Large Antenna Systems Research Articles

High-resolution 2D-DoA sequential algorithm of azimuth and elevation estimation in automotive distributed system of coherent MIMO radars

Objectives. One of the main tasks of radiolocation involves the problem of increasing spatial resolution of the targets in the case of limited aperture of the radar antenna array and short length of time samples (snapshots). Algorithms must be developed to provide high angular resolution and low computational complexity. In order to conform with the existing Advanced Driver Assistance Systems requirements, modern cars are equipped with more than one radar having a common signal processing scheme to improve performance during target detection, positioning, and recognition as compared to a single radar. The present study aims to develop a two-dimensional Direction-of-Arrival algorithm with low computation complexity as part of distributed coherent automotive radar system for cases involving short time samples (snapshots).Methods. A virtual antenna array formation algorithm is formulated according to the two-dimensional Capon method. A proposed modification of two-dimensional Capon algorithm is based on sequentially estimating the directions of arrival for the distributed radar system. The Monte Carlo method is used to compare the effectiveness of the considered algorithms.Results. The 2D-DoA sequential algorithm of azimuth and elevation estimation is proposed. The comparative analysis results for the developed algorithm and classical 2D Capon method based on numerical simulation using Monte Carlo method are presented. The proposed scheme of DoA estimation for coherent signal processing of distributed radars is shown to lead to an improvement of the main considered metrics representing the probability of correctly estimating the number of targets, mean square error, and square error compared to a single radar system. The proposed low-computational algorithm shows the gain in complexity compared to full 2D Capon algorithm.Conclusions. The proposed two-stage algorithm for estimating the directions of arrival of signals in azimuth and elevation planes can be applied to the distributed system of coherent radars with several receiving and transmitting antennas representing multiple input multiple output (MIMO) radars. The algorithm is based on sequentially estimating the directions of arrival, implying estimation in the azimuthal plane at the first stage and estimation in the vertical plane at the second stage. The performance of a coherent radar system with limited antenna array configuration of separate radar is close in characteristics to a high-performance 4D-radar with a large antenna array system.

A decentralized HC-ADMM approach for large antenna arrays

This work addresses the evolving landscape of internet of things (IoT) applications and large antenna array systems, where optimizing spectral efficiency and simplifying design complexities are crucial. Focusing on two key challenges, the study introduces a novel hybrid analog-digital transceiver strategy tailored for frequency-selective channels. By integrating Shannon and Hartley theorems, the approach enhances data transfer rates, thereby optimizing radio frequency (RF) chain utilization in large-scale antennas. To achieve a balance between transceiver performance and hardware complexity, the study employs a decentralized alternating direction method of multipliers (ADMM) framework. The proposed hybrid consensus ADMM algorithm (HC-ADMM) ensures efficient convergence in decentralized optimization scenarios. Comparative analyses with ADMM and existing system transceiver optimization (ESTO) models highlight HC-ADMM's superior performance across key metrics such as spectral efficiency, efficiency per cell, total efficiency, and optimal scheduling of user equipment (UEs). Particularly notable is HC-ADMM's advanced optimization capabilities as the number of transmit antennas increases, positioning it as a promising approach for enhancing overall communication network performance.

An Integrated DRA-Based Large Frequency Ratio Antenna System Consisting of a MM-Wave Array and a MIMO Antenna for 5G Applications

An Integrated DRA-Based Large Frequency Ratio Antenna System Consisting of a MM-Wave Array and a MIMO Antenna for 5G Applications

A Massively Parallel Preconditioned FEM–BEM Method for Accurate Analysis of Complex Electromagnetic Field Problems

In this letter, a parallel preconditioned FEM-BEM method is presented for the solution of challenging electromagnetic (EM) field problems, especially for calculating the radiation characteristics of electrically large radome antenna systems. A preconditioner that combining the absorbing boundary condition (ABC) with the improved additive schwarz method is first proposed for the FEM-BEM system to improve the convergence. Then, an efficient parallel framework based on message passing interface (MPI) and open multi-processing (OpenMP) is designed to achieve massively distributed parallelization of the method. Numerical results demonstrate the benefits of the proposed preconditioner over traditional additive schwarz method, as well as the accuracy and parallel scalability of the implemented method. Finally, a complex EM system consisting of a waveguide slot antenna array and a dielectric radome with more than 127 medium wavelengths is simulated to verify the capability and effectiveness of the presented method.

A Complexity-Efficient Quantum Architecture and Simulation for Eigen Spectrum Estimation of Vandermonde System in a Large Antenna Array

Vandermonde matrix finds a wide range of applications in the digital signal processing domain including antenna array processing. Eigenspectrum estimation remains a challenge in such a system when the matrix dimension increases. This precise problem can be well addressed in a quantum simulation framework based on quantum Hamiltonian simulation (QHS). However, the structural exploitation of the matrix is never considered, which can give benefits in terms of quantum gate complexity. In this work, we exploit the special structural property of a dense Vandermonde matrix by decomposing it into several sparse matrices and then use them for a complexity-efficient eigenvalue spectrum estimation in a quantum framework. In this context, we propose an iterative quantum simulation framework for a Vandermonde-structured Hamiltonian called quantum Vandermonde simulation (QVS). The proposed approach requires fewer quantum gate operations compared to the standard QHS of a dense Vandermonde matrix. In this work, we have considered a large antenna array system, that exploits a delay Vandermonde matrix (DVM) to create a multi-beam beamformer using the proposed low complex quantum architecture. Extensive theoretical and numerical analysis in terms of complexity and other quality parameters have been conducted to show the benefit of the proposed method.

Sub-6-GHz Uplink Massive MIMO System Using Holographic Beamforming Metasurfaces: A Conceptual Development

We propose an uplink massive MIMO system using an array of holographic metasurfaces as a sector antenna operating at 3.5 GHz. The antenna consists of a set of rectangular waveguide-fed metasurfaces combined along the elevation direction into a planar aperture, each with subwavelength-sized metamaterial elements as radiators. The metamaterial radiators are designed such that the waveguide-fed metasurface implements a holographic solution for the guided (or reference) mode, generating a directional beam towards a prescribed direction, thereby forming a multibeam antenna system. We demonstrate that a narrowband uplink massive MIMO system using the metasurfaces can achieve the sum capacity close to that offered by the Rayleigh channel. We show that metasurfaces supporting multiple beams can achieve high spatial resolution in the azimuth directions in sub-6 GHz channels, and thereby form uncorrelated MIMO channels between the base station and users. Also, the proposed metasurface antenna is structurally simple, low-cost, and efficient, and thus is suitable to alleviate RF hardware issues common to massive MIMO systems equipped with a large antenna system.

A Performance Analysis of Massive MIMO System using Antenna Selection Algorithms

A large number of transmitting components makes Massive Multiple-Input Multiple-Output (MIMO) one of the most hopeful solution for the 5G technology. However, a large antenna system boosts the hardware intricacy and cost of the system because of RF transceivers used at the base station for every antenna element. Hence, antenna selection is one of the most effective schemes to select a good subset of antennas with the finest channel circumstances and contribute maximum to the channel capacity. This paper presents Branch and Bound (BAB) algorithm for efficient antenna selection in Massive MIMO technology. The effectiveness of the simulated BAB algorithm is evaluated based on channel capacity and compared with the traditional state of arts such as fast antenna selection algorithm, Exhaustive Search, Fast antenna selection, CBF, CBW, Random antenna selection, etc. Sunflower Optimization-based antenna selection has been shown to provide improved results in terms of channel capacity when compared to the traditional Branch and Bound algorithm. The results indicate that the Sunflower Optimization technique is a promising alternative for antenna selection in Massive MIMO systems, especially in cases where a large number of antennas are present at the transmitter and receiver ends. The proposed solution provides significant improvements over the traditional methods, making it an attractive option for optimizing MIMO performance in future wireless communication systems.

Spherical Fourier-Transform-Based Real-TimeNear-Field Shaping and Focusing in Beyond-5G Networks

For ultra-reliable high-data-rate communication, the beyond fifth generation (B5G) and the sixth generation (6G) wireless networks will heavily rely on beamforming, with mobile users often located in the radiative near-field of large antenna systems. Therefore, a novel approach to shape both the amplitude and phase of the electric near-field of any general antenna array topology is presented. Leveraging on the active element patterns generated by each antenna port, the beam synthesis capabilities of the array are exploited through Fourier analysis and spherical mode expansions. As a proof-of-concept, two different arrays are synthesized from the same active antenna element. These arrays are used to obtain 2D near-field patterns with sharp edges and a 30 dB difference between the fields' magnitudes inside and outside the target regions. Various validation and application examples demonstrate the full control of the radiation in every direction, yielding optimal performance for the users in the focal zones, while significantly improving the management of the power density outside of them. Moreover, the advocated algorithm is very efficient, allowing for a fast, real-time modification and shaping of the array's radiative near-field.

Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks

As a critical enabler for beyond fifth-generation (B5G) technology, millimeter wave (mmWave) beamforming for mmWave has been studied for many years. Multi-input multi-output (MIMO) system, which is the baseline for beamforming operation, rely heavily on multiple antennas to stream data in mmWave wireless communication systems. High-speed mmWave applications face challenges such as blockage and latency overhead. In addition, the efficiency of the mobile systems is severely impacted by the high training overhead required to discover the best beamforming vectors in large antenna array mmWave systems. In order to mitigate the stated challenges, in this paper, we propose a novel deep reinforcement learning (DRL) based coordinated beamforming scheme where multiple base stations serve one mobile station (MS) jointly. The constructed solution then uses a proposed DRL model and predicts the suboptimal beamforming vectors at the base stations (BSs) out of possible beamforming codebook candidates. This solution enables a complete system that facilitates highly mobile mmWave applications with dependable coverage, minimal training overhead, and low latency. Numerical results demonstrate that our proposed algorithm remarkably increases the achievable sum rate capacity for the highly mobile mmWave massive MIMO scenario while ensuring low training and latency overhead.

Channel Estimation for RIS-Aided mmWave Massive MIMO System Using Few-Bit ADCs

Millimeter wave (mmWave) massive multiple-input multiple-output (massive MIMO) is one of the most promising technologies for the fifth generation and beyond wireless communication system. However, a large number of antennas incur high power consumption and hardware costs, and high-frequency communications place a heavy burden on the analog-to-digital converters (ADCs) at the base station (BS). Furthermore, it is too costly to equipping each antenna with a high-precision ADC in a large antenna array system. It is promising to adopt low-resolution ADCs to address this problem. In this letter, we investigate the cascaded channel estimation for a mmWave massive MIMO system aided by a reconfigurable intelligent surface (RIS) with the BS equipped with few-bit ADCs. Due to the low-rank property of the cascaded channel, the estimation of the cascaded channel can be formulated as a low-rank matrix completion problem. We introduce the Bayesian optimal inference framework to tackle with the information loss caused by quantization. To implement the estimator and achieve the matrix completion, we use efficient bilinear generalized approximate message passing (BiG-AMP) algorithm. Extensive simulation results verify that our proposed method can accurately estimate the cascaded channel for the RIS-aided mmWave massive MIMO system with low-resolution ADCs.

Deep Learning of Near Field Beam Focusing in Terahertz Wideband Massive MIMO Systems

Employing large antenna arrays and utilizing large bandwidth have the potential of bringing very high data rates to future wireless communication systems. However, this brings the system into the near-field regime and also makes the conventional transceiver architectures suffer from the wideband effects. To address these problems, in this paper, we propose a low-complexity frequency-aware beamforming solution that is designed for hybrid time-delay and phase-shifter based RF architectures. To reduce the complexity, the joint design problem of the time delays and phase shifts is decomposed into two subproblems, where a signal model inspired online learning framework is proposed to learn the shifts of the quantized analog phase shifters, and a low-complexity geometry-assisted method is leveraged to configure the delay settings of the time-delay units. Simulation results highlight the efficacy of the proposed solution in achieving robust performance across a wide frequency range for large antenna array systems.

Statistical CSI-Based Design for RIS-Assisted Communication Systems

Reconfigurable intelligent surfaces (RISs) have attracted considerable attention over the past years. A RIS can smartly adjust the phase of incident wavefronts and create anomalous reflections towards desired directions. In this letter, we consider a RIS-assisted large antenna system and investigate the ergodic spectral efficiency (SE). By considering a finite dimensional channel, we derive an upper bound on the ergodic SE. Based on this upper bound, we propose an optimal phase shift design by exploiting statistical channel state information. Specifically, we develop a semidefinite relaxation technique and Gaussian randomization procedure for continous phase shift design. Furthermore, an alternating optimization algorithm is applied to the discrete case. Numerical results verify the tightness of the upper bound and the effectiveness of our phase shift design. Considering hardware limitations, we find that a RIS of 2-bit phase adjustable elements achieves the same ergodic SE as the continous phase shift architecture.

Integrated Analysis and Optimization of the Large Airborne Radome-Enclosed Antenna System

In order to realize integrally analysis and optimization of the large airborne radome-enclosed antenna system, a novel optimization strategy is proposed based on an overlapping domain decomposition method by using higher-order MoM and out-of-core solver (HO-OC-DDM), and combining with adaptive mutation particle swarm optimization (AMPSO). The introduction of parallel out-of-core solver and DDM can effectively break the random access memory (RAM) limit. This strategy can decompose difficult-to-solve global optimization problems into multi-domain optimization problems by using domain decomposition method. Finally, take airborne Yagi antenna system as an example, the numerical results show that the design of large airborne radome-enclosed antenna system based on the proposed strategy is convenient and effective.

Energy Efficiency of Multiple-Input, Multiple-Output Architectures: Future 60-GHz Applications

Large antenna systems at millimeter-wave (mmwave) can concentrate the transmit power and the receive region over narrow beams, as well as enable spatial multiplexing. Thanks to these benefits, large antenna systems at mm-wave are the core technologies for future wireless local area network (WLAN) and 5G/ beyond 5G (B5G) cellular standards. Energy efficiency is a crucial design objective for new technologies to reduce operating costs, minimize the environmental impact, and enable battery-powered applications. Power consumption and system performance depend on the design of the beamforming architecture. The objective of this article is to compare the energy efficiency of three different architectures using power consumption measurements of a 60-GHz CMOS transceiver. We compare a full digital architecture, in which each digital chain is connected to a single antenna, against hybrid partially connected (HPC) and hybrid fully connected (HFC) architectures, using fewer digital chains than antennas. The system throughput performance is evaluated considering the hardware nonidealities, including power amplifier saturation, quantization, and phase noise (PN). We show that the number of users spatially multiplexed impacts the hybrid beamforming tradeoff. When few users are multiplexed, the main drain of energy is the digital front end, as it needs to execute operations such as filtering and fast Fourier transform (FFT) on a wide modulation bandwidth of 1.76 GHz. In this case, reducing digital redundancy using a hybrid architecture is beneficial, and a HPC architecture is the most attractive. Scaling the system to a massive multiple-input, multiple-output (mMIMO) scenario instead allows full digital architectures to achieve the highest energy efficiency.

Adaptive Cooperative Jamming for Secure Communication in Energy Harvesting Relay Networks

This letter studies the problem of secure communication for a dual-hop energy harvesting (EH) relay network in the presence of multiple eavesdroppers. An adaptive cooperative jamming (ACJ) scheme is proposed, which can adaptively adjust power allocation factor to maximize the secrecy rate in practical networks where there is no eavesdroppers' channel-state-information (CSI). To evaluate the performance of the proposed scheme, we analyze the ergodic secrecy capacity (ESC) of multi-antenna networks using the ACJ, and derive the closed-form expressions of the ESC for large antenna systems. Simulation results show that the proposed scheme outperforms the benchmark scheme.

High detectability with low impact: Optimizing large PIT tracking systems for cave-dwelling bats.

Passive integrated transponder (PIT) tag technology permits the “resighting” of animals tagged for ecological research without the need for physical re‐trapping. Whilst this is effective if animals pass within centimeters of tag readers, short‐distance detection capabilities have prevented the use of this technology with many species. To address this problem, we optimized a large (15 m long) flexible antenna system to provide a c. 8 m2 vertical detection plane for detecting animals in flight. We installed antennas at two roosting caves, including the primary maternity cave, of the critically endangered southern bent‐winged bat (Miniopterus orianae bassanii) in south‐eastern Australia. Testing of these systems indicated PIT‐tags could be detected up to 105 cm either side of the antenna plane. Over the course of a three‐year study, we subcutaneously PIT‐tagged 2,966 bats and logged over 1.4 million unique detections, with 97% of tagged bats detected at least once. The probability of encountering a tagged bat decreased with increasing environmental “noise” (unwanted signal) perceived by the system. During the study, we mitigated initial high noise levels by earthing both systems, which contributed to an increase in daily detection probability (based on the proportion of individuals known to be alive that were detected each day) from <0.2 (noise level ≥30%) to 0.7–0.8 (noise level 5%–15%). Conditional on a low (5%) noise level, model‐based estimates of daily encounter probability were highest (>0.8) during peak breeding season when both female and male southern bent‐winged bats congregate at the maternity cave. In this paper, we detail the methods employed and make methodological recommendations for future wildlife research using large antennas, including earthing systems as standard protocol and quantifying noise metrics as a covariate influencing the probability of detection in subsequent analyses. Our results demonstrate that large PIT antennas can be used successfully to detect small volant species, extending the scope of PIT technology and enabling a much broader range of wildlife species to be studied using this approach.

Low-Complexity Coordinated Relay Beamforming Design for Multi-Cluster Relay Interference Networks

Consider a multi-user multi-cluster relay interference network where each cluster contains a source–destination pair communicating through their own dedicated multi-antenna relays. We propose a coordinated relay beamforming design for interference management among relaying clusters, aiming to maximize the minimum signal-to-interference-and-noise ratio (SINR) at destinations, subject to per relay power budgets. We propose a structured relay beam matrix solution based on a weighted sum of two types of beam matrices: zero-forcing (ZF) beamforming and maximum ratio combining (MRC) beamforming, of which we obtain the optimal ZF and MRC beam matrices in closed form. Our proposed beamforming structure allows us to transform the original max–min SINR problem into a low-complexity weight optimization problem, which we solve via the semi-definite relaxation approach. Our solution is highly computationally efficient with the complexity not growing with the number of antennas at each relay. Comparing with the direct approach to obtain relay beam matrices for the original problem, our solution provides a very similar performance but with a significantly lower complexity and thus is scalable and suitable for large-antenna systems. The weights in our relay beamforming solution clearly reveal how the relay power splits and shifts between suppressing inter-cluster interference and maximizing signal beamforming gain as the channel strength or cluster distance changes. The simulation shows that our solution significantly outperforms the MRC-only or ZF-only solution under both the perfect channel-state information (CSI) and imperfect CSI over interference channels.

A Novel Two-Timescale Limited Feedback Hybrid Beamforming/Combining Design for Millimeter Wave Systems

Hybrid beamforming offers a low hardware complexity for millimeter wave massive multiple-input multiple-output systems, which is divided among the analog radio frequency precoding and digital baseband precoding. In order to provide channel state information for the base station, channel feedback is essential, especially in frequency division duplexing systems. However, in large antenna systems, the size of channel matrix is huge, and the overhead of feedback complete channel is not acceptable. To reduce the feedback overhead for hybrid beamforming, leveraging the property that the millimeter wave path angles of departure vary more slowly than the path gains, we propose a two-timescale hybrid beamforming/combining algorithm for downlink multi-streams millimeter wave systems. We analyze the performance of the proposed algorithm with perfect channel state information, and the analytical and simulation results show that proposed algorithm can achieve the same performance of full digital singular value decomposition algorithm. We also analyze the rate gap between the proposed algorithm with perfect channel state information and limited feedback. The rate gap caused by quantization of radio frequency and baseband precoder codebooks are decoupled, and the quantization error is analyzed. At last, we provide a closed-form expression of relationship between feedback bits and rate gap.

Max-Min Fair Energy Beamforming for Wireless Powered Communication With Non-Linear Energy Harvesting

This paper considers a max-min rate optimization problem with practical non-linear energy harvesting (NLEH) in which a multi-antenna hybrid access point transfers power to the devices via energy beamforming (BF), followed by the devices sending their data simultaneously by consuming the harvested energy. Using a sigmoid NLEH model with sensitivity, we tackle the joint energy BF and time allocation problem in two steps by solving the NLEH-aware energy BF problem for given time allocation and then solving the convex time allocation problem formulated with the aforementioned energy BF solution. We propose several iterative methods to solve the non-convex energy BF problem with and without approximation of the NLEH function. In addition, we present an asymptotic energy BF problem for a large-antenna system that can be solved at low complexity. The results show that the algorithms developed with a simple NLEH approximation provide almost the same performance with the algorithms developed with the exact NLEH function. Furthermore, the sensitivity region of the NLEH should be considered more carefully than the saturation region in the max-min rate optimization problem.

Low-Resolution PSs Based Hybrid Precoding for Multiuser Communication Systems

Large antenna array systems are favored in next-generation wireless communications, as it can offer multiplexing and array gains that enhance the system sum-rate. However, the large antenna array systems often necessitate the use of high-cost and power-hungry radio frequency (RF) devices. To reduce the hardware complexity and avoid the explicit high-dimensional channel estimation, we propose a joint iterative training based hybrid precoding using low-resolution phase shifters (PSs). Different from the existing works based on the predefined codebook, the iterative training is applied for the hybrid architectures. The iterative training converges to the dominant steering vectors that align with the direction of the largest channel gain, thus it can harvest more array gains than the predefined codebook method. In addition, the performance loss induced by the finite phase quantization is analytically investigated for multiple RF chains. Simulation results show that the developed joint iterative training method having a fast convergence can achieve similar array gain compared with the systems equipped with the continuous PSs. Furthermore, the proposed hybrid precoding utilizing low-resolution PSs can offer a sum-rate comparable to the fixed-rank fully-digital multiple-input multiple-output systems, but with limited hardware cost and energy consumption.