Analog Beam Research Articles

Energy Efficient Multi User Spatial Modulated NOMA for mmWave Communications

In this paper, we propose a downlink multi-user (MU) generalized spatial modulation (GSM) aided non-orthogonal multiple access (MU-GSM-NOMA) transmission technique for mmWave communications. Given a set of users, a user grouping algorithm is presented to partition the users into different clusters, and each of them is served by a dedicated uniform linear array (ULA) of antennas. The ULAs generate analog beams to steer the amplitude and phase-modulated symbols by employing the principles of NOMA. Further, the analog beams carry additional spatial information bits encoded in GSM for a particular cluster user. We propose simple digital combiners for receiver processing based on matched filtering, for which we have characterized the theoretical guarantee of the perfect detection of spatially modulated analog beams. Also, the work proposes an optimization algorithm to allocate power to the users, ensuring stable successive interference cancellation for a specific case of two users in each cluster. Finally, we present the numerical results, which demonstrate the outperformance of our proposed approach over the existing ones in terms of average sum rate and energy efficiency.

Intelligent Analog Beam Selection and Beamspace Channel Tracking in THz Massive MIMO With Lens Antenna Array

Beamspace multiple-input-multiple-output (MIMO) as a green technology can efficiently substitute for the conventional massive MIMO, provided that the beamspace channel is acquired precisely. The prior efforts in this area of study, especially the learning-driven ones, however, indicate remarkable performance losses owing to a lack of generalization. In this paper, we propose a modified non-linear auto-regressive exogenous (NARX) model for tracking and predicting the beamspace channel over the sequences of time. Benefiting from bounded generalization error, fast convergence, limited prediction variance, and negligible performance loss, the proposed scheme achieves up to 15% spectral efficiency (SE) gain over its counterparts. We further improve this performance by means of an ensemble learning technique for simultaneously training multiple NARX modules in parallel, thus leading to a 23% SE gain. Relying on the predicted beamspace channel, we propose a beamspace analog beam selection technique through fine-tuning the architecture of a pre-trained off-the-shelf GoogleNet, which brings up to 21% SE gain over similar baselines. With the aid of an ensemble learning technique, it is further indicated numerically that up to 34% SE improvement can be achieved, as compared to the counterparts.

Programmable Metasurface Hybrid MIMO Beamforming: Channel Estimation, Data Transmission, and System Implementations at 28 GHz

With the development of wireless communication generations, multiple-input and multiple-output (MIMO) architectures have proven to be a solution to the higher required data rates for the increasing number of mobile users. As a matter of fact, better earnings can be gathered from a low-cost system with effective functions, which results in the hybrid beamforming architecture combining digital chains with large-scale antenna arrays. The programmable metasurface (PM) is a promising antenna array concept that realizes reconfigurable beamforming. It is raised to be an attractive antenna array architecture due to its low price and power consumption. However, experimental investigations combining PMs with modern wireless communication systems have not been intensively studied. Its challenges and difficulties of signal processing and system implementation remain uncertainties to be discovered. In this article, a PM hybrid MIMO beamforming system including the aforementioned important topics is presented as follows. First of all, the hybrid beamforming channel estimation algorithm adapted for PM is created by merging analog beam training and digital interleaved orthogonal frequency division multiplexing. Afterward, data transmissions based on the estimated channel state information utilizes variant signal recovery methods to examine the channel estimation accuracy and system feasibilities. To practically analyze the proposed system, the aforementioned aspects are implemented into system-level experiments using three PMs operating at 28 GHz for downlink wireless communication in both single-user and multiuser scenarios. Proper results are delivered, which successfully prove the PM hybrid MIMO beamforming system functionalities.

Machine Learning-Empowered Beam Management for mmWave-NOMA in Multi-UAVs Networks

We consider a mission-driven multiple unmanned aerial vehicles (multi-UAVs) network with millimeter-wave (mmWave) transmissions, where a leader UAV (LUAV) communicates with a large number of follower UAVs (FUAVs) simultaneously via a uniform planar array with only a limited number of radio frequency chains. Since only a few orthogonal beams are available and mission-driven UAV networks are autonomous and delay-sensitive, non-orthogonal multiple access (NOMA) over these beams is considered for agility and efficiency. In particular, aiming to address these challenges of highly dynamic mobility, we propose a machine learning framework to enable agile analog beam management for mmWave-NOMA transmissions. The proposed beam management aims to achieve efficient beam tracking and NOMA-grouping-aware analog beamforming optimization to facilitate mmWave-NOMA transmissions by judiciously utilizing angular domain information (ADI). More specifically, a Gaussian process machine learning-based ADI prediction scheme is proposed to track the angular dynamics of FUAVs, which facilitates fast beam-tracking for mmWave-NOMA transmissions. Moreover, by exploiting the predicted ADI, an unsupervised-learning-based FUAV grouping scheme is proposed to facilitate mmWave-NOMA transmissions with high radio-frequency chain efficiency, while a deep learning-based NOMA-grouping-aware fast transmit beamforming optimization scheme is proposed to improve the coverage of mmWave-NOMA transmissions in highly dynamic multi-UAVs networks. Simulation results validate the performance advantages of our proposed beam management scheme against state-of-the-art schemes.

Traffic-Aware Coordinated Beamforming for mmWave Backhauling of 5G Dense Networks

We study the problem of downlink Coordinated Beamforming for dense fixed-wireless millimeter wave (mmWave) networks under a Centralized/Cloud Radio Access Network (C-RAN) setting. To compensate for the increased mmWave path loss, we consider directional transmissions via antenna array analog beamforming, which leads to a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">discrete</i> set of available beams. We apply the Lyapunov optimization framework to propose a throughput-optimal policy which explicitly accounts for stochastic traffic and channel fluctuations and dynamically performs joint Base Station-to-user association and analog beam selection. Our model makes minimal assumptions and considers realistic mmWave antenna radiation patterns, while it can be easily extended to include additional MAC and PHY layer controls. Since this flexibility comes at the cost of high computational complexity, we also propose two heuristic policies offering reduced complexity. Their performance is compared against a baseline Round-Robin (RR)-based policy, while a theoretical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worst-case</i> performance bound is derived. Extensive simulations show the optimal Lyapunov policy achieving a stable throughput increase of more than 2X <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">per user</i> compared to the RR policy, while the proposed heuristics incur only a 20-30% performance penalty with respect to the optimal Lyapunov policy with 60X-900X computational time savings.

A Stacked Autoencoder and Multilayer Perceptrons for mmWave Beamforming Prediction

The millimeter-wave frequencies planned for 6G systems present challenges for channel modeling. At these frequencies, surface roughness affects wave propagation and causes severe attenuation of millimeter-wave (mmWave) signals. In general, beamforming techniques compensate for this problem. Analog beamforming has some major advantages over its counterpart, digital beamforming, because it uses low-cost phase shifters for massive MIMO systems compared to digital beamforming that provides more accurate and faster results in determining user signals. However, digital beamforming suffers from high complexity and expensive design, making it unsuitable for mmWave systems. The techniques proposed so far for analog beamforming are often challenging in practice. In this work, we have proposed a deep learning model for analog beams training that helps predict the optimal beam vector. Our model uses an available dataset of 18 base stations, over 1 million users, 60 GHz frequency. The training process first applies a stacked autoencoder to extract the features from the training datasets, and then uses a multilayer perceptron (MLP) to train and predict the optimal beams. Then, the results are evaluated by computing the mean squared error between the expected and predicted beams using the test set. The results show high efficiency compared to the benchmark method, which uses only the MLP for the training process.

Novel Design of User Scheduling and Analog Beam Selection in Downlink Millimeter-Wave Communications

In this article, the joint design for user scheduling and analog beam selection in a downlink multiuser millimeter-wave (mmWave) system is studied. Our objective is to maximize the achievable sum rate under the user scheduling constraint, the analog beam selection constraint, and the resource capacity constraint. This problem is nonconvex and NP hard. We first propose a whale optimization algorithm (WOA)-based scheme to obtain a near-global-optimal solution with fast convergence and low complexity. Since the joint optimization user scheduling and beam selection problem is a constrained integer programming problem, the binary version of WOA is applied to deal with integer variables and the penalty method is used to handle the constraints. Besides, a nonlinear convergence factor is introduced to enhance the optimal solutions. For real-time use, we also propose a low-complexity machine-learning (ML)-based scheme. In the ML-based scheme, we decompose the original optimization problem into two subproblems: 1) user classification subproblem and 2) the analog beam selection subproblem. The user classification subproblem is solved based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means algorithm, where the users are clustered according to channel correlation. To solve the analog beam selection subproblem, we reformulate this subproblem as a multiclass classification problem. Considering the imbalance nature of the data set of the subproblem, we train the multiclass classifiers via the biased-SVM algorithm. Finally, the simulation results of the WOA-based scheme and the proposed ML-based scheme against the state-of-the-art schemes have shown the advantages of our proposed schemes.

Enhancing 5G MIMO Core Spectral Efficiency with Higher-Resolution Multi-User MIMO and Multi-Beam Operation

In this article, the evolution of two core multiple-input multiple-output (MIMO) features of the 5G New Radio standardization is described along with their motivations, salient designs, and demonstrated benefits. The first feature enables high spectral efficiency with reduced overhead and complexity in the sub-6 GHz regime by exploiting frequency-domain compression and angle-delay reciprocity inherent in high-resolution precoding codebooks for multi-user MIMO. The second feature, intended for the over-6 GHz regime, facilitates streamlined beam management with greatly reduced signaling overhead and latency as well as improved link reliability in the presence of challenging scenarios such as high mobile speed, large number of analog beams, and unpre-dictable signal blocking. The presented features are the outcome of a five-year work and expected to continue evolving to meet a growing number of challenges from new deployment scenarios.

Multidimensional Vibrations of Cable-Harnessed Beam Structures with Periodic Pattern: Modeling and Experiment

The dynamics of space structures is significantly impacted by the presence of power and electronic cables. Robust physical model is essential to investigate how the host structure dynamics is influenced by cable harnessing. All the developed models only considered the decoupled bending motion. Initial studies by authors point out the importance of coordinate coupling in structures with straight longitudinal cable patterns. In this article, an experimentally validated mathematical model is developed to analyze the fully coupled dynamics of beam with a more complex cable wrapping pattern which is periodic in nature. The effects of cable wrapping pattern and geometry on the system dynamics are investigated through the proposed coupled model. Homogenization-based mathematical modeling is developed to obtain an analogous solid beam that represents the cable wrapped system. The energy expressions obtained for fundamental repeating segment are transferred into the global coordinates consisting of several periodic elements. The coupled partial differential equations (PDE) are obtained for an analogous solid structure. The advantage of the proposed analytical model over the existing models to analyze the vibratory motion of beam with complex cable wrapping pattern has been shown through experimental validation.

First Observations of the New MEXART's Digital System

AbstractThe Mexican Array Radio Telescope (MEXART) is a transit instrument mainly dedicated to performing interplanetary scintillation (IPS) observations with a central operating frequency of 139.65 MHz. The main scientific objective is to perform studies of solar wind properties and space weather effects. MEXART initially operated with an analog beam former (16 × 16 Butler matrix), which produced 16 fixed latitudinal beams. MEXART began operations and reported the first measurements of IPS sources. MEXART's beam forming system had several problems, however. The North‐South beams had poor directivity, with large side lobes, and the instrument did not achieve the expected performance. Therefore, we commissioned the design and construction of a digital back‐end. The digital system solved the problems with the beam forming, increases the bandwidth, and significantly improves the instrument's sensitivity. In this paper, we present the first light of MEXART's digital system. We describe the new technical capabilities of the instrument, and we show some preliminary results: an estimation of the radio telescope's sensitivity (ΔSmin = 2.28 ± 0.23 Jy), the transit of the Galaxy at 140 MHz with the simultaneous tracking of 62 latitudinal beams, and an example of an IPS observation and the single‐station methodology for calculating the solar wind speed. The new technical capabilities of the radio telescope will provide the potential for participating in several scientific studies. These include solar wind properties, space weather forecasting, ionospheric perturbations, and astrophysical aims such as the monitoring of repeating fast radio bursts and pulsars' observations.

Joint Phase-Time Arrays: A Paradigm for Frequency-Dependent Analog Beamforming in 6G

Hybrid beamforming is an attractive solution to build cost-effective and energy-efficient transceivers for millimeter-wave and terahertz systems. However, conventional hybrid beamforming techniques rely on analog components that generate a frequency flat response such as phase-shifters and switches, which limits the flexibility of the achievable beam patterns. As a novel alternative, this paper proposes a new class of hybrid beamforming called Joint phase-time arrays (JPTA), that additionally use true-time delay elements in the analog beamforming to create frequency-dependent analog beams. Using as an example two important frequency-dependent beam behaviors, the numerous benefits of such flexibility are exemplified. Subsequently, the JPTA beamformer design problem to generate any desired beam behavior is formulated and near-optimal algorithms to the problem are proposed. Simulations show that the proposed algorithms can outperform heuristics solutions for JPTA beamformer update. Furthermore, it is shown that JPTA can achieve the two exemplified beam behaviors with one radio-frequency chain, while conventional hybrid beamforming requires the radio-frequency chains to scale with the number of antennas to achieve similar performance. Finally, a wide range of problems to further tap into the potential of JPTA are also listed as future directions.

Multi-User Hybrid Beamforming Design for Physical Layer Secured mmWave LOS Communications

This paper proposes a hybrid beamforming design algorithm for a multi-user physical layer security modulation technique. The hybrid beamforming scheme is used in the base station to generate multi-beams according to the direction angle of the target users. The base station first uses a secure analog beamforming scheme to generate analog beams in multiple desired directions, then uses minimum mean square error (MMSE) to design the digital beamforming matrix to eliminate inter-beam interference. Due to randomly selecting a subset of antennas to transmit signals at the symbol rate, the base station transmits the defined constellation to the target users and projects the randomized constellation in the other angles. In addition, the superposition of signals is affected by a randomly selected antennas subset, resulting in higher sidelobe energy. However, due to the integer optimization target, the optimization problem of antenna subsets is non-trivial. Therefore, this paper proposes a cross-entropy iteration method to choose the optimal antenna combination to reduce the sidelobe energy. The simulation shows that the proposed method in this paper has about 10 dB lower sidelobe energy than the random selection method. Besides, the eavesdropper’s symbol error rate of QPSK is always 0.75, while the multi-target users meet the quality of service requirements.

Machine-Learning Beam Tracking and Weight Optimization for mmWave Multi-UAV Links

Millimeter-wave (mmWave) hybrid analog-digital beamforming is a promising approach to satisfy the low-latency constraint in multiple unmanned aerial vehicles (UAVs) systems, which serve as network infrastructure for flexible deployment. However, in highly dynamic multi-UAV environments, analog beam tracking becomes a critical challenge. The overhead of additional pilot transmission at the price of spectral efficiency is shown necessary to achieve high resilience in operation. An efficient method to deal with high dynamics of UAVs applies machine learning, particularly Q-learning, to analog beam tracking. The proposed Q-learning-based beam tracking scheme uses current/past observations to design rewards from environments to facilitate prediction, which significantly increases the efficiency of data transmission and beam switching. Given the selected analog beams, the goal of digital beamforming is to maximize the SINR. The received pilot signals are utilized to approximate the desired signal and interference power values, which yield the SINR measurements as well as the optimal digital weights. Since the selected analog beams based on the received power do not guarantee the hybrid beamforming achieving the maximization SINR, we therefore reserve additional analog beams as candidates during the beam tracking. When the candidates include the ideal beams, the combination of analog beams with their digital weights achieving the maximum SINR consequently provides the optimal solution to the hybrid beamforming.

Influence of the bolt size on the source of damping in automotive joints

Experimental tests are carried out on automotive bolted joints to study the influence of the bolt size on the source of damping during dynamic loading. Aluminium beams and five different bolt sizes are chosen and used to assemble single-lap joints under strictly controlled experiments. Measurements are taken to estimate the energy loss during forced excitation and to identify the source of damping in jointed structures, and an analogous monolithic solid beam is also used during the experimental investigation to isolate the joint effects and compare the data gathered. The dynamic response of the jointed structure exposed to forced excitation is captured under free-free boundary conditions. The motion of the assembled structure is identified by carrying out a finite element analysis.

Broadband beam steering for misaligned multi-mode OAM communication systems

Orbital angular momentum (OAM) at radio frequency (RF) has attracted more and more attention as a novel approach of multiplexing a set of orthogonal OAM modes on the same frequency channel to achieve high spectral efficiency (SE). However, the precondition for maintaining the orthogonality among different OAM modes is perfect alignment of the transmit and receive uniform circular arrays (UCAs), which is difficult to be satisfied in practical wireless communication scenarios. Therefore, to achieve available multi-mode OAM broadband wireless communication, we first investigate the effect of oblique angles on the transmission performance of the multi-mode OAM broadband system in the non-parallel misalignment case. Then, we compare the UCA-based RF analog and baseband digital transceiver structures and corresponding beam steering schemes. Mathematical analysis and numerical simulations validate that the SE of the misaligned multi-mode OAM broadband system is quite low, while analog and digital beam steering (DBS) both can significantly improve the SE of the system. However, DBS can obtain higher SE than analog beam steering especially when the bandwidth and the number of array elements are large, which validates that the baseband digital transceiver with DBS is more suitable for multi-mode OAM broadband wireless communication systems in practice.

Enabling Practical Large-Scale MIMO in WLANs With Hybrid Beamforming

In theory, the capacity of a wireless network grows linearly with the number of users and antennas equipped at the communication devices, and hence large-scale MU-MIMO can scale up the network throughput. However, three main challenges are impeding the implementation of this promising technology in the state-of-the-art WLANs. Firstly, the current large-scale MU-MIMO technology demands a large number of high-priced RF chains. Secondly, the wireless access points (APs) are overwhelmed by channel state information (CSI) feedback for nulling multi-user and -antenna interference. Thirdly, the lack of scalable user selection scheme limits the capability of APs to serve a large user population. To address these problems, we design BUSH, a large-scale MU-MIMO prototype that performs scalable beam user selection with hybrid beamforming for phased-array antennas in legacy WLANs. We design a low complexity algorithm that assigns each pair of RF chain and analog beam to the users to effectively reduce channel correlation and cross-talk interference without instantaneous CSI feedbacks. As a prerequisite of user selection, BUSH presents a low-overhead probing scheme in multi-carrier WLANs and designs a highly accurate blind Power Azimuth Spectrum (PAS) estimation algorithm using a single RF chain. For reducing the number of RF-chains used, the phased-array antennas use analog beamforming to steer beams toward each selected downlink user, and multiple RF chains use beamforming to further mitigate the interference among users. We implement BUSH on a software-defined radio platform and evaluate its performance in more than 30 indoor scenarios. The experimental results reveal that for throughput, BUSH outperforms the legacy 802.11ac by 2.08×, and an alternative benchmark system by 1.22× on average.

Hybrid precoding algorithm for massive MIMO system with imperfect channel knowledge

Millimeter wave MIMO system offers orders of magnitude high data rate due to the excess bandwidth available then conventional MIMO system. But, at the cost of excessive free space losses that highly influence the transmission at very high millimeter wave frequencies. The resulting small Wavelength at the millimeter wave frequency can overcome the path losses using beam forming gain offered by the massive antennas which is known as pre-coding. In millimeter wave massive MIMO system, hybrid pre-coding is the technique that combines analog pre-coding and digital pre-coding to reduce the requirement of high number of RF chains and thus energy consumption. Hybrid beam forming uses number of RF chains equal to the number of simulations users being served rather than using separate RF chains in the case of digital pre-coding. In designing analog and digital pre-coders, large number of calculations that uses complex mathematical process such as block diagonalization, SVD for channel matrix inversion required for the computation of pre-coder weight vector computation. In this paper, we propose low complexity hybrid pre-coding algorithm using simple linear approximation algorithms for matrix inversion operations. We first used the simple inversion algorithm in the computation of analog beam forming matrix without any SVD or direct inversion of matrices. Then, the same is being used in digital pre-coding matrix. In the proposed algorithm, there is no need for the complicated operations as SVD as in traditional pre coding algorithms.

Millimetre Wave Communication with Spatial Division Multiplexing for 5G systems

The point of this paper is to combination of spatial division multiplexing (SDMA) along with NOMA improves the performance of the system. This paper is done with multibeam forming for performance enhancement, Analog beam forming is done along with the digital beam forming to increase the spectral efficiency and also the user count increases so that we could able to achieve high throughput. In this paper the design problems of mm wave NOMA due to beamforming is focused so that achievable data rate is high. This is due to the number of RF chains. We analyses the demanding joint plan of the intertwined power distribution and client matching for mm Wave-SDMA. Some problems are talked about and a few arrangements are proposed in detail in mm Wave communications.

Large Antenna Array with Hybrid Beamforming System for 5G Outdoor Mobile Broadband Communication Deployments

Millimeter wave (mmWave) communication requires large antenna arrays to increase the capability of cellular networks of the fifth generation with good beam-forming gains and a substantial reduction in path losses for both transmitting and receiving terminals. As large antenna arrays require one radio frequency chain per antenna element, the fully digital beamforming technique results in high cost and high-power consumption, and it is therefore not feasible. However, in analog solutions, adaptive gain control cannot be used as it reduces the likelihood of advanced processing and contributes to poor efficiency. Hybrid schemes are possible exciting solutions that overcome the deficiencies of pure digital or analog beam forming. The following are the three key contributions of the proposed work: a typical link budget specification for target data rate 3.10 Gbps in downlink and 0.6 Gbps in uplink is provided, micro strip patch antenna with a single element is designed to operate at 28 GHz and then converted into a standard linear array and a Kalman-based hybrid analog/digital precoding is used with a downlink rate of 4.64 Gbps/cell and an uplink rate of 1.84 Gbps/cell in multi-user environments. And the influence of both base station (BS) and 5G User equipment (UEs) beam steering capability is also explored. From the simulation result, it is evident that the proposed work offers a substantial increase in spectral efficiency approximately 9.28 bps/Hz at 20 dB with 10 channel paths.

Beam Aggregation-Based mmWave MIMO-NOMA: An AI-Enhanced Approach

Millimeter wave (mmWave) multiple-input-multiple-output (MIMO) with hybrid beamforming enables high data rate broadband services for 5G. To further improve the spectral efficiency and connectivity, non-orthogonal multiple access (NOMA) has been considered to be integrated with mmWave MIMO. Nonetheless, the existing power-domain mmWave MIMO-NOMA with narrow analog beams suffers from beam misalignments which deteriorate the data rates of the misaligned users as well as the system fairness. Thereby, we propose a beam aggregation-based mmWave MIMO-NOMA scheme to loosen the requirement of beam alignment and improve the system fairness. The proposed scheme generates virtual beams with wider beamwidth by aggregating adjacent analog beams. NOMA transmissions are utilized within each aggregated virtual beam. Then, we propose a non-orthogonal multiuser precoding scheme to ensure the fairness within the aggregated beam by maximizing the minimum achievable rates of the grouped users. To address this issue, a max-min problem is proposed and artificial intelligence (AI) technology is exploited to solve this non-trivial problem. The problem is firstly converted to an equivalent penalized minimization problem and then an unsupervised deep neural network (DNN) is trained to map the instantaneous channel coefficients to the precoders in a data-driven fashion. Specifically, we explicitly introduce the transmit power as the DNN input to achieve better generalization in different signal-to-noise ratio regions. Performance evaluations reveal that the proposed scheme can achieve significant gain on the max-min data rate compared with the conventional scheme.