Large Antenna Arrays Research Articles

ELAA Channel Characterization with Parameter Estimation Based on a Generalized Array Manifold Model

The extremely large antenna arrays (ELAAs) and millimeter-wave systems have become key techniques for obtaining higher frequency spectrum efficiency in sixth-generation (6G) communication systems. It is necessary to determine appropriate statistical models to describe the channel characteristics for the ELAAs, such as spatial non-stationarity, dispersion in angular domain, and spatial consistency. Thus, a signal model based on the generalized array manifold (GAM) that describes the dispersion in direction using the definition of the slightly distributed scatterer (SDS) is proposed in this work. An estimator for the parameters of the GAM model, namely GAM Space-Alternating Generalized Expectation-maximization (GAM-SAGE), is also designed. Moreover, a method to obtain a stochastic SDS-based channel model (SBCM) that is capable of reproducing the spatial consistency is proposed. The method is then used to establish measurement-based models for line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios using a 40×40 receiver (Rx) planar antenna array at a carrier frequency of 40 GHz. The results demonstrate that the SBCM is capable of achieving spatially consistent results and outperforms the specular-path (SP) models in completely characterizing the ELAA channels at millimeter-wave bands, which are fundamental for the design of 6G.

Electrically large aperture horn array antenna with low side lobe level for Ku‐band frequency

AbstractUsing electrically large antennas for array applications may cause grating lobes and this can change the array pattern. To achieve a low side lobe level for boresight pattern for linear or planar array antennas, in the literature it is seen that the common method is the use of feeding each antenna with some amplitude distribution and keeping the distance between the antennas less than a wavelength. However, in this study the distance between the antennas is chosen to be greater than a wavelength. Therefore, the common methods cannot be used. To achieve a low side lob level in the radiation pattern for an array of antennas composed of antennas with a distance greater than one wavelength. The aperture length of each antenna is chosen to be variable and an analytical solution method is used to calculate the amplitude of each aperture field of the horn antennas. Thus, the determination of unit horns' optimal aperture lengths Li can be considered a multivariable optimization problem. This challenging problem was achieved using a novel meta‐heuristic optimization algorithm called the differential evolutionary algorithm (DEA), where the aperture lengths of each array element are taken as inputs to find the optimal side lobe level (SLL). Based on the obtained solutions, a 4 × 1 horn array antenna design alongside its feed network was designed for Ku‐band applications. The design of the array is simulated in CST simulation software, and the obtained simulated results had been compared with experimental results and counterpart designs in the literature.

Autoencoder‐based deep learning for massive multiple‐input multiple‐output uplink under high‐power amplifier non‐linearities

In this paper, the authors study the compensation of high-power amplifier (HPA) non-linear distortion in the multi-user (MU) massive multiple-input multiple-output (MIMO) systems and focus on uplink transmission, where the base station (BS) uses a large antenna array. First, the authors present a non-linear distortion iterative cancellation (NDIC) algorithm-based MMSE and approximate message passing (AMP) at the receiver level, in order to estimate and mitigate jointly a non-linear distortion and the channel noise. Second, the authors propose a novel distortion cancellation technique based on deep learning. At this level, the authors first introduce a multilayer neural network, trained in the Levenberg–Marquardt algorithm by eliminating HPA non-linearities on the ‘Pre distortion’ transmitter and ‘Post distortion’ receiver side. Next, the authors developed a novel end-to-end (E2E) learning approach for the joint transmitter and non-coherent receiver in the Rayleigh fading channel. The basic idea lies in the use of deep neural networks (DNNs), auto encoder (AE) for unknown channels, where DNNs are applied to perform several functions and modules existing in the transmission chain. The simulation results demonstrate the strong potential of the proposed approach E2E in terms of improving the link quality and symbol error rate (SER) compared to other compensation techniques presented in this work.

An Inhomogeneous Plane-Wave Based Single-Level Fast Direct Solver for the Scattering Analysis of Extremely Large Antenna Arrays

A semianalytic single-level fast direct solver (FDS) is presented for the method-of-moments (MoM) analysis of large planar antenna arrays with up to few thousands of complex elements on current desktop computers. Assuming a partition of the impedance matrix, the proposed scheme factorizes the near-field blocks via a singular value decomposition (SVD)-based low-rank approximation and the intermediate-field blocks via an analytical inhomogeneous plane-wave (IPW) expansion. The near- and intermediate-field bases are then unified to leverage the Woodbury formula for the inversion. The solutions for the excitation of each array ports are obtained at once with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(N^{15/7} \ln ^{3} N)$ </tex-math></inline-formula> computational cost and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(N^{11/7} \ln N)$ </tex-math></inline-formula> memory for array problems with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> basis functions. The complexities are traced precisely and the error is controlled by means of analytical formulas for the required number of IPWs. The FDS is also combined with a macrobasis function (MBF) approach to deal with antenna geometries containing many subwavelength details. This article includes simulations of regular arrays of about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$169^{2}~3$ </tex-math></inline-formula> -D dipoles and an irregular array composed of seven stations of the low-frequency square kilometer array (SKA) telescope comprising each 256 log-periodic (LP) antennas. The proposed FDS allows the analysis of the coupling effect both within any one station but also between the stations of the telescope, a size and complexity of problem which has not been possible with existing published techniques.

Index Modulation Pattern Design for Non-Orthogonal Multicarrier Signal Waveforms

Spectral efficiency improvement is a key focus in most wireless communication systems and achieved by various means such as using large antenna arrays and/or advanced modulation schemes and signal formats. This work proposes to further improve spectral efficiency through combining non-orthogonal spectrally efficient frequency division multiplexing (SEFDM) systems with index modulation (IM), which can efficiently make use of the indices of activated subcarriers as communication information. Recent research has verified that IM may be used with SEFDM to alleviate inter-carrier interference (ICI) and improve error performance. This work proposes new SEFDM signal formats based on novel activation pattern designs, which limit the locations of activated subcarriers and enable a variable number of activated subcarriers in each SEFDM subblock. SEFDM-IM system designs are developed by jointly considering activation patterns, modulation schemes and signal waveform formats, with a set of solutions evaluated under different spectral efficiency scenarios. Detailed modelling of coded systems and simulation studies reveal that the proposed designs not only lead to better bit error rate (BER) but also lower peak-to-average power ratio (PAPR) and reduced computational complexity relative to other reported index-modulated systems.

Massive MIMO Empowered Wireless Powered Sensor Networks: An Optimal Design With Statistical CSI

Wireless energy transfer (WET) has been anticipated as a viable solution to combat the challenges of scarce energy supply in Internet-of-Things (IoT) applications. To reap the benefit brought by large scale antenna arrays, massive multiple-input-multiple-out (MIMO) empowered wireless powered sensor networks (WPSNs) is investigated in this letter to meet the urgent demand for reliable and self-sustainable IoT applications. Considering a large-scale antenna array deployed at the access point for energy transfer to and information collection from the multiple-antenna wireless powered sensors, the hardening property in energy transfer is generalized and theoretically proved based on random matrix theory. Based on the hardening properties, the harvesting energy and the uplink sum-rate for sensor data collection are found to be asymptotically deterministic and depend on the statistical channel state information (CSI) only. The hardening properties in both energy and information transfers enable a novel optimal design on the WPSNs based on statistical CSI only. Particularly, the designs of the downlink energy beamforming and multiple uplink information beamforming can be decoupled, and global optimal solutions for the beamforming and time allocation to maximize the uplink sum-rate given downlink power constraint are found in semi-closed forms. Different from prior WPSNs designs, statistical CSI but not instantaneous CSI is exploited and frequent channel estimation and heavy communication overhead can be avoided with a much lower computation complexity, thus improving the practicability and sustainability of the WPSNs. The effectiveness of the proposed design is finally demonstrated by numerical simulations.

A beam broadening method for phased arrays in wireless communications

5G and IEEE 802.11ay introduce the use of the millimeter band as one promising solution to provide broadband wireless communication at multi-Gb/s user data rate. Due to the severe path-loss at such frequencies, it is generally assumed that large antenna arrays are used at the base station to steer narrow beams and build highly directional communication links towards the terminal points. However, broader and less directional beams are also of high interest in some of the steps involved in the establishment or the maintenance of the communication links . Indeed, search of a large area by narrow beams becomes too time consuming and link outage becomes more critical, thus affecting the latency and the robustness of the communications. A method enabling an adaptation of the beam widths is then worthwhile to consider. In this article, we investigate how narrow beams naturally produced by large antenna arrays can be broadened to adapt the beam width to a desired angular sector. We consider that the multi-antenna processing is performed by phase shifters on the radio-frequency stage since fully digital beamforming is hardly feasible in practice at such high frequencies. The main idea of our systematic phase-only beam broadening technique relies on the determination of a quadratic phase excitation law from a desired beam width and steering angle. We first lead a thorough analysis of the radiation behavior regarding the coefficients of such quadratic excitation. We then propose a calculation method for determining the polynomial coefficients as a function of the desired beam width and steering angle. This non-iterative beam broadening method is described for boresight and non-boresight directions and is intended for discrete antenna arrays.

AI-based indoor localization using mmWave MIMO channel at 60 GHz

In recent years, indoor localization using wireless systems has been an important area of research for its applications towards health, security and the tracking of users. A Global Positioning System (GPS) is considered as the best solution for localization for outdoor scenarios but it fails to provide accurate positioning for indoor scenarios. Wi-Fi fingerprinting methods using received signal strength from multiple access points are popular for solving indoor localization problem. As the wireless systems move towards higher frequencies, higher bandwidth and a large antenna array, sensing has also become feasible along with communication, which is an important research area towards 6G named as Integrated Communication And Sensing (ISAC). ISAC relies on sensing parameter estimations, such as estimation of fine range, Doppler and angular information which contains the signature of the surrounding objects. A localization problem can be solved by analysing the sensing parameters. In this paper, we propose a solution for the localization problem for IEEE 802.11ay WLAN systems based on signal processing and Machine Learning (ML) in indoor scenarios. (...)

Beam Focusing for Near-Field Multiuser MIMO Communications

Large antenna arrays and high-frequency bands are two key features of future wireless communication systems. The combination of large-scale antennas with high transmission frequencies often results in the communicating devices operating in the near-field (Fresnel) region. In this paper, we study the potential of beam focusing, feasible in near-field operation, in facilitating high-rate multi-user downlink multiple-input multiple-output (MIMO) systems. As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signalling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays. We first provide a mathematical model to characterize near-field wireless channels as well as the transmission pattern for the considered antenna architectures. Then, we formulate the beam focusing problem for the goal of maximizing the achievable sum-rate in multi-user networks. We propose efficient solutions based on the sum-rate maximization task for fully-digital, (phase shifters based-) hybrid and DMA architectures. Simulation results show the feasibility of the proposed beam focusing scheme for both single- and multi-user scenarios. In particular, the designed focused beams provide a new degree of freedom to mitigate interference in both angle and distance domains, which is not achievable using conventional far-field beam steering, allowing reliable communications for uses even residing at the same angular direction.

Analysis of Multipath Components Distributions Over a Large Array in Indoor mmWave Channels

In this article, we analyze the characteristics of indoor radio channels at millimeter waves. Measurements were performed using a large virtual antenna array (VAA), for massive multiple input multiple output (MIMO) applications. The results in terms of correlation and multipath components (MPCs) characteristics are analyzed for the whole large array and virtual subarrays composing it. Common MPC among the subarrays are identified, and the impact on the overall channel large-scale parameters is discussed.

Fourier Plane-Wave Series Expansion for Holographic MIMO Communications

Imagine a MIMO communication system that fully exploits the propagation characteristics offered by an electromagnetic channel and ultimately approaches the limits imposed by wireless communications. This is the concept of Holographic MIMO communications. Accurate and tractable channel modeling is critical to understanding its full potential. Classical stochastic models used by communications theorists are derived under the electromagnetic far-field assumption, i.e. planar wave approximation over the array. However, such assumption breaks down when electromagnetically large (compared to the wavelength) antenna arrays are considered. In this paper, we start from the first principles of wave propagation and provide a Fourier plane-wave series expansion of the channel response, which fully captures the essence of electromagnetic propagation in arbitrary scattering and is also valid in the (radiative) near-field. The expansion is based on the Fourier spectral representation and has an intuitive physical interpretation, as it statistically describes the angular coupling between source and receiver. When discretized uniformly, it leads to a low-rank semi-unitarily equivalent approximation of the electromagnetic channel in the angular domain. The developed channel model is used to compute the ergodic capacity of a point-to-point Holographic MIMO system with different degrees of channel state information.

Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation

Future wireless systems are trending towards higher carrier frequencies that offer larger communication bandwidth but necessitate the use of large antenna arrays. Signal processing techniques for channel estimation currently deployed in wireless devices do not scale well to this “high-dimensional" regime in terms of performance and pilot overhead. Meanwhile, training deep learning-based approaches for channel estimation requires large labeled datasets mapping pilot measurements to clean channel realizations, which can only be generated offline using simulated channels. In this paper, we develop a novel unsupervised over-the-air (OTA) algorithm that utilizes noisy received pilot measurements to train a deep generative model to output beamspace MIMO channel realizations. Our approach leverages Generative Adversarial Networks (GAN), while using a conditional input to distinguish between Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) channel realizations. We also present a federated implementation of the OTA algorithm that distributes the GAN training over multiple users and greatly reduces the user side computation. We then formulate channel estimation from a limited number of pilot measurements as an inverse problem and reconstruct the channel by optimizing the input vector of the trained generative model. Our proposed approach significantly outperforms Orthogonal Matching Pursuit on both LOS and NLOS channel models, and EM-GM-AMP – an Approximate Message Passing algorithm – on LOS channel models, while achieving comparable performance on NLOS channel models in terms of the normalized channel reconstruction error. More importantly, our proposed framework has the potential to be trained online using real noisy pilot measurements, is not restricted to a specific channel model and can even be utilized for a federated OTA design of a dataset generator from noisy data.

Hybrid Beamforming for Intelligent Reflecting Surface Aided Millimeter Wave MIMO Systems

As communication systems that employ millimeter wave (mmWave) frequency bands must use large antenna arrays to overcome the severe propagation loss of mmWave signals, hybrid beamforming has been considered as an integral component of mmWave communications. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative technology that can significantly improve the performance of mmWave communication systems through the use of low-cost passive reflecting elements. In this paper, we study IRS-aided mmWave multiple-input multiple-output (MIMO) systems with hybrid beamforming architectures. We first exploit the sparse-scattering structure and large dimension of mmWave channels to develop the joint design of IRS reflection matrix and hybrid beamformer for narrowband MIMO systems. Then, we generalize the proposed joint design to broadband MIMO systems with orthogonal frequency division multiplexing (OFDM) modulation by leveraging the angular sparsity of frequency-selective mmWave channels. Simulation results demonstrate that the proposed joint designs can significantly enhance the spectral efficiency of the systems of interest and achieve superior performance over the existing designs.

Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems.

Intelligent reconfigurable surfaces (IRSs) have gained much attention due to their passive behavior that can be a successor to relays in many applications. However, traditional relay systems might still be a perfect choice when reliability and throughput are the main concerns in a communication system. In this work, we use an IRS along with a decode-and-forward relay to provide a possible solution to address one of the main challenges of future wireless networks which is providing reliability. We investigate a robust transceiver design against the residual self-interference (RSI), which maximizes the throughput rate under self-interference channel uncertainty-bound constraints. The yielded problem turns out to be a non-convex optimization problem, where the non-convex objective is optimized over the cone of semidefinite matrices. We propose a novel mathematical method to find a lower bound on the performance of the IRS that can be used as a benchmark. Eventually, we show an important result in which, for the worst-case scenario, IRS can be helpful only if the number of IRS elements are at least as large as the size of the interference channel. Moreover, a novel method based on majorization theory and singular value decomposition (SVD) is proposed to find the best response of the transmitters and relay against worst-case RSI. Furthermore, we propose a multi-level water-filling algorithm to obtain a locally optimal solution iteratively. We show that our algorithm performs better that the state of the art in terms of time complexity as well as robustness. For instance, our numerical results show that the acheivable rate can be increased twofold and almost sixfold, respectively, for the case of small and large antenna array at transceivers.

Hybrid Beamforming Designs for Frequency-Selective mmWave MIMO Systems With Per-RF Chain or Per-Antenna Power Constraints

Configuring precoders and combiners is a major challenge to deploy practical multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication systems with large antenna arrays. Most prior work addresses the problem focusing on a total transmit power constraint. In practical transmitters, however, power amplifiers must operate within their linear range, so that a power constraint applies to each one of the input signals to these devices. Therefore, precoder and combiner designs should incorporate per-antenna or per-radio frequency (RF) chain transmit power constraints. We focus on such problem for frequency-selective channels with multicarrier modulation, and assuming hybrid analog/digital architectures based on fully connected analog blocks implemented with finite-resolution phase shifters. We first derive an all-digital solution which aims to maximize spectral efficiency. Then, we develop hybrid precoders and combiners by approximately matching the corresponding all-digital matrices while still enforcing the power constraints. Numerical results show that the proposed all-digital design performs close to the upper bound given by the standard waterfilling-based solution with a total power constraint. Additionally, the hybrid designs exhibit a moderate loss even when low-resolution phase shifters are considered.

Spectrum Shaping for Multiple Link Discovery in 6G THz Systems

This paper presents a novel antenna configuration to measure directions of multiple signal sources at the receiver in a THz mobile network via a single channel measurement. Directional communication is an intrinsic attribute of THz wireless networks and the knowledge of direction should be harvested continuously to maintain link quality. Direction discovery can potentially impose an immense burden on the network that limits its communication capacity exceedingly. To utterly mitigate direction discovery overhead, we propose a novel technique called spectrum shaping capable of measuring direction, power, and relative distance of propagation paths via a single measurement. We demonstrate that the proposed technique is also able to measure the transmitter antenna orientation. We evaluate the performance of the proposed design in several scenarios and show that the introduced technique performs similar to a large array of antennas while attaining a much simpler hardware architecture. Results show that the spectrum shaping with only two antennas placed 0.5 mm, 5 mm, and 1 cm apart performs direction of arrival estimation similar to a much more complex uniform linear array equipped with 7, 60, and 120 antennas, respectively.

Joint Channel Estimation and Feedback for mm-Wave System Using Federated Learning

Due to the high propagation path loss in the millimeter wave (mm-wave) system, it is necessary to use the large antenna array for obtaining the high beamforming gain in mm-wave systems. In order to reduce the cost and complexity of hardware, an architecture called hybrid precoding is proposed, which divides the precoding operation into the analog domain and the digital domain. However, this poses a challenge for channel estimation in mm-wave systems. In addition, due to the great amount of users and antennas, the feedback overhead of channel estimation is also an issue that needs to be considered. In the letter, we consider the mm-wave massive multiple-input-multiple-output (MIMO) system and propose a federated learning (FL)-base channel state information (CSI) estimation and feedback (FCEF) scheme, where each user trains the local model through the local dataset and exchanges the model parameters with the base station (BS). Thanks to upload model parameters rather than huge channel data, the FCEF scheme has lower transmission overhead than centralized learning (CL). The architecture of the proposed FCEF model is described in the letter, which consists of a CSI estimation and compression (CEC) network and a CSI recovery (CR) network. Numerical results demonstrate that the proposed FCEF scheme has good performance for CSI estimation and feedback in mm-wave MIMO systems, which is close to that of the CL-based scheme while ensuring lower transmission overhead.

Binarized Aggregated Network With Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to be fed back from the user equipment to the base station in frequency division duplexing (FDD) mode. However, the overhead of the direct feedback is unacceptable due to the large antenna array in massive MIMO system. Recently, deep learning is widely adopted to the compressed CSI feedback task and proved to be effective. In this paper, a novel network named aggregated channel reconstruction network (ACRNet) is designed to boost the feedback performance with network aggregation and parametric rectified linear unit (PReLU) activation. The practical deployment of the feedback network in the communication system is also considered. Specifically, the elastic feedback scheme is proposed to flexibly adapt the network to meet different resource limitations. Besides, the network binarization technique is combined with the feature quantization for lightweight and practical deployment. Experiments show that the proposed ACRNet outperforms loads of previous state-of-the-art networks, providing a neat feedback solution with high performance, low cost and impressive flexibility.

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