Beamforming Operation Research Articles

Deep Learning based Channel Estimation and Hybrid Beamforming for 5G Massive MIMO Wireless Communications

Hybrid beamforming (BF), which divides beamforming operation into radio frequency (RF) and baseband (BB) domains, will play a critical role in MIMO communication at millimeter-wave(mmWave) frequencies. This paper also introduce offline training and prediction schemes for channel estimation and hybrid beamforming. The aim of this paper is that to increase spectral efficiency over more data streams by leveraging the deep learning based LSTM network. The LSTM network is used to train the numeric values from sequence data and predict on new sequence data. The performance is evaluated under different parameters including number of data streams (1, 2, 3 and 4) with different signal-to-noise ratio (SNR) for different carrier frequencies (28GHz, 38GHz, 60GHz and 73GHz) through computer simulation using MATLAB. The simulation results verified that the proposed method can achieve higher spectral efficiency when the number of data streams increases and the value of SNR-Test increases too.

Volumetric beamforming in real-time using commodity hardware

Ultrasound imaging is widely used in medicine for its safety and affordability. However, it demands large transducer arrays because the image resolution is proportional to the number of elements, N, typically around 128 for 2D imaging. Three-dimensional imaging requires N2 audio channels (≈350 GB/s of data) if the equivalent 2D matrix array is used, which is practically impossible to process. To solve this issue, row-column arrays (RCAs) aggregate rows and columns of elements, reducing data rate and processing demands by a factor of N. A novel dual-stage beamforming algorithm further lowers the beamforming operations by N/2, with negligible impact on the image quality. For N = 128, the processing is 8192 times faster than with a matrix array, and it is hypothesized the 3D RCA beamforming can be done in real-time using a commodity graphics card. The beamforming rate of an NVIDIA RTX 4090 GPU was measured for in-vivo data from a rat kidney, achieving 1394 full volumes per second, which is over 150 times faster than previous implementations. Combining RCAs with the new beamforming algorithm and GPU processing thus enables volumetric beamforming to be done affordably at the bedside in real-time using a standard scanner and PC.

DESIGN OF A SWITCHED BEAM ANTENNA SYSTEM USING A 4 × 4 BUTLER MATRIX FOR WIRELESS COMMUNICATION APPLICATIONS

Demand for quality service within the cellular network, which has many cells within the cluster, has certain requirements to satisfy the customer or subscriber at any cost. Hence, a reliable approach to provide quality service to the subscriber is needed, and in that attempt, a switched beam system is proposed, which is composed of a Butler matrix operating at 2 GHz for 5G wireless communication systems. The Butler matrix operates at 2 GHz, however, and has an overall bandwidth of 1.2 GHz, ranging from 1.8 GHz to 3 GHz. The beamforming operation is completed by incorporating an antenna array; typically for such applications a linear array with half lambda spacing is preferred. Here, the antenna array is made of a dielectric material, hence forming a dielectric resonator array operating at the same frequency as that of the Butler matrix. To complete the switched beam system, a radiofrequency source is chosen, typically USRP 2900 from National Instruments, to provide excitation to the Butler matrix throughout its operation. The simulation is carried out in Ansys HFSS and PCB Antenna Designer from MathWorks. This article gives the simulation as well as measured results of the Butler matrix and dielectric resonator antenna and draws a conclusion of the performance of the system based on simulated and measured results.

Fully-Metallic Additively Manufactured Monolithic Double-Ridged Waveguide Rotman Lens in the K/Ka-Band.

This paper reports on the design and experimental validation of a fully-metallic double-ridged waveguide 10 × 10 Rotman lens additively manufactured as a single part. The wide band operation of this quasi-optical beamformer enables us to cover the uplink and downlink frequencies allocated to satellite communications in the K/Ka-band, from 17.3 GHz to 30 GHz. The feeding port design was adjusted to enable vertical printing, thus minimizing the use of supporting structures. A prototype was manufactured and tested. The reported results indicate losses in the range of 0.5 dB in the lower-frequency band and 0.8 dB in the upper-frequency band, including the waveguide transitions added for test purposes. The measured reflection and coupling coefficients remain below -11.5 dB over the operating band. The standard deviation of the residual phase error across the array ports is below 5° in simulation and below 10° in measurements. Array factors synthesized using the scattering parameters confirm the good stability of the beamforming functionality over the wide frequency band analyzed. This monolithic design is a promising step toward more integrated antenna systems, such as a compact dual-stack configuration for planar array design.

The impact of loss function domain for optimizing ultrasound imaging

The conventional view of ultrasound pulse-echo data—focused sound sent in a direction and bounced back towards the transducer—does not capture the rich information available. REFoCUS beamforming abandons this limitation, combining the ideas of spatially coded excitation and synthetic aperture imaging. Each transmission is an encoding of elements with various weights and time delays, and the multistatic data set (all transmit/receive element pairs) is estimated from the received data. We can, therefore, apply a single focusing operation across different choices of transmit pulse sequencing and ask questions about how to best optimize that sequence.The multistatic data set is a useful mathematical model since it is tied to individual array elements. Several works have used such raw echo data in optimization problems, for instance training neural networks to improve data quality from limited transmissions. However, we have found that the image formation process (e.g., focusing, beamforming) can change the appearance of errors in the data set. Even small errors in the raw data can result in significant image artifacts due to the inherent ill-conditioning of the beamforming operation. We demonstrate improvements in optimization using loss functions defined in the image domain compared to the raw multistatic data.

A Dual Polarization 3-D Beamforming AiP

This paper describes the implementation of an antenna-in-package (AiP) with a dual polarization function, supporting a three-dimensional (3D) beamforming operation. In order to implement 3D beamforming, a Yagi-type end-fire antenna supporting each of the x and y directions and a patch-type broadsided antenna supporting the z-direction were implemented. The broadside antennas have dual polarization functions so that they can be received in any direction. Each antenna was implemented in four array structures to support beamforming operations. The broadside antenna was designed in a 2 × 2 array structure, with a patch-type antenna and two linear dual polarization functions. The single antenna operated with a gain of 6 dBi, an E-plane beam width of ±45 degrees, and an H-plane beam width of ±50 degrees and had an antenna gain of 9~11 dBi as well as a vertical/horizontal forming operation with a radiation angle of ±22 degrees The end-fire antenna unit was designed in a 1 × 4 array structure with a Yagi-type antenna. The single antenna had a gain of 4 dBi, with an antenna gain of 8 dBi in the array structure, and it was improved to 11 dBi by adding a parasitic array director. The final end-fire antenna unit had a radiation angle of ±11 degrees and a beamforming coverage of ±45 degrees The vertical and horizontal design results were secured for reception in any direction, and all the array antennas had a return loss of 10 dB or less in the entire frequency band, from 57 to 66 GHz.

Phase-Encoded Linear Sampling Method Imaging of Conducting Surfaces From Full and Limited Synthetic Apertures

The linear sampling method (LSM) is a qualitative inverse scattering technique that can create good-fidelity imagery at low computational expense. However, it is challenging to use in many practical scenarios due to its need for wide-angle multistatic-multiview data with dense spatial sampling. We present a new LSM formulation for imaging conducting targets from a more limited sensor distribution. The technique mitigates the challenge of limited multistatic diversity by disciplining the solution via a propagation-based phase encoding. Phase encoding is accomplished on receive via a beamforming operation and on transmit via a regularization that enforces a desired phase behavior. We evaluate the method by applying it to multistatic synthetic aperture scenarios where a few sensors travel in formation while collecting data. These scenarios are challenging for conventional LSM, but also potentially desirable due to the limited required hardware resources. We demonstrate with both simulated and experimental data that the proposed technique produces images of fundamentally greater fidelity compared to conventional LSM processing. We demonstrate significantly improved performance both when the aperture completely encircles the target and when the aperture is limited in aspect. The latter result is particularly significant, as limited apertures present significant challenges to LSM imaging.

Learning-Aided Beam Prediction in mmWave MU-MIMO Systems for High-Speed Railway

The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway (HSR) becomes extremely challenging, since large overhead cost and significant time delay are introduced for fast time-varying channel estimation. To tackle this challenge, we propose a learning-aided beam prediction scheme for HSR networks, which predicts the beam directions and the channel amplitudes within a period of future time with fine time granularity, using a group of observations. Concretely, we transform the problem of high-dimensional beam prediction into a two-stage task, i.e., a low-dimensional parameter estimation and a cascaded hybrid beamforming operation. In the first stage, the location and speed of a certain terminal are estimated by maximum likelihood criterion, and a data-driven data fusion module is designed to improve the final estimation accuracy and robustness. Then, the probable future beam directions and channel amplitudes are predicted, based on the HSR scenario priors including deterministic trajectory, motion model, and channel model. Furthermore, we incorporate a learnable non-linear mapping module into the overall beam prediction to allow non-linear tracks. Both of the proposed learnable modules are model-based and have a good interpretability. Compared to the existing beam management scheme, the proposed beam prediction has (near) zero overhead cost and time delay. Simulation results verify the effectiveness of the proposed scheme.

Reconfigurable Antenna for UAV-Assisted Wide Coverage Air-to-Ground Communications

A wide-coverage unmanned aerial vehicle (UAV) antenna system for air-to-ground communications is proposed in this paper. The proposed antenna system has reconfigurable beam patterns based on five sets of dual-polarization and high gain antennas. Each antenna set is configured by a dual-polarization crossed inverted-V radiator and switched feed network to drive switched beamforming operations. Depending on the switch modes of the proposed antenna, main beam direction and circular polarization types can be independently selected. Further, each antenna set is faced toward the ground level at different angles from the center of UAV to provide wide beam coverage. To verify the antenna performance, each antenna set is designed at the center frequency of 5.7 GHz and total five sets are integrated with a switched feed network and a microcontroller. To implement the wide angle switched beamforming with 2-D angle-shifted main beams, a 3-D printed conformal fixture is used to connect both the antenna module and a drone body. The maximum measured bandwidth was more than 22% and the measured peak gain with the de-embedded switch feed network loss was about 10.2 dBic.

Study of MIMO integration methods with fifth generation technologies

It is expected that with the general deployment of cellular 4G systems the new technologies would be required to satisfy the heightening demand for higher data transfer speeds and lower delays while retaining low deployment and maintenance costs. Multiple array technologies such as MIMO will play a key role in the forthcoming 5G and 6G systems. This paper will present several MIMO architectures and beamforming solutions for cm- and mm-wave 5G systems that work in the 20 (3-30 GHz) and 100 (30-300 GHz) spectrums. These spectrums provide much higher potential for small cell deployment with the use of large-scale antenna arrays due to the higher data capacity than that of the traditional cellular network systems. The mm-wave 5G ELA systems that will gradually replace LTE are going to have higher capacity with the use of Frequency Division Duplexing (FDD) because it enables the usage of uplink/downlink reciprocity in controlling the beamforming operation. It is, however, must be noted that MIMO technology has a key problem that consists of obtaining the necessary information needed to control the transmit and receive algorithms, the solution of which will be discussed in this paper. The array calibration errors that can cause severe performance losses are also going to be considered. The paper therefore will focus on the performance and implementation issues of different multi-user-MIMO (MU-MIMO) solutions for transmission in the cm- and mm-wave bands as well as key problems that follow the transmit MIMO processing for 5G systems. Performance evaluation of RF-based architecture and the traditional baseband transmission methodology for downlink MU-MIMO implementation as well as several transmission schemes to highlight the issues with MU-MIMO for 5G and the solutions to them will be provided.

MIMO-Aided Nonlinear Hybrid Transceiver Design for Multiuser Mmwave Systems Relying on Tomlinson-Harashima Precoding

Hybrid analog-digital (A/D) transceivers designed for millimeter wave (mmWave) systems have received substantial research attention, as a benefit of their lower cost and modest energy consumption compared to their fully-digital counterparts. We further improve their performance by conceiving a Tomlinson-Harashima precoding (THP) based nonlinear joint design for the downlink of multiuser multiple-input multiple-output (MIMO) mmWave systems. Our optimization criterion is that of minimizing the mean square error (MSE) of the system under channel uncertainties subject both to realistic transmit power constraint and to the unit modulus constraint imposed on the elements of the analog beamforming (BF) matrices governing the BF operation in the radio frequency domain. We transform this optimization problem into a more tractable form and develop an efficient block coordinate descent (BCD) based algorithm for solving it. Then, a novel two-timescale nonlinear joint hybrid transceiver design algorithm is developed, which can be viewed as an extension of the BCD-based joint design algorithm for reducing both the channel state information (CSI) signalling overhead and the effects of outdated CSI. Moreover, we determine the near-optimal cancellation order for the THP structure based on the lower bound of the MSE. The proposed algorithms can be guaranteed to converge to a Karush-Kuhn-Tucker (KKT) solution of the original problem. The simulation results demonstrate that our proposed nonlinear joint hybrid transceiver design algorithms significantly outperform the existing linear hybrid transceiver algorithms and approach the performance of the fully-digital transceiver, despite its lower cost and power dissipation.

An Adaptive Bidirectional Multibeam High-Altitude Platforms Aeronautical Telecommunication Network Using Dual Concentric Conical Arrays

An efficient aeronautical telecommunication network (ATN) is proposed in this article based on adaptive multibeam antenna arrays and high-altitude platforms (HAPs). First, the network structure is demonstrated, and its geometry is analyzed based on a geocentric coordinate system that converts the global positioning system (GPS) and altitude data of aircrafts and HAPs into direction-of-arrival (DOA) information which is required for subsequent beamforming operation. The antenna array is then formed by designing a low-profile dual concentric conical array (DCCA) with uniform elements distribution and is used at the aircraft and HAP to achieve bidirectional beamforming. The antenna array elements are fed by an adaptive-exponent sine profile function to reduce the sidelobe levels while the low-profile dual conical array structure reduces the secondary major lobe and backlobe levels. The proposed beamforming technique is also capable of providing multibeam towards several aircrafts at the same time for effective resource sharing and management. The radiation performance of the array is demonstrated, analyzed, and compared with the concentric circular array where it is found that the secondary major lobe has been reduced by 13 dB with sidelobe level down to -40 dB relative to the mainlobe level. In addition, the bit energy-to-noise power spectral density and probability of bit error of the proposed aeronautical network has been investigated at different operating frequencies including 3.5 GHz and millimeter wave (mm-wave) frequencies of 28 and 39 GHz. The simulation results have shown that a channel capacity of about 3 Gbps can be achieved for BPSK and QPSK signals using bidirectional beamforming for a channel bandwidth of 400 MHz at 28 and 39 GHz mm-wave frequencies while it is about 773 Mbps at 3.5 GHz for 100 MHz bandwidth that can be provided over a line-of-sight distance of 896 km between an aircraft and HAP.

Near-Field Superdirectivity: An Analytical Perspective

The gain achieved by a superdirective beamformer operating in a diffuse noise-field is significantly higher than the gain attainable with conventional delay-and-sum weights. A classical result states that for a compact linear array consisting of $N$ sensors which receives a plane-wave signal from the end-fire direction, the optimal superdirective gain approaches $N^2$ . It has been noted that in the near-field regime higher gains can be attained. The gain can increase, in theory, without bound for increasing wavelength or decreasing source-receiver distance. We aim to address the phenomenon of near-field superdirectivity in a comprehensive manner. We derive the optimal performance for the limiting case of an infinitesimal-aperture array receiving a spherical-wave signal. This is done with the aid of a sequence of linear transformations. The resulting gain expression is a polynomial, which depends on the number of sensors employed, the wavelength, and the source-receiver distance. The resulting gain curves are optimal and outperform weights corresponding to other superdirectivity methods. The practical case of a finite-aperture array is discussed. We present conditions for which the gain of such an array would approach that predicted by the theory of the infinitesimal case. The white noise gain (WNG) metric of robustness is shown to increase in the near-field regime.

Wideband frequency-invariant beamforming with dynamic range ratio constraints

This paper focuses on the optimization design of weighting coefficients of Frost-structural beamformer to obtain a frequency-invariant (FI) beampattern. To ensure the normal operation of beamformer in transmitting links with limited dynamic range, the design is modeled by minimizing the weighted sum of the beampattern matching error and spatial response variation under the constraints of dynamic range ratio (DRR) and linear phase. Next, we propose a novel iterative algorithm to solve the resulting high-dimensional non-convex optimization problems via alternately handling a tractable problem and a multi-constrained convex subproblem. To speed up, we reformulate this multi-constrained subproblem into a new alternating direction method of multipliers form with a series of low-dimensional and less-constrained subproblems, and deduce the closed-form solutions to each subproblem. Numerical results exhibit the good convergence of proposed algorithm and the excellent FI of obtained beampattern under the DRR constraints.

Deep Learning for SVD and Hybrid Beamforming

Hybrid beamforming (BF), which divides BF operation into radio frequency (RF) and baseband (BB) domains, will play a critical role in MIMO communication at millimeter-wave (mmW) frequencies. In principle, we can obtain unconstrained (optimum) beamformers of a transceiver, which approach the maximum achievable data rates, through its singular value decomposition (SVD). Due to the use of finite-precision phase shifters, combined with power constraints, additional challenges are imposed on the problem of designing hybrid beamformers. Motivated by the recent success of machine learning (ML) techniques, particularly in areas such as computer vision and speech recognition, we explore if ML techniques can be effectively used for SVD and hybrid BF. To this end, we first present a data-driven approach to compute the SVD. We propose three deep neural network (DNN) architectures to approximate the SVD, with varying levels of complexity. The methodology for training these DNN architectures is inspired by the fundamental property of SVD, i.e., it can be used to obtain low-rank approximations. We next explicitly take the constraints of hybrid BF into account (such as quantized phase shifters, power constraints), and propose a novel DNN based approach for the design of hybrid BF systems. To validate the DNN based approach, we present simulation results for both approximating the SVD as well as for hybrid BF. Our results show that DNNs can be an attractive and efficient solution for estimating SVD in a data-driven manner. For the simulations of hybrid BF, we first consider the geometric channel model. We show that the DNN based hybrid BF improves rates by up to 50 - 70% compared to conventional hybrid BF algorithms and achieves 10 - 30% gain in rates compared with the state-of-art ML-aided hybrid BF algorithms. We also discuss the impact of the choice of hyperparameters, such as the number of hidden layers, mini-batch size, and training iterations on the accuracy of DNNs. Furthermore, we provide time complexity and memory requirement analyses for the proposed approach and state-of-the-art approaches.

Effects of aberration on super-resolution ultrasound imaging using microbubbles

Visualizating vasculature beyond the diffraction limit can be achieved using ultrasound super-resolution (USR). Typically, ultrasound (US) scanners model the target medium as homogeneous, assuming a constant speed-of-sound for time-of-flight based calculations. However, variations in US propagation velocity caused by varying tissue layers affect beamforming operations. This aberration is likely to have considerable effect on USR accuracy when imaging at depth. Here we investigate the effect of aberration on USR localization accuracy. Wave propagation through different tissue media was modelled from a linear transducer using k-Wave, and the resulting pulse-echo data from a point scatterer located at 45mm depth was beamformed. Media included a control homogeneous propagation medium, and aberrating media typical of liver imaging. Aberration effects were estimated using RMS arrival-time fluctuations (ATF) and energy-level fluctuations (ELF) at the scatterer location. Signals were extracted and localized with existing localization techniques. Results indicated USR localisation accuracy decreased with increasing aberration. Axial localisation errors reached 836.6 mm for an ATF 30.9 ns. Furthermore, errors increased with decreasing frequency from 4 to 3 MHz. The scale of these errors relative to micro-vascular structures of interest suggests that aberration will have considerable impact on USR performance and requires attention to ensure its success.

Silicon-Based Optical Phased Array Using Electro-Optic $p$ -$i$ -$n$ Phase Shifters

We present a $1\times 16$ silicon optical phased array using electro-optic phase shifters to attain high-speed operation with low power consumption. The phase shifters are constructed using a $p$ - $i$ - $n$ junction structure with the $p$ - and $n$ -doped regions formed on both sides of the $i$ -region. The $i$ -region width of the phase shifter is optimized considering the power consumption for phase-tuning and the propagation loss in the phase shifter. The fabricated $p$ - $i$ - $n$ phase shifter exhibits a fast operating speed of 20 MHz and a low phase-tuning power of 1.7 mW/ $\pi $ . From a 1-D optical phased array integrated with 2- $\mu \text{m}$ -pitch grating radiators, a wide beam-steering range of 45° is attained along the transversal direction at a $1.55~\mu \text{m}$ wavelength. Average power consumption for the beam-forming operation of the $1\times 16$ OPA is measured to be 39.6 mW and the average transition time to steer the beam is 24 ns.

Repeat radar jammer suppression for a colocated MIMO radar

The digital radio frequency memory repeat jammer, which replicates transmit signals and transmits them back to a radar receiver to mimic real targets, is a notably efficient electronic countermeasure device. It has been pointed out that the colocated multiple-input multiple-output (MIMO) radar has an additional advantage in combatting with repeat jammers whose amplifiers operate in the saturation mode and thus can only transmit constant-modulus waveforms, because the amplitude modulation of angular waveforms is difficult to simulate for them. Two waveform design criteria are presented, and both suppress a mutual correlation between angular waveforms and corresponding constant-modulus jamming signals. The two approaches differ on whether the receive beamforming operation is considered. Numerical results validate that the two approaches can both efficiently suppress the mutual correlation and that the second approach outperforms the first approach in terms of the peak sidelobe level.

Online Learning-Based Downlink Transmission Coordination in Ultra-Dense Millimeter Wave Heterogeneous Networks

In heterogeneous ultra-dense networks with millimeter wave macro cells and small cells, base stations (BSs) and mobile user equipments (UEs) perform beamforming operations to establish highly directional links. In spite of the spatial diversity achieved through directional links, as a number of BSs are densely deployed, inter-cell interference caused by concurrent directional transmissions of adjacent BSs becomes severe, resulting in downlink performance degradation in the network. However, it is very difficult to manage inter-cell interference because of the nature of the time-varying wireless fading environment, the dynamic changes in beam propagation directivity, and unpredictable UEs’ locations. In this paper, we propose an online learning-based transmission coordination algorithm based on the framework of multi-armed bandits to learn the unknown characteristics of inter-BS interference and exploit learned data to derive an optimal policy for maximizing the number of successful downlink transmissions. Through the numerical simulations, we verify the effectiveness of the proposed online learning-based inter-BS interference management scheme.

Multi-User Hybrid MIMO at 60 GHz Using 16-Antenna Transmitters

Given the high throughput requirement for the next generation wireless communication systems, merging millimeter wave technologies and multi-user MIMO seems a very promising strategy to achieve the required 20 Gbps. Although a full digital architecture provides the best performance and flexibility, its implementation at millimeter-wave frequencies today seems unrealistic due to the prohibitive costs and high power consumption. Hybrid analog-digital architectures, efficiently sharing beamforming operations between analog and digital domains, appear as a feasible way to implement multi-user MIMO systems at millimeter wave frequencies. While hybrid architectures have been studied intensively, an effective and flexible demonstration proving the feasibility is still missing. In this paper, we introduce the design of a millimeter wave multi-user MIMO system with hybrid analog and digital processing using 60 GHz radios equipped with phased arrays. A base station, employing two 16-antennas transmitters, serves simultaneously two user equipment devices, each with 4 antenna elements, effectively realizing spatial multiplexing. We propose a complete system design comprising the description of millimeter radio transceivers, the multi-user hybrid MIMO algorithms including a strategy for channel estimation and frequency selective precoding along with the transmission protocol. We show that the spatial multiplexing is achieved in several scenarios, most importantly in a strong interference-limited scenario.