimuth multichannel synthetic aperture radar (SAR), nonuniform sampling is common since the pulse-repetition frequency cannot be selected arbitrarily, which leads to the contradiction between ambiguity and noise suppression in the azimuth signal reconstruction. This letter constructs a novel multichannel signal model by taking the coherence among different Doppler bins into account and proposes a minimum-energy-based multichannel reconstruction algorithm, which can obtain the unambiguous signal spectrum without increasing noise in the nonuniform sampling cases. By comparing the simulation results formed by digital beamforming (DBF), improved DBF (IDBF), and space-time adaptive processing (STAP) algorithms, the proposed algorithm shows better performance in balancing ambiguity and noise.

PRF Selection in Formation-Flying SAR: Experimental Verification on Sentinel-1 Monostatic Repeat-Pass Data

Formation-flying synthetic aperture radar (FF-SAR) enables new working modes and can achieve very high performance through a series of very compact, low-weight, satellite platforms thanks to passive operations of conveniently distributed formation-flying receivers. System timing is a crucial aspect of FF-SAR. The manuscript presents a novel approach to pulse repetition frequency (PRF) selection in order to obtain a uniform distribution of samples at given platform positions. A digital beamforming algorithm is applied on a stack of monostatic repeat-pass images collected by the Sentinel-1 system to test the validity of the PRF selection method. Processed images were thus properly selected to achieve the best merit index measuring the quality of samples distribution. The results show that: (a) the image resulting from beamforming features better azimuth ambiguity-to-signal ratio and (b) the proposed approach for PRF selection allows one to individuate a subset of the available images leading to uniform distribution of samples which can be used to support FF-SAR processing.

Performance Evaluation of Low Complexity Digital Beamforming Algorithms by Link-Level Simulations and Outdoor Experimental Trials for 5G Low-SHF-Band Massive MIMO

Performance Evaluation of Low Complexity Digital Beamforming Algorithms by Link-Level Simulations and Outdoor Experimental Trials for 5G Low-SHF-Band Massive MIMO

MIMO Transceiver based on Software Defined Radio

Amplitude-phase consistency of multiple input multiple output (MIMO) transceivers has a significant impact on the performance of digital beamforming algorithms and MIMO communication algorithms. Based on the concept of software defined radio (SDR), architecture of a MIMO transceiver with a amplitude-phase consistency calibration scheme was designed. By looping the output signals into input channels, amplitude-phase consistency of the output or input channels can be measured, thus the channel calibration was performed in the digital signal processing. Based on the designed architecture, a MIMO transceiver prototype was implemented by adopting SDR radio frequency (RF) agile transceivers and a digital signal processing system on chip (SOC), with software running on it also implemented. Experiments show that the MIMO transceiver achieves high amplitude-phase consistency performance after calibration, and has the characteristics of high integration, miniaturization and high flexibility.

A Single Receive Channel DBF Method for Ultra-Wideband Radar

In this paper, we present a space code agility-based single receive channel digital beamforming (DBF) method for ultra-wideband radar. The proposed single channel DBF method outperforms the existing single channel DBF method with a space-time coded array architecture regarding both the angular and the range sidelobe levels by integrating multiple pulses modulated by irrelevant space codes. A frequency-domain equivalent DBF algorithm is also developed. This algorithm is immune to the mismatch between the sampling rate of the recorded signal and the space-time response function of the space-time coded array under ultra-wideband circumstances, in which cases the existing space-time coded array DBF methods will suffer. Moreover, the target motion over multiple pulse repetition intervals (PRIs) is taken into account in the development of the frequency-domain DBF algorithm. Therefore, performance degradation due to target motion is avoided. Numerical simulations verify the effectiveness of the proposed method.

Extrapolation-RELAX Estimator Based on Spectrum Partitioning for DOA Estimation of FMCW Radar

This paper proposes an extrapolation-RELAX estimator based on spectrum partitioning (SP) for the direction of arrival (DOA) estimation of frequency-modulated continuous-wave (FMCW) radar. The FMCW radar employs fast Fourier transform (FFT)-based digital beamforming (DBF) for the DOA estimation owing to its low complexity and easy implementation. However, the DBF algorithm has a disadvantage of low angle resolution. To improve the angle resolution, super-resolution algorithms such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT) are proposed. However, these algorithms require the high signal-to-noise ratio (SNR) to meet the required performance. To overcome this drawback of super-resolution algorithms, the SP-based extrapolation method has been proposed. However, this algorithm still has the problem that the resolution performance degrades owing to the insufficient number of actual antenna arrays. To solve this problem, we propose the SP-based extrapolation-RELAX algorithm for DOA estimation of FMCW radar. Through extrapolation, the proposed structure solves the problem of insufficient number of arrays, resulting in high reliability of SP results. When the extrapolation algorithm is used to generate the input signal of the RELAX algorithm, the RELAX method improves the performance of the DOA estimation. To confirm the effectiveness of the proposed estimation, we compare the Monte Carlo simulation and root-mean-square error results of the proposed and conventional algorithms. To verify the performance of the proposed algorithm in practical conditions, experiments were performed using the FMCW radar module within a chamber and in an indoor environment.

A Unifying Design of Hybrid Beamforming Architectures Employing Phase Shifters or Switches

Hybrid beamforming (BF) architectures employing phase shifters or switches reduce the number of required radio frequency chains and the power consumption of base stations that employ a large number of antennas. Due to the inherent tradeoff between the number of radio frequency chains, the complexity of the employed analog and digital BF algorithms and the achieved spectral and energy efficiency, designing hybrid BF architectures is a complex task. To deal with this complexity, we propose a unifying design that is applicable to architectures employing either phase shifters or switches. In our design, the analog part of the hybrid BF architecture maximizes the capacity of the equivalent channel, while the digital part is updated using the well-known block diagonalization approach. We then employ the proposed joint analog-digital beamforming algorithm on four recently proposed hybrid architectures and compare their performance in terms of spectral and energy efficiency, and find that the proposed analog-digital BF algorithm outperforms previously proposed schemes. We also find that phase shifter-based architectures achieve high spectral efficiency, whereas switching-based architectures can boost energy efficiency with increasing number of base station antennas.

Hybrid Beamforming Design for mmWave Joint Unicast and Multicast Transmission

In this letter, we investigate the beamforming design for millimeter-wave (mmWave) joint unicast and multicast (JUM) systems, in which the common and private signals are transmitted simultaneously. Due to the hardware limitation, the conventional fully digital beamforming methods are infeasible. We propose a suboptimal low-complexity hybrid zero-forcing (ZF) beamforming scheme for the mmWave JUM systems. The analog precoder and combiner are first optimized to maximize the effective channel gains. The ZF algorithm is then used to decide the unicast beamforming directions for cancelling the inter-user interference so that the optimization problem is reduced. With the unicast beamforming directions, the unicast power allocation and multicast beamformer are finally optimized by employing successive convex approximation (SCA) iteratively. The simulation results demonstrate that the sum-rate of the proposed algorithm is slightly inferior to the fully digital beamforming algorithm, but outperforms the orthogonal multiple access scheme.

A Portable 3-D Imaging FMCW MIMO Radar Demonstrator With a $24\times 24$ Antenna Array for Medium-Range Applications

Multiple-input multiple-output (MIMO) radars have been shown to improve target detection for surveillance applications thanks to their proven high-performance properties. In this paper, the design, implementation, and results of a complete 3-D imaging frequency-modulated continuous-wave MIMO radar demonstrator are presented. The radar sensor working frequency range spans between 16 and 17 GHz, and the proposed solution is based on a 24-transmitter and 24-receiver MIMO radar architecture, implemented by time-division multiplexing of the transmit signals. A modular approach based on conventional low-cost printed circuit boards is used for the transmit and receive systems. Using digital beamforming algorithms and radar processing techniques on the received signals, a high-resolution 3-D sensing of the range, azimuth, and elevation can be calculated. With the current antenna configuration, an angular resolution of 2.9° can be reached. Furthermore, by taking advantage of the 1-GHz bandwidth of the system, a range resolution of 0.5 m is achieved. The radio-frequency front-end, digital system and radar signal processing units are here presented. The medium-range surveillance potential and the high-resolution capabilities of the MIMO radar are proved with results in the form of radar images captured from the field measurements.

Adaptive Beamforming for Sparse Array Based on Semi-Definite Programming

An adaptive beamforming (ABF) technique for sparse receiving arrays with gain/phase uncertainties is proposed. The basic idea of the proposed method is using the compressed sensing theory to estimate directions and amplitudes of the received signals with sparse array and then obtain the covariance matrix of the signals through the estimated directions and amplitudes. However, on a discrete grid, the accuracy of directions and amplitudes estimation will degrade because of the basis mismatch and the existence of the gain/phase uncertainties. It will influence the performance of the adaptive digital beamforming. In order to eliminate the influence of the gain/phase uncertainties and the basis mismatch, we propose a semi-definite programming-total least squares (SDP-TLS) method in this paper. First, we convert the problem we want to solve into a TLS framework. Then, we develop an alternating descent algorithm to solve this problem. In the algorithm we proposed, the directions and amplitudes are estimated by semi-definite programming. The covariance matrix of the signals, which is used for ABF, is obtained by the estimated directions and amplitudes. Then, the adaptive digital beamforming algorithm is adopted to form a beam with the obtained covariance matrix.

Comparison of digital beamforming algorithms for 3-D terahertz imaging with sparse multistatic line arrays

Abstract. In this contribution we compare the back-projection algorithm with our recently developed modified range migration algorithm for 3-D terahertz imaging using sparse multistatic line arrays. A 2-D planar sampling scheme is generated using the array's aperture in combination with an orthogonal synthetic aperture obtained through linear movement of the object under test. A stepped frequency continuous wave signal modulation is used for range focusing. Comparisons of the focusing quality show that results using the modified range migration algorithm reflect these of the back-projection algorithm except for some degradation along the array's axis due to the operation in the array's near-field. Nevertheless the highest computational efficiency is obtained from the modified range migration algorithm, which is better than the numerically optimized version of the back-projection algorithm. Measurements have been performed by using an imaging system operating in the W frequency band to verify the theoretical results.

Three-Dimensional Terahertz Imaging With Sparse Multistatic Line Arrays

Many established terahertz imaging modalities are on one side restricted by the tradeoff between resolution and field of view such as in the case of focal plane arrays and on the other side suffer from a limited depth of field such as in the case of quasi-optical terahertz imaging configurations. Furthermore, typical scanning solutions require time-consuming measurement procedures and restrict significant potential industrial deployments of terahertz imaging technology. Imaging with sparse multistatic line arrays in combination with digital beam forming (DBF) techniques enables us to overcome these limitations and offer three-dimensional (3D) terahertz image reconstructions of the object. This contribution addresses the design of such terahertz imaging systems from a general point of view with the focus on the design of sparse line arrays, while considering objects scattering properties. Based on this design concept, the realization of a novel highly sparse 3D terahertz imaging system is discussed. The sparse line array of the system is operating within a frequency range from 75 to 110 GHz and is used in combination with a conveyor in order to generate a synthetic sampling aperture. The system is capable to generate 3D terahertz images with tens of megavoxels at feed motions of up to a few 10 cm/s. Also, a sparse array design in regard to an imaging system operating at 240 GHz with integrated SiGe sensor elements is discussed. In addition, three different DBF algorithms are compared in regard to their computational efficiency.

Digital beamforming in elevation for spaceborne HRWS SAR based on sparse DOA estimation

ABSTRACTA digital beamforming (DBF) algorithm in elevation for spaceborne high-resolution and wide-swath synthetic aperture radar (SAR) systems is presented. According to the imaging geometry, a generalized signal model for SAR systems of multiple elevation channels is studied. The signals received by the channels are regarded as that coming from different direction-of-arrive (DOA) angles. Then, the key procedure for DBF is transformed to DOA estimation. Considering the sparsity of the received signals in spatial domain, the sparse-based DOA estimation algorithm is adopted. After obtaining the DOA angles, the weighted vector for DBF is achieved. Compared to the conventional algorithms, the proposed algorithm has no requirement for the number of snapshots and is more robust to signal-to-noise ratio. Finally, the effectiveness of the proposed algorithm is confirmed in simulations.