Large-scale Antenna Arrays Research Articles

Asymptotic Performance Analysis of MMSE Receivers in Multicell MU-MIMO Systems

For large-scale antenna array multiple-input multiple-output (MIMO) systems, a typical solution often put forward for uplink detection consists in applying low-complexity linear receivers such as zero-forcing (ZF) and minimum mean-square error (MMSE) combiners. In this paper, the performance of uplink MMSE detection has been analyzed in the context of a multi-cell multiuser MIMO system. An approximate expression for the signal-to-interference-plus-noise ratio (SINR) is proposed, which is in simple form and becomes exact in the low SNR regime. This approximate SINR applies to systems where the base station (BS) has the statistical information of the out-of-cell interfering signals, and systems where the BS ignores the presence of such interfering signals. This approximate SINR is tight for arbitrary user powers, arbitrary number of antennas at the base station, and arbitrary channel correlation matrix associated with any particular user. Approximate expressions for capacity and bit-error rate/symbol-error rate have also been derived. Particularly, it can be proven that, when the MMSE receiver ignores the presence of out-of-cell interfering signals, the performance gap between ZF and MMSE vanishes in the high SNR regime.

Decoupling of a Wideband Dual-Polarized Large-Scale Antenna Array With Dielectric Stubs

A technique of decoupling dielectric stubs (DDS) is proposed for mutual coupling reduction of wideband, dual-polarized, and large-scale antenna arrays in this paper, where a low dielectric constant DDS is mounting on a dual-polarized and wideband antenna array seamlessly. The decoupling mechanism of the proposed DDS is qualitatively explained. By properly optimizing the dielectric constant and dimensions of the dielectric stubs, the DDS can reduce the mutual couplings between array elements efficiently. The dielectric stubs are considered as perturbations to slightly localize the electromagnetic fields emitted from the antenna element to lower the space wave coupling. The advantages of the proposed decoupling technique are wideband, low cost, high efficiency, and suitable for large-scale antenna arrays. For demonstration, 4 × 4 dual-polarized and wideband antenna arrays with and without the DDS are simulated, measured, and compared. The simulated results show that the antenna array with the DDS can achieve all port-to-port isolations over 25 dB from 4.4 to 5.0 GHz for all the coupling paths, which are 7 dB higher than the counterparts without the DDS. Moreover, the total efficiencies of the antenna elements with the proposed DDS are also improved. The measured results agree well with the simulated, verifying the effectiveness of the proposed method of DDS for decoupling of large-scale antenna arrays.

Large-scale true-time-delay remote beamforming with EO frequency combs and multicore fiber.

Optical true-time-delay (OTTD) beamforming is a promising solution to support the ultra-broadband radio access network. However, large-scale antenna arrays set at remote radio units require the OTTD counterpart to have corresponding larger-scale channel numbers. Here, we demonstrate an OTTD remote beamforming network with a record 287 channel number using electro-optic frequency combs and multicore fiber. Our proposed scheme can generate beams for both one-dimensional and two-dimensional antenna arrays. We highlight that using multicore fiber not only increases the channel numbers but also supports remote beamforming. We estimate the long-term stability of this remote beamforming network, and 1-ps-level relative time delay variation in 2 h is obtained when using multicore fiber. It is one order of magnitude better than using parallel single-mode fibers. Thus, highly stable beamforming is achieved. These results pave the way for the application of OTTD beamforming in 5G and beyond networks.

Radio Frequency Link and User Selection Algorithm for 5G Mobile Communication System

Based on the millimetre-wave time-domain channel detector, this paper conducts channel measurement and modelling for different scenarios and antenna types in typical frequency bands and thus carries out a study on the propagation characteristics of millimetre-wave channels in typical frequency bands and scenarios for 5G. The time-varying characteristics and modelling methods of millimetre-wave clusters are studied, and the time-varying channel simulation strategy and linear regression fitting method are used to establish the time-varying generation and extinction model of clusters by combining the measured data of 26 GHz large-scale antenna channels. The algorithm is based on the correlation dictionary and achieves the efficient and optimal selection of large-scale antenna arrays in the millimetre-wave band. A low-complexity multiuser hybrid precoding design scheme is proposed based on the wideband millimetre-wave large-scale Multiple-Input Multiple-Output (MIMO) system. The scheme aims to maximize the system spectral efficiency and introduces the net spectral efficiency as a bridge to decouple the analogy precoding design from the digital precoding design, thus significantly reducing the computational complexity. In the analogy precoding stage, a Hungarian algorithm-based beam assignment method is proposed to avoid beam conflicts and maximize the net spectral efficiency of the system; in the digital precoding stage, the equivalent channel matrix of each subcarrier is diagonalized to eliminate multiuser interference. Finally, the effectiveness of the scheme is verified by simulation. In this paper, we focus on the radio frequency (RF) link and user selection algorithm of 5G mobile communication system. Through the study of these two aspects, the RF link of our communication system has high stability and reliability, and through the study of the algorithm used for selection, our results also have a strong practical value.

Feature-Aided Adaptive-Tuning Deep Learning for Massive Device Detection

With the increasing development of Internet of Things (IoT), the upcoming sixth-generation (6G) wireless network is required to support grant-free random access of a massive number of sporadic traffic devices. In particular, at the beginning of each time slot, the base station (BS) performs joint activity detection and channel estimation (JADCE) based on the received pilot sequences sent from active devices. Due to the deployment of a large-scale antenna array and the existence of a massive number of IoT devices, conventional JADCE approaches usually have high computational complexity and need long pilot sequences. To solve these challenges, this paper proposes a novel deep learning framework for JADCE in 6G wireless networks, which contains a dimension reduction module, a deep learning network module, an active device detection module, and a channel estimation module. Then, prior-feature learning followed by an adaptive-tuning strategy is proposed, where an inner network composed of the Expectation-maximization (EM) and back-propagation is introduced to jointly tune the precision and learn the distribution parameters of the device state matrix. Finally, by designing the inner layer-by-layer and outer layer-by-layer training method, a feature-aided adaptive-tuning deep learning network is built. Both theoretical analysis and simulation results confirm that the proposed deep learning framework has low computational complexity and needs short pilot sequences in practical scenarios.

1-Bit Massive MIMO Transmission: Embracing Interference with Symbol-Level Precoding

The deployment of large-scale antenna arrays for cellular base stations (BSs), termed as `Massive MIMO', has been a key enabler for meeting the ever-increasing capacity requirement for 5G communication systems and beyond. Despite their promising performance, fully-digital massive MIMO systems require a vast amount of hardware components including radio frequency chains, power amplifiers, digital-to-analog converters (DACs), etc., resulting in a huge increase in terms of the total power consumption and hardware costs for cellular BSs. Towards both spectrally-efficient and energy-efficient massive MIMO deployment, a number of hardware limited architectures have been proposed, including hybrid analog-digital structures, constant-envelope transmission, and use of low-resolution DACs. In this paper, we overview the recent interest in improving the error-rate performance of massive MIMO systems deployed with 1-bit DACs through precoding at the symbol level. This line of research goes beyond traditional interference suppression or cancellation techniques by managing interference on a symbol-by-symbol basis. This provides unique opportunities for interference-aware precoding tailored for practical massive MIMO systems. Firstly, we characterize constructive interference (CI) and elaborate on how CI can benefit the 1-bit signal design by exploiting the traditionally undesired multi-user interference as well as the interference from imperfect hardware components. Subsequently, we overview several solutions for 1-bit signal design to illustrate the gains achievable by exploiting CI. Finally, we identify some challenges and future research directions for 1-bit massive MIMO systems that are yet to be explored.

Low-Complexity and Low-Overhead Receiver for OTFS via Large-Scale Antenna Array

Orthogonal time frequency space (OTFS) is a modulation technique that is dedicated to the high-speed mobility scenario. However, its transmission involves a two-dimensional convolution of the symbols of interest and the multipath fading channel, and it complicates the equalization. In addition to the high-complexity issue, the existing pilot pattern to estimate the unknown channel accurately requires large overhead to avoid the pilot being contaminated, which is spectrally inefficient. In this paper, we propose a receiver design by the marriage of the OTFS and a large-scale antenna array, which allows low-complexity detection and low-overhead pilot pattern design. The receiver is briefly summarized as follows. First, the received signal from each path of the multipath fading channel is identified by a high-resolution spatial matched filter beamformer facilitated by a large-scale antenna array. Then, the derivation shows that the received signal from an angle of arrival can be regarded as a flat-faded signal with rotations in both the delay and the Doppler coordinates. In addition, the identified signal from each beamforming branch in the delay-Doppler domain can be simply equalized using the channel information estimated by our pilot pattern. We further provide the estimator of the channel fading and the rotations of delay and Doppler in each identified branch. With these estimates, the delay and the Doppler shift in each identified branch can be compensated, and then, the signals from all angles of arrival are combined as different diversity versions. Furthermore, the equalization complexity is reduced to 5.0% of the message passing detection and our pilot pattern with only around 25% overhead of the existing pilot pattern ensures the same protection of the pilot. Moreover, the carrier frequency offset is considered and be compensated. The significance of the proposed receiver is its practicality, and it achieves better error performance with lower receiver complexity and lower overhead compared to the existing approaches, at the cost of a linear beamforming antenna array. The price is quite affordable, since the linear antenna array has moderate computational complexity, and it is deployed widely in current and future wireless communication systems. Eventually, the efficiency, the reliability, and the low complicacy of the proposed receiver approach are further validated by the numerical results.

Blind Diagnosis for Millimeter-Wave Large-Scale Antenna Systems

Millimeter-wave (mmWave) communication systems rely on large-scale antenna arrays to combat large path-loss at mmWave band. Due to hardware characteristics and deployment environments, mmWave large-scale antenna systems are vulnerable to antenna element blockages and failures, which necessitate diagnostic techniques to locate faulty antenna elements for calibration purposes. Current diagnostic techniques require full or partial knowledge of channel state information (CSI), which can be challenging to acquire in the presence of antenna failures. In this letter, we propose a blind diagnostic technique to identify faulty antenna elements in mmWave large-scale antenna systems, which does not require any CSI knowledge. By jointly exploiting the sparsity of mmWave channel and failure pattern, we first formulate the diagnosis problem as a joint sparse recovery problem. Then, the atomic norm is introduced to induce the sparsity of mmWave channel over continuous Fourier dictionary. An efficient algorithm based on alternating direction method of multipliers (ADMM) is proposed to solve the formulated problem. Finally, the performance of the proposed technique is evaluated through numerical simulations.

Energy-Efficient Hybrid Symbol-Level Precoding for Large-Scale mmWave Multiuser MIMO Systems

We address the symbol-level precoding design problem for the downlink of a multiuser millimeter wave (mmWave) multiple-input multiple-output (MIMO) wireless system where the transmitter is equipped with a large-scale antenna array. The high cost and power consumption associated with the massive use of radio frequency (RF) chains prohibit fully-digital implementation of the precoder, and therefore, we consider a hybrid analog-digital architecture where a small-sized baseband precoder is followed by two successive networks of analog on-off switches and variable phase shifters according to a fully-connected structure. We jointly optimize the digital baseband precoder and the states of the switching network on a symbol-level basis, i.e., by exploiting both the channel state information (CSI) and the instantaneous data symbols, whereas the phase-shifting network is designed only based on the CSI due to practical considerations. Our approach to this joint optimization is to minimize the Euclidean distance between the optimal fully-digital and the hybrid symbol-level precoders. Remarkably, the use of a switching network allows for power-savings in the analog precoder by switching some of the phase shifters off according to the instantaneously optimized states of the switches. Our numerical results indicate that, on average, up to 50 percent of the phase shifters can be switched off. We provide an analysis of energy efficiency by adopting appropriate power dissipation models for the analog precoder, where it is shown that the energy efficiency of precoding can substantially be improved thanks to the phase shifter selection approach, compared to the fully-digital and the state-of-the-art hybrid symbol-level schemes.

A Reconfigurable Access Scheme for Massive-MIMO MTC Networks

This paper presents an efficient reconfigurable access scheme for massive machine-type communication networks, where to provide massive connectivity, the base station is equipped with a large-scale antenna array. In particular, to maximize the expected throughput of the network, the scheme uses a frame divided into two segments: grant-based and grant-free that are more efficient for devices with high and low expected throughput, respectively. At the beginning of each frame, the base station decides how to partition the two segments, the resource allocation in the grant-based segment and the access probabilities in the grant-free segment to maximize the expected throughput based on the device traffic profiles. The corresponding optimization problem is formulated, and a sub-optimal solution algorithm with low computational complexity is proposed. The performance of the proposed access scheme is evaluated under different conditions to demonstrate its advantages in terms of achieved throughput and average packet delay.

A novel low-RCS antenna array based on integration of electromagnetic metasurface and conventional antenna

Aiming at obtaining low scattering antenna array, in this paper a novel method of integrating electromagnetic metasurface with conventional antenna is proposed. The theoretical analysis and practical implementation of this method are presented. Using this method, a novel antenna array is obtained by connecting partial unit cells of metasurface with transmission line and adopting coaxial excitations. In the radiation mode, the metasurface is excited and radiates effectively. Besides, the array has almost the same performance as the conventional array. In the scattering mode, this array demonstrates low in-band RCS due to the scattering cancellation of middle metasurface and other surrounding structures. Using this method, a 2 × 1 array, as an example, is designed and numerically analyzed. The results show that the array has the well-behaved radiation performance and low RCS property. The working principle of the proposed array is illustrated by investigating the current and resultant field. Further analysis also reveals the effecting law of metasurface unit cells in antenna's radiation and scattering performance. Therefore, flexible designs can be obtained to fit different requirements. Finally, experiments are conducted. And the good agreement between computations and measurements further verifies the validity of the proposed design. Moreover, the proposed method also features easy implementation and high integrity and can be extended to the designing of large scale array antennas.

Hybrid beamforming in Multiuser mmWave Massive MIMO System

Conventional MIMO systems typically use full digital processing for optimal performance while it requires an independent radio frequency (RF) chain for each antenna, which is not feasible for large-scale antenna arrays due to the high cost, high-power consumption and high complexity of RF chain components in high frequencies. Fortunately, in recent years, the Hybrid beamforming has been proposed as an efficient and auspicious technique for the practical implementation of millimeter-Wave (mmWave) multiple-input multiple-output (MIMO) wireless systems. In this paper we propose a novel HBF (Hybrid beamforming) algorithm and simulate the sum rate of the proposed approach, then compare it with other algorithms in the sum rates. Specifically, the HBF algorithm as well as the maximizing minimum phase difference algorithm are based on the MMSE (Minimum Mean Square Error). For the full-digital beamforming and the beam steering, the ZF (zero forcing) criterion is employed. Simulations demonstrate that the performance of the proposed algorithm is similar to the full digital algorithm, and its robustness is confirmed by comparisons with other existing algorithms under the same channel model.

Dual-Polarized Two-Dimensional Multibeam Antenna Array With Hybrid Beamforming and its Planarization

A feeding network crossing method is proposed to form an N $\times $ N dual-polarized two-dimensional multibeam antenna array (2D-MBAA) with analog-digital hybrid beamforming, which solves the problem of needing additional connection structure between the dual-polarized antenna array and analog feeding network for large-scale antenna arrays. The main design work is focused on the analog-beamformer (ABF) and two structures are proposed. One is vertical-crossed (VC) ABF and the other is parallel-crossed (PC) ABF, and the latter is a more complex design in exchange for a significantly reduced profile. As a verification, a $4\times 4$ dual-polarized 2D-MBAA with PC-ABF has been fabricated and tested. It is operated at 3.45GHz with a bandwidth of about 10%, and it can realize a scanning angle of ±45°.

Intelligent Interactive Beam Training for Millimeter Wave Communications

Millimeter wave communications, equipped with large-scale antenna arrays, are able to provide Gbps data rates by exploring abundant spectrum resources. However, the use of a large number of antennas along with narrow beams causes a large overhead in obtaining channel state information (CSI) via beam training, especially for fast-changing channels. To reduce beam training overhead, in this paper we develop an interactive learning design paradigm (ILDP) that makes full use of domain knowledge of wireless communications (WCs) and adaptive learning ability of machine learning (ML). Specifically, the ILDP is fulfilled via deep reinforcement learning (DRL), which yields DRL-ILDP, and consists of communication model (CM) module and adaptive learning (AL) module, which work in an interactive manner. Then, we exploit the DRL-ILDP to design efficient beam training algorithms for both multi-user and user-centric cooperative communications. The proposed DRL-ILDP based algorithms enjoy three folds of advantages. Firstly, ILDP takes full advantages of the existing WC models and methods. Secondly, ILDP integrates powerful ML elements, which facilitates extracting interested statistical and probabilistic information from environments. Thirdly, via the interaction between the CM and AL modules, the algorithms are able to collect samples and extract information in real-time and sufficiently adapt to the ever-changing environments. Simulation results demonstrate the effectiveness and superiority of the designed algorithms.

Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System

In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

Millimeter-Wave Hybrid Precoder Design With a Fast Iterative Beam Split and Detection Algorithm

The hybrid analog and digital structure of large-scale antenna array offers a good tradeoff between cost and flexibility, which is a promising solution for millimeter-wave (mm-wave) system. This letter proposes a novel hybrid precoder (HP) design with an iterative beam split and detection algorithm, which works well for sparse mm-wave channels. The proposed strategy can extract the dominant propagation paths by iterative trainings, where the number of dominant paths up to that of radio frequency chains can be detected. The effectiveness and time overhead parameterized by the number of iterations are investigated. The experimental results demonstrated that we can approach optimal HP design with the proposed algorithm with only eight iterations for measured channels, which significantly reduced the complexity compared to the exhaustive search method.

Tensor-Based Multi-Dimensional Wideband Channel Estimation for mmWave Hybrid Cylindrical Arrays

Channel estimation is challenging for hybrid millimeter wave (mmWave) large-scale antenna arrays which are promising in 5G/B5G applications. The challenges are associated with angular resolution losses resulting from hybrid front-ends, beam squinting, and susceptibility to the receiver noises. Based on tensor signal processing, this paper presents a novel multi-dimensional approach to channel parameter estimation with large-scale mmWave hybrid uniform circular cylindrical arrays (UCyAs) which are compact in size and immune to mutual coupling but known to suffer from infinite-dimensional array responses and intractability. We design a new resolution-preserving hybrid beamformer and a low-complexity beam squinting suppression method, and reveal the existence of shift-invariance relations in the tensor models of received array signals at the UCyA. Exploiting these relations, we propose a new tensor-based subspace estimation algorithm to suppress the receiver noises in all dimensions (time, frequency, and space). The algorithm can accurately estimate the channel parameters from both coherent and incoherent signals. Corroborated by the Cramer-Rao lower bound (CRLB), simulation results show that the proposed algorithm is able to achieve substantially higher estimation accuracy than existing matrix-based techniques, with a comparable computational complexity.

Effects of Wall Reflection on the Per-Antenna Power Distribution of ZF-Precoded ULA for Indoor mmWave MU-MIMO Transmissions

Indoor access points (APs) with large-scale antenna arrays are commonly deployed in the vicinity of a wall, where wall reflection (WR) affects the indoor electromagnetic (EM) wave propagation. In this letter, we investigate the effects of WR on the per-antenna power distribution of a transmit uniform linear array (ULA) adopting a zero-forcing (ZF) precoder. A new channel model is constructed to characterise the impact of both the line-of-sight (LOS) path and the WR path on indoor millimetre wave (mmWave) multi-user (MU) multiple-input multiple-output (MIMO) downlink transmissions. Specifically for the dual user equipment (UE) scenario, the ZF precoding matrix is analytically obtained and verified through simulations. The effects of WR on the per-antenna power distribution of the ZF-precoded ULA, in terms of the normalised power distribution and maximum power ratio (MPR), are evaluated through the comparisons between our proposed channel model and the pure LOS channel model. Our analytical and numerical results reveal the impact of AP configurations (the number of antennas and the AP-wall distance), multi-user spatial distribution (the angle of departure (AoD) and length of the LOS path for each user), and wall parameters (permittivity and thickness) on the power distribution across the ZF-precoded ULA. It is found that the effects of WR will exacerbate the uneven power distribution across the ZF-precoded ULA.

Channel Path Identification in mmWave Systems With Large-Scale Antenna Arrays

We consider the uplink channel estimation problem in a millimeter wave (mmWave) system with large-scale antenna arrays. Unlike many existing works which estimate the channel assuming that the number of channel paths is known a priori , we address the problem of channel estimation with an unknown number of channel paths. The spatial channel is transformed into the beamspace channel by the discrete Fourier transform (DFT). Based on the sparsity property of the beamspace channel, we propose three algorithms to estimate the number of paths, direction of arrivals (DoAs) and path gains. The first one is the Spectrum Weighted Identification of Signal Sources (SWISS) for the case when the channel statistics are unknown, which introduces a weight vector to amplify the desired signal and suppress the noise. The second one is the Neyman-Pearson criterion based-Detector (NPD) based on the Rician channel model, which adopts the Neyman-Pearson criterion to decide whether there exists a path on each DFT point. In practice, the DoAs are continuously distributed, leading to the power leakage problem. We solve this leakage problem by proposing the combined algorithm with leakage (CAL). Simulation results show that the proposed algorithms perform better than the conventional spatial smoothing.

Novel 3-D Irregular-Shaped Model for Massive MIMO V2V Channels in Street Scattering Environments

In this letter, we develop a three-dimensional (3-D) irregular-shaped semi-ellipsoid channel model for massive multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) communication in street scattering environments. The geometric model is irregular-shaped due to the scattering environments cut by vertical structures along the roadside. With employing a uniform planar array (UPA), we first characterize the geometrical relationships between transmitter and receiver constrained by different boundaries of scattering regions in detail. Then we derive the closed-form expressions for the probability density functions (PDFs) of the angle of departure (AoD) and joint time of arrival (ToA)/AoD in azimuth and elevation planes. Moreover, we discuss the impacts of the street width and large-scale antenna array on channel statistical properties. Numerical results show that our proposed model can capture characteristics of massive MIMO V2V channels while in close agreement with the prior reported results, thereby demonstrating the efficiency of the proposed model that can provide guidance for system design and performance evaluation of massive MIMO V2V communication in street scattering environments.