Massive Multiple-input And Multiple-output Systems Research Articles

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.

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.

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.

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.

On Performance Loss of DOA Measurement Using Massive MIMO Receiver With Mixed-ADCs

High hardware cost and high power consumption of massive multiple-input and multiple output (MIMO) are two challenges for the future wireless communications including sixth generation (6G). Adopting the low-resolution analog-to-digital converter (ADC) is viewed as a promising solution. Additionally, the direction of arrival (DOA) estimation is an indispensable technology for beam alignment and tracking in massive MIMO systems. Thus, in this letter, the performance of DOA estimation with mixed-ADC structure is firstly investigated. The Cramér-Rao lower bound (CRLB) for this architecture is derived based on the additive quantization noise model. Eventually, a performance loss factor and the associated energy efficiency factor is defined for analysis in detail. Simulation results show that the mixed-ADC architecture can strike a good balance among performance loss, circuit cost and energy efficiency. More importantly, just a few bits (up to 4 bits) of low-resolution ADCs can achieve a satisfactory performance for DOA measurement.

Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple‐input and multiple‐output system

AbstractThe core requirements are generated for sixth generation (6G) wireless communication with low‐latency and ultra‐high speeds to increase the count of ultra scale intelligent factors, like smart cars, mobile root users. The advancement of 6G communication can lead the interference exploitation. To manage the exploitation of uplink multiuser massive (UMM), the multiple‐input and multiple‐output (MIMO) is very difficult to detect the mechanisms, particularly, quadrature amplitude modulation (QAM) signals. To overcome these issues, a novel deep graph neural network optimized with fertile field algorithm based detection model (DGNNO‐FFA) is proposed in this article for uplink multiuser massive MIMO System. The proposed DGNNO‐FFA approach minimizes the channel estimation errors under low signal to noise ratio (SNR) with better bit error rate (BER). Finally, the proposed DGNNO‐FFA approach attains 11.02%, 12.22%, and 25.27% lower BER value, 14.55%, 18.66%, and 29.49% higher energy efficiency, 15.59%, 19.06%, and 29.59% lower NMSE, and 15.59%, 19.06%, and 29.59% lower computational complexity compared with other existing approaches, like deep neural network based semi definite relaxation (DNN‐SDR), QR based zero forcing algorithms (QR‐ZF), and QAM based 2‐dimensional double successive projection model (QAM‐2D‐DSP).

Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications.

Exponential growth in data rate demands has driven efforts to develop novel beamforming techniques for realizing massive multiple-input and multiple-output (MIMO) systems in sixth-generation (6G) terabits per second wireless communications. Existing beamforming techniques rely on conventional optimization algorithms that are too computationally expensive for real-time applications and require complex digital processing yet to be achieved for phased array antennas at terahertz frequencies. Here, we develop an intelligent and self-adaptive beamforming scheme enabled by deep reinforcement learning, which can predict the spatial phase profiles required to produce arbitrary desired radiation patterns in real-time. Our deep learning model adaptively trains an artificial neural network in real-time by comparing the input and predicted intensity patterns via automatic differentiation of the phase-to-intensity function. As a proof of concept, we experimentally demonstrate two-dimensional beamforming by spatially modulating broadband terahertz waves using silicon metasurfaces designed with the aid of the deep learning model. Our work offers an efficient and robust deep learning model for real-time self-adaptive beamforming to enable multi-user massive MIMO systems for 6G terahertz wireless communications, as well as intelligent metasurfaces for other terahertz applications in imaging and sensing.

Performance Evaluation of Spectral Efficiency for Uplink and Downlink Multi-Cell Massive MIMO Systems

Massive multiple-input and multiple-output (MIMO) systems have become the most persuasive technology for 5G as it increased the energy efficiency gigantically as compared to other wireless communication systems. Being the most vibrant research technology in the communication sector, this research work is based on the optimal model development of energy-efficient massive MIMO systems. The proposed model is a realistic model that augmented the spectral efficiency (SE) of massive MIMO systems where a multi-cell model scenario is considered. Channel estimation is carried out at the base stations (BSs) based on uplink (UL) transmission while the minimum mean-squared error (MMSE), Element-wise MMSE, and Least-square (LS) estimators are used for the estimation. We analyze the achievable SE of the UL based on the MMSE channel estimator with different receive combining schemes. Moreover, the downlink (DL) transmission model is also modelled with different precoding schemes by taking the same vectors used in combining schemes. The simulation results show a significant improvement in spectral efficiency by developing UL and DL transmission models and also realized that the average sum of SE per cell can be improved by optimized MMSE channel estimation, installing multiple BS antennas, and serving multiple UEs per cell. The findings of this work specify that the massive MIMO system can be developed by optimizing the channel estimation for the augmentation of SE in UL and DL transmissions. Conclusively, it can be summarized that some complex computations of MMSE channel estimators can enhance the average sum of SE per cell as per the results verified in this model.

Performance Analysis on Cell-Free Massive MIMO With Capacity-Constrained Fronthauls and Variable-Resolution ADCs

In the recently proposed cell-free massive multiple-input and multiple-output (MIMO) networks, the capacity of fronthaul links connecting all access points (APs) and a central processing unit (CPU) is limited. In this context, taking into consideration the spatial channel correlation at the APs, this article investigates the performance of cell-free massive MIMO systems with variable-resolution quantization, i.e., each analog-to-digital converter at the APs and quantizer at the CPU use arbitrary bits for quantization. Specifically, we first introduce a technique based on linear minimum mean-square to perform channel estimation. On this basis, we then derive the closed-form expressions of achievable rates over spatially correlated Rayleigh fading channels for both uplink and downlink if maximal ratio combining and maximal ratio transmission are used at the CPU. Finally, simulation results validate our theoretical analyses and corroborate that the performance of channel estimation and achievable rates reduces as the spatial correlation strengthens. Moreover, from a statistic perspective, under the constraint of the total number of quantization bits, it is preferable to assign more bits to the AP with larger aggregated large-scale fading coefficient and lower channel correlation.

Generalized 3-D Spatial Scattering Modulation

Three-dimensional (3-D) massive multiple-input-and-multiple-output (MIMO) systems, which explore degrees of freedom in both the vertical and the horizontal dimensions, are a promising technology to enhance spectral efficiency in the next-generation communication systems. As an emerging modulation technology with millimetre wave (mm-wave) communication in massive MIMO systems with limited radio-frequency (RF) chains, the spatial scattering modulation (SSM) makes use of beamspace domain resources to further improve the spectral efficiency. However, most published works on the SSM only focus on two-dimensional (2-D) MIMO systems. In this paper, we generalize the SSM system to 3-D space. First, we design a novel generalized 3-D SSM system by considering both vertical and horizontal angles to determine scattering paths, which are selected to convey information bits. Then, we propose a whitening filter based optimal detection algorithm to detect the received symbols with correlated noise, where the correlation is generated by the combination of the received signals from the large-scale planar receiving antenna array. Next, we design a 2-D fast Fourier transform (FFT) based transceiving approach to improve the hardware friendliness. After that, we propose a low-complexity detector based on the linear minimum mean square error (MMSE) detection algorithm. Moreover, we derive the union upper bound on average bit error probability (ABEP) in a closed form and the asymptotic performance expression for the generalized 3-D SSM system. Finally, we analyse the impact of transmission environment on the ABEP performance. Numerical results show that the generalized 3-D SSM system outperforms the conventional 2-D SSM system, which reduces the ABEP by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> with the same signal-to-noise ratio (SNR) level under the typical indoor environment.

QoE Driven VR 360° Video Massive MIMO Transmission

Massive multiple-input and multiple-output (MIMO) enables ultra-high throughput and low latency for tile-based adaptive virtual reality (VR) 360° video transmission in wireless network. In this paper, we consider a massive MIMO system where multiple users in a single-cell theater watch an identical VR 360° video. Based on tile prediction, base station (BS) deliveries the tiles in predicted field of view (FoV) to users. By introducing practical supplementary transmission for missing tiles and unacceptable VR sickness, we propose the first stable transmission scheme for VR video. we formulate an integer non-linear programming (INLP) problem to maximize users’ average quality of experience (QoE) score. Moreover, we derive the achievable spectral efficiency (SE) expression of predictive tile groups and the approximately achievable SE expression of missing tile groups, respectively. Analytically, the overall throughput is related to the number of tile groups and the length of pilot sequences. By exploiting the relationship between the structure of viewport tiles and SE expression, we propose a multi-lattice multi-stream grouping method aimed at improving the overall throughput for VR video transmission. Moreover, we analyze the relationship between QoE objective and number of predictive tile. We transform the original INLP problem into an integer linear programming problem by setting the predictive tiles groups as some constants. With variable relaxation and recovery, we obtain the optimal average QoE. Extensive simulation results validate that the proposed algorithm effectively improves QoE.

Partially-Connected Hybrid Beamforming for Spectral Efficiency Maximization via a Weighted MMSE Equivalence

Hybrid beamforming (HBF) is an attractive technology for practical massive multiple-input and multiple-output (MIMO) millimeter wave (mmWave) systems. Compared with the fully-connected HBF architecture, the partially-connected one can further reduce the hardware cost and power consumption. However, the special block diagonal structure of its analog beamforming matrix brings additional design challenges. In this paper, we develop effective HBF algorithms for spectral efficiency maximization (SEM) in wideband mmWave massive MIMO systems with the partially-connected architecture. One main contribution is that we prove the equivalence of the SEM problem and a weighted mean square error minimization (WMMSE) problem, which leads to a convenient algorithmic approach to directly tackle the SEM problem. Specifically, we decompose the equivalent WMMSE problem into the hybrid precoding and hybrid combining subproblems, for which both the optimal digital precoder and combiner have closed-form solutions. For the more challenging analog precoder and combiner, we propose an element iteration based algorithm and a manifold optimization based algorithm. Finally, the hybrid precoder and combiner are alternatively updated. The overall HBF algorithms are proved to monotonously increase the spectral efficiency and converge. Furthermore, we also propose modified algorithms with reduced computational complexity and finite-resolution phase shifters. Simulation results demonstrate that the proposed HBF algorithms achieve significant performance gains over conventional algorithms.

Asymptotic Analysis and Beamforming Design for High Frequency Massive MIMO With a Compact Planar Array

Thanks to the short wavelength at high frequency (HF), it is promising to combine massive multiple-input and multiple-output (MIMO) with HF communications. In particular, HF massive MIMO differs from the conventional MIMO in array structure and radio propagation. In this paper, we investigate the HF massive MIMO system with a compact planar array (CPA) deployed at the base station (BS) in the presence of electromagnetic mutual coupling (MC) for the asymptotic case under unlimited antenna elements assumption. Specifically, the HF sparse channel is analyzed asymptotically to show that the randomness characteristic of channel decreases in the asymptotic regime, which is conducive to the design of communication systems. Moreover, on the basis of the asymptotic analysis results, the optimal beamforming to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) is derived by applying random matrix theory. Numerical results verify the accuracy of the asymptotic analysis, indicate the performance superiority of the proposed scheme to the traditional minimum mean squared error (MMSE) and maximum ratio transmission (MRT) schemes, and reveal the robustness of the proposal to electromagnetic MC.

α-Fairness-based joint power allocation and power splitting for massive MIMO systems with SWIPT

Fairness with a α parameter is an important consideration to provide uniform and high quality service for each user when designing a wireless communication system. In general, α-fairness problem includes the proportional fairness and the max-min fairness. In this paper, we consider the problem of α-fairness-based joint power allocation and power splitting for massive multiple-input and multiple-output (MIMO) systems with simultaneous wireless information and power transfer (SWIPT). Different from the existing works, we utilize a low-complexity parametric optimization method to solve the non-convex proportional fairness problem. Furthermore, we develop a joint iterative algorithm to solve the general α-fairness problem and obtain the suboptimal solutions on power allocation and power splitting. Simulation results illustrate the performance of the proposed algorithm.

A Modified OMP Algorithm with Reduced Feedback Overhead for Massive MIMO System

Objectives: Massive Multiple-Input and Multiple-Output (MIMO) is the optimum way to enhance the bandwidth issue, in which the feedback overhead is a challenging concern when tested with Frequency Division Duplex (FDD) systems. Conventional greedy search algorithms like Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP) are based on the prior knowledge of the signal sparsity, which enhances overhead. So, the objective of this study is to develop a method with low complexity channel estimation and reduced feedback overhead. Methods: The proposed method is based on the residual vector which is updated after iteration and a threshold-based approach is utilized to decide the stopping criteria thereby controlling the number of iterations. The evaluation parameters used in the experimental analysis are Normalized Mean Square Error and channel capacity. Findings: The proposed method improves the effectiveness in signal recovery and reconstruction along with signal sparsity it also reduces the complexity of the estimation process and limits the feedback. This results in a high data rate and can be used in a massive MIMO system to optimize the channel estimation. Novelty : The proposed model is independent of the sparsity in the input vector and improves the effectiveness of the signal recovery and reduces the feedback burden. The proposed method is suitable for a 5G massive MIMO systems. Keywords: Feedback overhead; CSI; OMP; Residue vector

Combined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems

In this paper, a novel iterative detection technique that combines deep learning (DL) and the approximated algorithm of successive over relaxation (SOR) is proposed to achieve high reliability and reduce the computational complexity. Recently, as the demanded data rates increase, the massive multiple-input and multiple-output (MIMO) system has drawn attention in wireless communication. In massive MIMO, the implementation of traditional detectors for high reliability has become impractical, and the reduction for the complexity of detectors has emerged as a practical implementation challenge. The existing DL-based detection technique of orthogonal approximate message passing network (OAMPNet) can provide high detection performance. However, the computational complexity is too high for the implementation in massive MIMO systems. The proposed detection technique uses SOR algorithm to reduce the computational complexity, and the relaxation parameter of SOR is adaptively determined by a learning algorithm. A non-linear estimator using the DL algorithm is combined with the SOR algorithm to achieve high reliability, and regardless of the size of the MIMO system, only the size of the DL architecture determines the complexity of the non-linear estimator. Simulation results show that the proposed detector outperforms the conventional linear detector based on minimum mean square error (MMSE) and achieves high reliability with lower complexity than OAMPNet in various channel environments with spatial correlation.

Indoor Direct Positioning With Imperfect Massive MIMO Array Using Measured Near-Field Channels

In this article, we focus on indoor direct positioning using a practical massive multiple-input and multiple-output (MIMO) array with imperfections. First, the imperfect array needs to be calibrated in an anechoic chamber from the view of array signal processing. This brings inconveniences due to the large size of massive MIMO systems. To solve this problem, we propose onsite calibration, where a banded calibration matrix, calibrating both phase-gain error and mutual coupling, can be estimated by performing least-squares on the measure near-field channels, i.e., the site survey data. The feasibility of this calibration method can be explained by that the non-line-of-sight (NLOS) channel is able to be modeled using Rayleigh fading; i.e., the sum of the multipath components can be treated as Gaussian noise. Then, to estimate the user position, we propose a new two-stage direct positioning scheme. In the first stage, the coarse position is obtained via global search-based conventional beamforming (CBF) using a global calibration matrix. In the second stage, fine positioning is achieved via local search-based multiple signal classification (MUSIC) using a local calibration matrix. The proposed method is computationally efficient, insensitive to model order selection, free of dense site survey, and robust to partial line-of-sight (LOS) blocking. Finally, the proposed method is verified by practical measurements from a distributed array (DA), uniform linear array (ULA), and uniform rectangular array (URA), and compared with a deep learning positioning-method. The results show that the DA with the proposed method performs the best, achieving sub-centimeter accuracy (typically below 5 mm).

Mean‐based reciprocity calibration in TDD massive MIMO system

Non-reciprocity limits the potential of time division duplexing (TDD) massive multiple-input and multiple-output (MIMO) systems. Due to the reciprocal channel, there is no need for downlink (DL) pilot transmission implies computational complexity reduction and saving precious time resources. In this study, the DL channel matrix is estimated using the uplink channel matrix by a novel reciprocity calibration technique. Non-reciprocity arises due to the hardware mismatch (HM) across the base station (BS) and the user terminal (UT)/both. Its detrimental effect causes amplitude and phase impairment in the downlink signal transmission. Since a massive MIMO network has a large number of antenna elements across the BS, hence the instantaneous HM coefficient cannot be determined accurately. For solving such a problem, the authors use the statistical response of the hardware unit. Considering a complex Gaussian hardware response across each antenna terminal, the probability density function (PDF) of amplitude and phase mismatch is derived. They also derive the joint PDF of amplitude and phase mismatch, considering the HM. Simulation results address the sensitivity and complexity for evaluating the sum-rate capacity under three linear precoders (matched filter, regularized zero-forcing, and zero-forcing), which validate their effectiveness in this scenario.

The Configurable Hybrid Precoding Processor for Bit-Stream-Based mmWave MIMO Systems

Beamforming technology plays an essential role in the promising millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) communications for fifth generation (5G) new radio system. Specifically, hybrid analog beamforming and digital precoding scheme can be employed to reduce the excessive radio frequency (RF) chains and data converters in the massive MIMO transceiver while still maintaining optimal spectral efficiency. However, traditional hybrid precoding architecture cannot be configured to support different system specifications, such as the number of bit streams or transmit antennas, leading to the limitation to hardware flexibility and efficiency. This article presents a configurable and low-complexity hybrid precoder based on the parallel data-stream processing in respect of system, algorithm, and architecture. The proposed algorithm was designed to avoid signal dependence between data streams so as to realize configurable precoding architecture. The performance and complexity of the proposed algorithm were also simulated and analyzed in detail in this article. Moreover, the hybrid precoding processor chip was designed and implemented based on the proposed algorithm. A novel data-processing flow was designed in the precoder so as to increase the hardware efficiency and configurability. The designed hybrid precoding chip can be configured to support one to four data streams for 16 × 16 mmWave MIMO systems. The designed precoder was implemented by using TSMC 40-nm CMOS technology. The normalized throughput achieved 11.1 M channel-matrices per second at a maximum clock frequency of 300 MHz. The area complexity is 263.5 kGE, and power consumption is 119.1 mW.