Large Antenna Arrays Research Articles

Single-Antenna Device-to-Device Localization in Smart Environments With Backscatter

A long-standing vision of indoor localization is to eliminate infrastructure and deployment costs. Recent innovations make it possible to enable device-to-device (D2D) localization while requiring multiple antennas for the systems. We ask the following question: can we localize the more generally used single-antenna devices (e.g., IoT) using another single-antenna device (e.g., smartphone or smartwatch) in a smart environment where low-cost backscatter tags are widely deployed on walls or smart objects? In this article, we present TagLoc, a lightweight system that enables D2D localization without relying on large antenna arrays. Our observation is that the reflected signals from the ambient smart environment can be exploited to eliminate the requirement of bulky antenna arrays that are unachievable for the simple-designed IoT devices. Specifically, TagLoc creates multiple direction signatures using backscatter arrays in smart environments. Then, the receiver can accurately estimate the direction signatures from the transmitter to the arrays and then localize the target by cooperating all tag arrays. We prototype TagLoc using two single-antenna Intel NUCs with off-the-shelf Intel 5300 WiFi cards and customized backscatter tags. The results show TagLoc can achieve robust performance in a real indoor environment with a median localization error of 0.82 m.

Method for Calculation of the Interaction of Elements in a Large Convex Quasi-Periodic Phased Antenna Array

Method for Calculation of the Interaction of Elements in a Large Convex Quasi-Periodic Phased Antenna Array

Spatial-Spectral Terahertz Networks

This paper focuses on the spatial-spectral terahertz (THz) networks, where transmitters equipped with leaky-wave antennas send information to their receivers at the THz frequency bands. As a directional and nearly planar antenna, the leaky-wave antenna allows for information transmissions with narrow beams and high antenna gains. The conventional large antenna arrays are confronted with challenging issues such as scaling limits and path discovery in the THz frequencies. Therefore, this work exploits the potential of leaky-wave antennas in the dense THz networks, to establish low-complexity THz links. By addressing the propagation angle-frequency coupling effects, the transmission rate is analyzed. The results show that the leaky-wave antenna is efficient for achieving the high-speed transmission rate. The co-channel interference management is unnecessary when the THz transmitters with large subchannel bandwidths are not extremely dense. A simple subchannel allocation solution is proposed, which enhances the transmission rate compared with the same number of subchannels with the equal allocation of the frequency band. After subchannel allocation, a low-complexity power allocation method is proposed to improve the energy efficiency.

A high‐precision and fast off‐line beamforming pattern estimation technique for active phased array antenna application

A detailed modeling method including all states of each active devices is proposed in this work, aiming to achieve high-precision and fast off-line estimation of beamforming pattern. Experimental verification of hundreds of radiating patterns shows that the beam steering angle accuracy is able to obtain 0.2% (Tx mode) and 0.19% (Rx mode) beam width in average, respectively; and the beam width accuracy is 0.2% (Tx mode) and 0.39% (Rx mode) in average, respectively. Also, SLL accuracy can reach 0.01 dB (Tx mode) and 0.1 dB (Rx mode), respectively. By adopting this technology, the optimal beam controlling codes of large active phased array antenna (APAA) could be quickly derived. Furthermore, measurement and online compensation works of enormous quantities of beams could be greatly simplified.

Deep Neural Network-Based Precoder for Fairness Aware Secure NOMA Scheme

This work proposes an artificial noise (AN)-aided secure multiple-input single-output non-orthogonal multiple access (NOMA) scheme. In the design of the proposed scheme, we consider fairness that all the users have higher secrecy rates as compared to those given by a competing orthogonal multiple access (OMA) scheme. Despite its importance, the fairness aware design has remained rarely touched since it is mathematically intractable. This work shows that this problem can be efficiently solved by utilizing a deep neural network as the precoder for the information and AN signals even without resorting to some assumptions such as a large antenna array and/or a high signal-to-noise ratio. We will also propose an adaptive mode that switches the access protocol from the OMA scheme to the NOMA scheme only when the fairness is met. Performance of the proposed secure NOMA scheme will be extensively evaluated and compared with existing NOMA and OMA schemes. The comparisons clearly show that the sum secrecy rate can be significantly improved while guaranteeing the fairness, which however cannot be achieved with the existing NOMA scheme.

Hybrid Beamforming for Millimeter Wave MIMO Integrated Sensing and Communications

In this letter, we consider hybrid beamforming for millimeter wave (mmWave) MIMO integrated sensing and communications (ISAC). We design the transmit beam of a dual-functional radar-communication (DFRC) base station (BS), aiming at approaching the objective radar beam pattern, subject to the constraints of the signal to interference-plus-noise ratio (SINR) of communication users and total transmission power of the DFRC BS. To provide additional degree of freedom for the beam design problem, we introduce a phase vector to the objective beam pattern and propose an alternating minimization method to iteratively optimize the transmit beam and the phase vector, which involves second-order cone programming and constrained least squared estimation, respectively. Then based on the designed transmit beam, we determine the analog beamformer and digital beamformer subject to the constant envelop constraint of phase shifter network in mmWave MIMO, still using the alternating minimization method. Simulation results show that under the same SINR constraint of communication users, larger antenna array can achieve better radar beam quality.

High-Resolution Phased-Subarray MIMO Radar With Grating Lobe Cancellation Technique

A collocated phased-subarray multiple-input and multiple-output (MIMO) radar system that reduces the number of MIMO channels to implement a large antenna array is proposed. Both the transmitter (TX) and receiver (RX) arrays are divided into phased subarrays, which consist of several RF beamforming antenna elements. Each phased subarray is exploited as an MIMO channel. To estimate the angular information of targets, a hybrid beamforming technique that incorporates RF beamforming of the phased subarrays and digital beamforming of the MIMO virtual array is used. Antenna configurations of the phased-subarray MIMO radar and the corresponding virtual array are also presented. A space between the TX phased subarrays larger than one TX subarray aperture is introduced to facilitate the larger antenna aperture. Because this involves unwanted overall grating lobes, a grating lobe cancellation method is proposed using sum and difference RF subarray beams. This provides a higher angular resolution with the same number of antenna elements. The proposed phased-subarray MIMO radar concept with the corresponding grating lobe cancellation method is demonstrated by implementing a 60-GHz 16-element phased-subarray 2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\times }$ </tex-math></inline-formula> 2 MIMO frequency-modulated continuous-wave (FMCW) radar system.

Deep Learning-Aided Off-Grid Channel Estimation for Millimeter Wave Cellular Systems

It is challenging to acquire accurate channel knowledge for sufficient beamforming gain because of the large number of antennas. In this paper, a deep learning aided channel estimation algorithm is proposed for mmWave cellular systems, in which both the base station (BS) and mobile station (MS) gain directional beamforming by employing large antenna arrays. We consider the off-grid (OG) mmWave channel estimation and propose a deep network architecture for solving this problem, which is different from most existing works concerning the compressed sensing (CS)-based channel estimation. Firstly, the off-grid channel model is used to relieve the basis mismatch issue by exploiting first-order Taylor-series approximation of the array manifold taken on a fixed grid of both BS and MS. Secondly, a new formulation of this off-grid channel estimation problem is solved by using a low computational complexity alternating direction method of multipliers (ADMM)-based algorithm, dubbed ADMM-OG algorithm. Thirdly, the idea of algorithm unrolling guides us to design a deep network architecture ADMM-OGChannelNet corresponding to the ADMM-OG algorithm for the mmWave channel estimation. Based on the interpretability of this deep network architecture, the optimal parameters of this network can be learned without tuning in a hand-crafted way. The Cramér-Rao bound (CRB) of the off-grid channel model is derived for performance comparison as well. Finally, from the simulation results, we can verify that the ADMM-OGChannelNet has better estimation accuracy and relatively low computational complexity compared with the state-of-the-art algorithms.

Performance analysis of a millimeter wave MIMO channel estimation method in an embedded multi-core processor

The emerging Multi-Processor System-on-Chip (MPSoC) technology, which combines heterogeneous computing with the high performance of field programmable gate arrays (FPGA), is a promising platform for a large number of applications, including wireless communications and vehicular technology. In this specific application context, when multiple-input multiple-output (MIMO) scenarios are considered, the system usually has to manage a large number of communication links among sensors and antennas involving different vehicles and users. Millimeter wave (mmWave) communications are one of the key technology enablers toward achieving high data rates in beyond 5G systems (B5G). Communication at these frequency bands usually involves the use of large antenna arrays, often requiring high computational resources. One of the candidate platforms able to manage a huge number of communications is the Xilinx Zynq UltraScale+ EG Heterogeneous MPSoC, which is composed of a dual-core Cortex-R5, a quad-core ARM Cortex-A53, a graphics processing unit (GPU) and a high-end FPGA. This work analyzes the computational performance that requires a recent mmWave MIMO channel estimation algorithm in a platform of this kind. As a first approach, we will focus our work on the performance that can be achieved via the quad-core ARM Cortex-A53. To this end, we will use the libraries for numerical algebra (BLAS and LAPACK). The results show that our reference implementation is able to manage a large MIMO communication system with 256 antennas without exhausting platform resources.

HF Skywave Massive MIMO Communication

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. Considering the large antenna array aperture and increased signal bandwidth, the effect of the propagation delay across the large-scale antenna array cannot be ignored, and thus the steering vectors vary across different subcarriers. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error (MMSE) based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. In order to reduce the design complexities of the MMSE receiver and precoder, we derive a polynomial expansion based design using a deterministic equivalent. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

The Open Challenge of Integrating Satellites into (Beyond-) 5G Cellular Networks

The 3rd Generation Partnership Program (3GPP) standardization has recognized the relevance of integrating a non-terrestrial (i.e., sat-ellite-based) segment into the ubiquitous 5G (terrestrial) cellular network. Butsuch integration has actually proven considerably slower than expected. This article starts with a review of the recent history of satellite mobile communications and surveys the current status of satellite Networks and the related technologies, including high throughput satellites with large antenna arrays and mega constellations. Such discussion is used as the basis for the identification of technological gaps that are making the cited integration an open challenge. Since terrestrial networks are already targeting 6G, this article tries to project the current 5G gaps toward the beyond 5G horizon of 2030.

Editorial Issue on “Information Theoretic Foundations of Future Communication Systems”

Information theory, starting with Shannon’s groundbreaking work, has fundamentally shaped the way communication systems are designed and operated. Information theoretic principles form the underpinnings of modern communication networks. This issue explores how new advances in information theory can impact future communication systems. Several papers address issues at the heart of next generation wireless and wired networks: Multiple access, including access by a massive number of devices, multi-hop, large antenna arrays, communication security, and timeliness of information. Others consider new applications such as joint communication and sensing, communication for learning and inference, wireless imaging, and new storage mediums such as DNA, thereby providing the information theoretic foundations of modalities beyond human-to-human communications.

Phase-Only Synthesis for Large Planar Arrays via Zernike Polynomials and Invasive Weed Optimization

A synthesis method for the design of large planar array antennas with phase-only control is here presented. The synthesis is based on the use of Zernike polynomials, as global basis function for the phase, to reduce the number of optimization variables with respect to the number of elements of the array. Invasive weed optimization (IWO) is applied to polynomial coefficients&#x2019; optimization to circumvent nonlinearity and local trapping issues typical of phase-only problems. The periodicity of the array factor is exploited to reduce the optimization to a Voronoi cell of the grating-lobes&#x2019; lattice and nonuniform meshing is introduced to best adapt the control stations to the beam shape requirements. The technique is applied to the design of shaped beams for continental coverage from geostationary satellites.

Improved Gauss–Seidel detector for large‐scale MIMO systems

Large-scale multiple-input multiple-output (LS-MIMO) is one of the promising technologies beyond the 5G cellular system in which large antenna arrays at the base station (BS) improve the system capacity and energy-efficiency. However, the large number of antennas at the BS makes it challenging to design low-complexity high-performance data detectors. Thus, a number of iterative detection methods, such as Gauss–Seidel and conjugate gradient, are introduced to achieve complexity-performance tradeoff. However, their performance deteriorates for the systems with small BS-to-user antenna ratio or for the channels that exhibit correlation. This paper proposes a new efficient iterative detection algorithm based on the improved Gauss–Seidel iteration to address this problem. The proposed method performs one conjugate gradient iteration that enables better performance with less number of iterations. A new hybrid iteration is introduced and a low-complexity initial estimation is utilised to enhance detection accuracy while reducing the complexity further. In addition, a novel preconditioning technique is proposed to maintain the benefits of the proposed detector in correlated MIMO channels. It is mathematically demonstrate that the proposed detector achieves low approximated error. Theoretical analysis and numerical results show that the proposed algorithm provides a faster convergence rate compared to conventional methods.

Tile-based massively scalable MIMO and phased arrays for 5G/B5G-enabled smart skins and reconfigurable intelligent surfaces

This work presents a novel tile based approach to constructing, in a modular fashion, massively scalable MIMO and phased arrays for 5G/B5G millimeter-wave smart skins and large-area reconfigurable intelligent surfaces for Smart Cities and IoT applications. A proof-of-concept 29 GHz 32 elements phased array utilizing 2 times 2 “8-element subarray” tiles was fabricated and measured and demonstrates +/- 30beamsteering capability. The unique benefits of the proposed tile approach utilizes the fact that tiles of identical sizes can be manufactured in large quantities rather than have arrays of multiple sizes serve various user capacity coverage areas. It has to be stressed that the proof-of-concept flexible 2 times 2 tile array features no performance degradation when it is wrapped around a 3.5 cm radius curvature. This topology can be easily scaled up to massively large arrays by simply adding more tiles and extending the feeding network on the mounting tiling layer. The tiles are assembled onto a single flexible substrate which interconnects the RF, DC and digital traces, allowing for the easy realization of on-demand very large antenna arrays on virtually any practical conformal platform for frequencies up to sub-THz frequency range.

Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation.

Wireless networks beyond 5G will mostly be serving myriads of sensors and other machine-type communications (MTC), with each device having different requirements in respect to latency, error rate, energy consumption, spectral efficiency or other specifications. Multiple-input multiple-output (MIMO) systems remain a central technology towards 6G, and in cases where massive antenna arrays or cell-free networks are not possible to deploy and only moderately large antenna arrays are allowed, the detection problem at the base-station cannot rely on zero-forcing or matched filters and more complex detection schemes have to be used. The main challenge is to find low complexity, hardware feasible methods that are able to attain near optimal performance. Randomized algorithms based on Gibbs sampling (GS) were proven to perform very close to the optimal detection, even for moderately large antenna arrays, while yielding an acceptable number of operations. However, their performance is highly dependent on the chosen “temperature” parameter (TP). In this paper, we propose and study an optimized variant of the GS method, denoted by triple mixed GS, and where three distinct values for the TP are considered. The method exhibits faster convergence rates than the existing ones in the literature, hence requiring fewer iterations to achieve a target bit error rate. The proposed detector is suitable for symmetric large MIMO systems, however the proposed fixed complexity detector is highly suitable to spectrally efficient adaptively modulated MIMO (AM-MIMO) systems where different types of devices upload information at different bit rates or have different requirements regarding spectral efficiency. The proposed receiver is shown to attain quasi-optimal performance in both scenarios.

Cluster Index Modulation for mmWave Communication Systems

In this study, a novel cluster index modulation (CIM) scheme, which is based on indexing the available clusters in the environment, is proposed for future mmWave communication systems. Exploiting the fact that the available clusters in the system are well separated in terms of their angular distribution, we selected the best path for each of them and then performed IM in an algorithmic manner to convey information bits. It is shown that by means of large antenna arrays and analog RF beamforming with the indexed clusters, the destructive effect of inter-beam/cluster interference can be remarkably mitigated. Also, we designed a hybrid beamforming architecture at the transmitter to further reduce the effect of residual inter-beam/cluster interference, where the analog RF beamformer is followed by a digital baseband precoder using the zero-forcing technique. Computer simulations reveal that the proposed scheme can provide better error performance than traditional mmWave communication, and the proposed hybrid architecture outperforms beam index modulation (BIM) for a point-to-point scenario. Semi-analytical derivations and closed-form unconditional pairwise error probability (UPEP) expressions are derived for both analog and hybrid architectures, which confirm the validity and superiority of our proposed scheme.

Low-Complexity and Robust Framework of Precoding for Multi-Panel Massive MIMO

Massive multiple-input multiple-output (m-MIMO) is used to improve the robustness of data transmission and high sum spectral efficiency in 5G systems. For 5G New Radio (NR) systems need to be designed to reduce cost and complexity, most notably hybrid analog-digital beamforming with large-scale antenna array which has become a major research target. In the downlink transmission, we propose a robust and low-complexity framework of precoding design to implement hybrid beamforming (BF) of large antenna arrays without limitation on the size of the antenna. Furthermore, a large number of antenna elements can be coupled into multiple panels m-MIMO for the purpose of reducing costs and saving power. This study proposes to use both azimuth and elevation angles parameters to provide low complexity adaptive beam control in panels m-MIMO system. In contrast to existing works, that aim to eliminate inter-user interference, this work is distinguished by adopting polarization function and space-time block code to the m-MIMO antenna arrays. We also propose a new multi-panel codebook design correspondingly with control parameters. The performance of the proposed framework is analysed with state-of-the-art research comparisons. To this end, the important factor of beamforming which is the Error Vector Magnitude (EVM) will be analyzed and simulated.

Phase-only transmit beampattern design for large phased array antennas with multi-point nulling

Phase-only transmit beampattern design for large phased array antennas with multi-point nulling

An Efficient Adaptive and Steep-Convergent Sidelobes Simultaneous Reduction Algorithm for Massive Linear Arrays

Antenna arrays have become an essential part of most wireless communications systems. In this paper, the unwanted sidelobes in the symmetric linear array power pattern are reduced efficiently by utilizing a faster simultaneous sidelobes processing algorithm, which generates nulling sub-beams that are adapted to control and maintain steep convergence toward lower sidelobe levels. The proposed algorithm is performed using adaptive damping and heuristic factors which result in learning curve perturbations during the first few loops of the reduction process and is followed by a very steep convergence profile towards deep sidelobe levels. The numerical results show that, using the proposed adaptive sidelobes simultaneous reduction algorithm, a maximum sidelobe level of −50 dB can be achieved after only 10 iteration loops (especially for very large antenna arrays formed by 256 elements, wherein the processing time is reduced to approximately 25% of that required by the conventional fixed damping factor case). On the other hand, the generated array weights can be applied to practical linear antenna arrays under mutual coupling effects, which have shown very similar results to the radiation pattern of the isotropic antenna elements with very deep sidelobe levels and the same beamwidth.