Massive Multiple-input And Multiple-output Research Articles

Multi-Cluster Approaches to Radio Sensor Array Channel Modeling and Simulation.

In this paper, we explore the physical propagation environment of radio waves by describing it in terms of distant scattering clusters. Each cluster consists of numerous scattering objects that may exhibit certain statistical properties. By utilizing geometry-based methods, we can study the channel second-order statistics (CSOS), where each distant scattering cluster corresponds to a CSOS, contributes a portion to the Doppler spectrum, and is associated with a state-space multiple-input and multiple-output (MIMO) radio channel model. Consequently, the physical propagation environment of radio waves can be modeled by summing multiple state-space MIMO radio channel models. This approach offers three key advantages: simplicity, the ability to construct the entire Doppler power spectrum from multiple uncorrelated distant scattering clusters, and the capability to obtain the channels contributed by these clusters by summing the individual channels. This methodology enables the reconstruction of the radio wave propagation environment in a simulated manner and is crucial for developing massive MIMO channel models.

Phase Shift Design for RIS-Aided Cell-Free Massive MIMO With Improved Differential Evolution

This paper proposes a novel phase shift design for cell-free massive multiple-input and multiple-output (MIMO) systems assisted by reconfigurable intelligent surface (RIS), which only utilizes channel statistics to achieve the uplink sum ergodic throughput maximization under spatial channel correlations. Due to the non-convexity and the scale of the derived optimization problem, we develop an improved version of the differential evolution (DE) algorithm. The proposed scheme is capable of providing high-quality solutions within reasonable computing time. Numerical results demonstrate superior improvements of the proposed phase shift designs over the other benchmarks, particularly in scenarios where direct links are highly probable.

Optimized transmit antenna selection and self-attention based convolutional resource allocation model for massive MIMO technology

Massive multiple input and multiple output (MIMO) plays an important role in enhancing the transmission reliability and capacity of the transmitting channel. However, the resource allocation (RA) and transmit antenna selection (TAS) scheme are essential for massive MIMO systems to reduce implementation costs and complex operations. Because the presence of a large antenna consumes enormous resources that must be reduced in order to develop a user-friendly model. Hence, this article introduces a novel TAS and RA scheme in a massive MIMO system that can enhance communication performance efficiently. In this study, an improved sheep flock optimization algorithm (ISFOA) is first emphasized to select the efficient antenna, thus effectively minimizing the cost and the design complexity. Then, a novel deep learning (DL) based self-attention aided deep convolutional neural network (SA-DCNN) model for the stable allocation of resources to all available users is proposed. Thus, the user equipment (UE) can develop a better quality path and reduce the power consumption of the entire system. In the experimental scenario, the performance of the proposed model is compared with an existing technique based on sum rate, spectral efficiency (SE) and energy efficiency (EE) by varying base station (BS) antennas, varying user, signal to noise ratio (SNR), and sub-carriers along with the computational performance. Particularly, when the SNR=10 dB, the proposed model obtains the SE of about 50 bits/s Hz compared to the existing techniques.

A Novel Performance Bound for Massive MIMO Enabled HetNets

Massive multiple-input and multiple-output (MIMO) networks with higher throughput rates, where a base station (BS) with a large-scale antenna array serves multiple users, have been widely employed in next-generation wireless communication test systems. Massive MIMO-enabled dense heterogeneous networks (HetNets) have also emerged as a promising architecture to increase the system spectrum efficiency and improve the system reliability. Massive MIMO-enabled HetNets have been successfully exploited in sustainable Internet of Thing networks (IoTs). In order to facilitate the testing and performance estimation of IoTs communication systems, this paper studies the achievable rate performance of massive MIMO and HetNets. Differing from the existing literature, we first consider an interference power model for massive MIMO-enabled HetNets. We next obtain an expression for the signal-to-interference-plus-noise ratio (SINR) by introducing an interference power. Furthermore, we derive a new closed-form lower bound expression for the achievable rate. The proposed closedform expression shows that the achievable rate is an explicit expression of the number of transmit antennas. In simulation results, the impact of the number of transmit antennas on the achievable rate performance is investigated.

Deep Learning-Based Multipath DoAs Estimation Method for mmWave Massive MIMO Systems in Low SNR

To realize the direction-of-arrivals (DoAs) estimation without the prior information about the multipath number, a novel method using the deep learning is introduced for the millimeter (mmWave) massive multiple-input and multiple-output (MIMO) systems. In particular, the DoAs estimation is decomposed into three sub-problems, which are solved by the corresponding convolutional neural network (CNN), respectively. The estimation of multipath number is achieved by a multi-label classification model using the proposed CNN-I. Then, the proposed CNN-II is introduced for the DoA estimation of the line-of-sight (LOS) path. Based on the predicted multipath number obtained by the CNN-I, a regression model using the proposed CNN-III is applied for the DoAs estimation of non-line-of-sight (NLOS) paths. The proposed DoAs estimation method is implemented by learning the non-linear relationship between the sample covariance matrix of the received signal and the angles, thus reconstructing the mapping model. A series of results validate the superiority of the proposed DoAs estimation method in low signal-to-noise-ratio (SNR) regimes. The CNN-I is capable of achieving the good multipath number estimation performance across a range of SNRs. The classification model based on the proposed CNN-II and the regression model using the proposed CNN-III achieve the lower DoAs estimation root mean square error (RMSE) compared with some deep learning-based methods, estimation of signal parameters via rotational invariance techniques (ESPRIT) and Root-MUltiple SIgnal Classification (Root-MUSIC) methods. Remarkably, the proposed method using the CNN-II exhibits the good robustness in the DoA estimation of the LOS path. Furthermore, the low DoAs estimation RMSE is also achieved in the uniform planar array.

Contact-Free Multitarget Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis

Wireless-based human activity recognition has become an essential technology that enables contact-free human-machine and human-environment interactions. In this paper, we consider contact-free multi-target tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6 GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets’ locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density (GM-PHD) filter for tracking. A multi-pedestrian tracking experiment is conducted in a room with a size of 6.5 m×10 m to evaluate the performance of the proposed algorithm. According to experimental results, we achieve 75th and 95th percentile accuracy of 12.7 cm and 18.2 cm for single-person tracking and 28.9 cm and 45.7 cm for multi-person tracking, respectively. Furthermore, the proposed algorithm achieves tracking purposes in real-time, which is promising for practical MTT use cases.

Triband Antenna Array for FR1/FR2 5G NR Base Stations

This paper presents the concept and development of a novel tri-band base station antenna array for the two 5G New Radio (5G NR) frequency ranges, i.e. FR1 and FR2. The proposed array combines three different dipole-shaped antenna arrays, one designed for FR2 and two for FR1 standardized bands, aiming sectorial coverage, multiple-input and multiple-output (MIMO) and massive MIMO (mMIMO) applications. Particularly, a total of 98 radiating elements implies in 80, 16 and 02 antenna array elements for the 26 GHz band (N258 channel), 3.5 GHz band (N77 and N78 channels) and sub-GHz band (N20 and N28 channels), respectively. Experimental results, in accordance to ANSYS HFSS numerical simulations, demonstrate dual-polarization feature and the following bandwidths: from 0.65 to 0.88 GHz; from 2.76 to 4.2 GHz; from 24.9 to 27.8 GHz. Regarding the array radiation pattern and MIMO figures of merit, the coupling among the array adjacent elements is kept lower than -20 dB and envelope correlation coefficient (ECC) lower than 0.005. Furthermore, all array elements provide beamwidth of approximately 60°, cross-polarization lower than -20 dB, and gain varying from 7 to 9.7 dBi over the 5G bands.

SVM-Based Optimization on the Number of Data Streams for Massive MIMO Systems

For millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) systems, hardware cost and power consumption can be reduced via hybrid precoding, while keeping almost the same spectral efficiency (SE) as the fully digital precoding scheme. However, for different hardware configuration and channel environments, system SE varies with the number of transmitted data streams at the transmitter, and hence this should be determined primarily before hybrid precoding design. In this article, for single-user massive MIMO systems, a regression model based on the support vector machine technique is proposed, through which the optimal number of data streams can be obtained automatically based on the propagation characteristics of wireless channels, the number of antennas, and RF chains. To validate the correctness of the proposed model, simulation results are presented under various parameter settings.

Generalized Spatial Modulation Aided mmWave Massive MIMO Systems With Switch-and-Inverter Hybrid Precoding Design

This article concentrates on the generalized spatial modulation (GenSM) aided millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) switch-and-inverter (SI) based hybrid precoding design technology. As the technological advancements, the great demands for the scales of antenna arrays and frequency bands adopted in mobile communications become imperative. The essential radio frequency (RF) chains and phase shifters (PSs) turn into the principal causes of excessive energy dissipation in the massive MIMO mmWave systems. Accordingly, a complexity-efficient SI-based network is being substituted for a massive volume of energy-consuming high-resolution PSs in the analog RF precoder. As a consequence, the need of hardware complexity and energy consumption is alleviated significantly and thus results in superior energy efficiency (EE) performance. In addition, an innovative integration of the GenSM and the SI-based hybrid precoder is exploited as well in the article. The updated version of the coordinate update algorithm is developed and utilized to the analog precoding design. Furthermore, the spatial multiplexing gain acquired by the extra space-domain information of the GenSM mechanism improves substantially the spectral efficiency of the system. Finally, simulation results demonstrate remarkable performances on both the achievable sum-rate and the EE.

Enhancing NOMA’s Spectrum Efficiency in a 5G Network through Cooperative Spectrum Sharing

Non-orthogonal multiple access (NOMA) is one of the most effective techniques for meeting the spectrum efficiency (SE) requirements of 5G and beyond networks. This paper presents two novel methods for improving the SE of the downlink (DL) NOMA power domain (PD) integrated with a cooperative cognitive radio network (CCRN) in a 5G network using single-input and single-output (SISO), multiple-input and multiple-output (MIMO), and massive MIMO (M-MIMO) in the same network and in a single cell. In the first method, NOMA users compete for free channels in a competing channel (C-CH) on the CCRN. The second method provides NOMA users with a dedicated channel (D-CH) with high priority. The proposed methods are evaluated using the Matlab software program using the three scenarios with different distances, power location coefficients, and transmitting power. Four users are assumed to operate on 80 MHz bandwidths (BWs) and use the quadrature phase shift keying (QPSK) modulation technique in all three scenarios. Successive interference cancellation (SIC) and unstable channel conditions are also considered when evaluating the performance of the proposed system under the assumption of frequency selective Rayleigh fading. The best four-user SE performance obtained by user U4 was 3.9 bps/Hz/cell for SISO DL NOMA, 5.1 bps/Hz/cell for SISO DL NOMA with CCRN with C-CH, and 7.2 bps/Hz/cell for SISO DL NOMA with CCRN with D-CH at 40 dBm transmit power. While 64 × 64 MIMO DL NOMA improved SE performance of the best-use U4 by 51%, 64 × 64 MIMO DL NOMA with C-CH CCRN enhanced SE performance by 64%, and 64 × 64 MIMO DL NOMA with D-CH CCRN boosted performance by 65% SE compared to SISO DL NOMA at 40 dB transmit power. While 128 × 128 M-MIMO DL NOMA improved SE performance for the best U4 user by 79%, 128 × 128 M-MIMO DL NOMA with C-CH CCRN boosted SE performance by 85%, and 128 × 128 M-MIMO DL NOMA with D-CH CCRN enhanced SE performance by 86% when compared to SISO DL NOMA SE performance at 40 dB transmit power. We discovered that the second proposed method, when using D-CH with CCR-NOMA, produced the best SE performance for users. On the other hand, the spectral efficiency is significantly increased when applying MIMO and M-MIMO techniques.

A low profile, dual‐polarised, high gain patch antenna

AbstractA low‐profile patch antenna with dual‐polarisation and high gain is proposed in this study. It contains a primary radiator, a secondary radiator, and a planar feed network. The primary and secondary radiators are square patches placed 5 mm from each other. Two planar feed networks are electrically connected with the primary radiator through the stepped impedance transformer, providing equal amplitude and 180° phase shift signals. These two feed networks are settled in different layers to weaken the coupling effect. The resonance generated by the primary radiator is merged with the one by the secondary radiator, covering the 3.3–3.6 GHz band. Benefiting from the differential feed method, antenna specifications such as port isolation and cross‐polarisation discrimination (XPD) are ideal. Moreover, as the primary radiator is fed by planar microstrip, the overall height can be reduced, and the gain can be enhanced. Finally, a 2 × 2 subarray is investigated. Desirable results show that the proposed antenna is suitable for application in Massive Multiple Input and Multiple Output (MIMO) systems.

Proportional fairness secrecy beamforming for massive MIMO-SWIPT systems with low-resolution ADCs

Simultaneous wireless information and power transfer (SWIPT) technology is one of the solutions to the energy shortage problem in Massive multiple-input and multiple-output (MIMO) systems, while the low-resolution analog-to-digital convertor (ADC) is a potential way to significantly reduce the power consumption of radio frequency circuits in Massive MIMO enabled SWIPT systems. In this paper, we focus on the secrecy transmission maximization problem for Massive MIMO-SWIPT systems over Nakagami-m fading channels, where a low-resolution ADC quantization model and a non-linear energy harvester are exploited. By considering proportional fairness, a joint beamforming design and power allocation problem are proposed to achieve the sum logarithmic secrecy rates. To tackle the non-convex maximization problem, the suboptimal solutions of the beamforming design and power allocation are respectively obtained based on a successive convex approximation and a semidefinite relaxation. Numerical results demonstrate the effectiveness of the proposed joint optimization scheme.

Quantum Genetic Algorithm for Highly Constrained Optimization Problems

Quantum computing appears as an alternative solution for solving computationally intractable problems. This paper presents a new constrained quantum genetic algorithm designed specifically for identifying the extreme value of a highly constrained optimization problem, where the search space size _database is massive and unsorted_ cannot be handled by the currently available classical or quantum processor, called the highly constrained quantum genetic algorithm (HCQGA). To validate the efficiency of the suggested quantum method, maximizing the energy efficiency with respect to the target user bit rate of an uplink multi-cell massive multiple-input and multiple- output (MIMO) system is considered as an application. Simulation results demonstrate that the proposed HCQGA converges rapidly to the optimum solution compared with its classical benchmark.

Performance Analysis and Bit Allocation of Cell-Free Massive MIMO Network With Variable-Resolution ADCs

This paper concentrates on cell-free massive multiple-input and multiple-output (MIMO) network with variable-resolution analog-to-digital converters (ADCs). In such an architecture, all ADCs equipping at any access point (AP) can use arbitrary bit resolution to realize adaptive quantization and reduce power consumption. Under this circumstance, we first introduce a quantization-aware channel estimator based on linear minimum mean-square error (LMMSE) theory. On this basis, intra-AP and inter-AP bit allocation problems are investigated to maximize channel estimation quality subject to the total number of quantization bits. By leveraging the statistical characteristics of the estimated channels and estimation errors, we then derive the theoretical expressions of the achievable uplink spectral efficiency (SE) for maximal ratio combining (MRC) and minimum mean-square error (MMSE) combining, respectively. Furthermore, to maximize the sum SE under the constraint of total ADC quantization bits, we also investigate intra-AP and inter-AP bit allocation problems for both single-user and multi-user scenarios. Finally, simulation results confirm that our theoretical analyses are correct and accurate. In addition, we resort to numerical results to achieve some new insights and verify the advantages and conclusions pertinent to the proposed bit allocation techniques.

Performance Analysis and Optimization of Multicell Massive MIMO With Variable-Resolution ADCs Over Correlated Rayleigh Fading Channels

This article makes performance analysis and optimization for multicell massive multiple-input and multiple-output (MIMO) systems with variable-resolution analog-to-digital converters (ADCs). In such an architecture, every ADC uses arbitrary quantization resolution to save power and hardware cost. Along this direction, we first introduce a quantization-aware channel estimator based on linear minimum mean-squared error (LMMSE) estimate theory over spatially correlated Rayleigh fading channels. By leveraging on the estimated channel state information (CSI), we derive the theoretical expressions of the achievable uplink spectral efficiency (SE) for maximal ratio combining (MRC), quantization-aware multicell minimum mean-squared error (QA-M-MMSE) combining, and quantization-aware single-cell MMSE (QA-S-MMSE) combining, respectively. We consider the impacts of quantization errors and resort to random matrix theory to derive theoretical results. Afterwards, power and bit allocation problems are investigated under the variable-resolution architecture. Finally, simulation results demonstrate that our theoretical analyses are correct and that the proposed quantization-aware estimator and combiners are more beneficial than their quantization-unaware counterparts. Moreover, simulation results also verify the conclusions of the proposed power and bit allocation schemes.

Millimeter Wave Massive MIMO Heterogeneous Networks Using Fuzzy-Based Deep Convolutional Neural Network (FDCNN)

Enabling high mobility applications in millimeter wave (mmWave) based systems opens up a slew of new possibilities, including vehicle communications in addition to wireless virtual/augmented reality. The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links. In this research work, the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated. The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output (MIMO) which is utilized in a hyperdense environment called heterogeneous networks (HetNet). The optimization problem which arises while maximizing the Mean Opinion Score (MOS) is analyzed along with the QoE(Quality of Experience) metric by considering the Base Station(BS) powers in addition to the needed Quality of Service (QoS). Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic nature. Hence a deep reinforcement learning framework is developed for tackling the same optimization problem. In this work, a Fuzzy-based Deep Convolutional Neural Network (FDCNN) is proposed in addition to a Deep Reinforcing Learning Framework (DRLF) for extracting the features of highly correlated data. The investigational results prove that the proposed method yields the highest satisfaction to the user by increasing the number of antennas in addition with the small-scale antennas at the base stations. The proposed work outperforms in terms of MOS with multiple antennas.

Principal Component Tracking for Massive MIMO Channels in High Mobility Scenarios With Diagonal Step Size Matrix

The present article proposes a novel adaptive algorithm with an optimal diagonal step size matrix for multi-dimensional channel principal component tracking in two dimensional massive multiple-input and multiple-output (M-MIMO) systems. First, we prove that the weighted subspace algorithm globally converges to the stationary stochastic process’ major eigenvectors. Then, using the maximum likelihood criterion, we optimize the weight coefficient matrix and derive the convergent condition for the step size range in order to maintain the algorithm stability. To accelerate the convergence, we initially suggest the diagonal step size matrix for multi-dimensional eigenvector tracking. Simultaneously, an optimal diagonal step size matrix is derived, which not only accelerates the convergence speed distinctively but also improves the tracking of multi-dimensional eigenvectors. Moreover, the transient behavior during the adaptation process is investigated in a straight-forward way and the relationship between the convergence time constant and the eigenvalues of the received signals is uncovered. Finally, simulations reveal that the proposed approach outperforms established algorithms such as Oja <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\prime}\text{s}$ </tex-math></inline-formula> , Delmas and gradient descent algorithms. This approach establishes a sound foundation for tracking channel state information in M-MIMO systems with great mobility.

Improved Hierarchical Codebook-Based Channel Estimation for mmWave Massive MIMO Systems

A novel approach is proposed to improve channel estimation accuracy in hierarchical codebook-based beam search for millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) transmissions with channels that are accessible only through analog beamforming. Our approach is targeted at overcoming the resolution limit of directional angle estimation. In the proposed method, the data transmission beam is determined based on either two or three candidate codewords in the bottom layer and their beamforming gains. Design of the optimal beam is derived by exploiting the analytic structure of codewords in the bottom layer of the hierarchical codebook. Compared to conventional schemes used to increase the angle estimation resolution, the proposed scheme presents a higher achievable data rate in practical signal-to-noise ratio (SNR) ranges as well as superior channel estimation accuracy. This is verified through simulation results. The proposed method requires fewer codewords to be probed for beam training compared to the conventional methods, which reduces complexity.

Omnidirectional precoding for massive MIMO with uniform rectangular array in presence of mutual coupling

The massive Multiple-Input and Multiple-Output (MIMO) system is one of the technologies in 6-Generation (6G) communication networks. Omnidirectional or broad coverage precoding is used in massive MIMO to broadcast the common signals from public channels to all active and non-active users so that the radiation power of these signals is constant in all spatial directions. On the other hand, in large-scale array antennas, the presence of mutual coupling leads to deteriorating the performance of MIMO systems. In this paper, we propose omnidirectional precoding of the massive MIMO equipped with a Uniform Rectangular Array (URA) in presence of the mutual coupling. In the proposed method, omnidirectional transmission and maximum achievable ergodic rate conditions are developed for the case that mutual coupling exists, then an omnidirectional precoding matrix is designed to satisfy three constraints: 1) Omnidirectional transmission, 2) Equal average power transmission per antenna, and 3) Maximum achievable ergodic rate. For this purpose, we show that an omnidirectional matrix can be reconstructed using a pair of vectors with elements' absolute value equal to one that forms a Complementary Set (CS), and the Complete Complementary Codes (CCC) that satisfied some conditions. The intended CS is selected from the Golay sequence and the intended CCC is obtained by solving a gradient algorithm with projection on the Grassmann and Stiefel manifolds. Simulation results confirm the expected results.

A Family of Hybrid Precoding Schemes for Millimeter-Wave Massive MIMO Systems

For millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) systems, hybrid precoding/combining is a promising way to reduce hardware cost and power consumption by decreasing the number of radio-frequency (RF) chains without causing a significant spectral-efficiency loss. Most of the existing hybrid precoding schemes involve iterative searching or alternating optimization, so their computational complexity is high and they do not take advantage of parallel computing to improve the real-time performance. In this article, considering the scenarios where the number of data streams equals to the number of RF chains, we propose a family of hybrid precoding schemes by leveraging the equivalent channel and singular value decomposition for a better tradeoff between spectral efficiency and computational complexity. Simulation results with different system settings show that our proposed schemes are superior to other existing schemes in terms of system spectral efficiency.