Massive Massive Multiple-input And Multiple-output Research Articles

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.

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.

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.

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.

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 Computational Offloading in Massive MIMO-Enabled Next-Generation Mobile Edge Computing

Next-generation wireless communication networks are expected to support massive connectivity with high data rate, low power consumption, and computational latency. However, it can significantly enhance the existing network complexity, which results in high latency. To ease this situation, mobile edge cloud and massive multiple input and multiple output (MIMO) have recently emerged as the effective solutions. Mobile edge cloud has the ability to overcome the constraints of low power and finite computational resources in next-generation communication systems by allowing devices to offload their extensive computation to maximize the computation rate. On the other hand, MIMO can enhance network spectral efficiency by using large number of antenna elements. The integration of mobile edge cloud with massive MIMO also helps to increase the energy efficiency of the devices; as a result, more bits are computed with minimal energy consumption. In this work, a mathematical model is formulated by considering the devices’ energy constraint, which is nonconvex in nature. Following that, to overcome this, we transformed the original optimization problem using the first approximation method and solved the partial offloading schemes. Results reveal that the proposed scheme outperforms the others by considering computational rate as a performance matrix.

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.

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.

MMwave MIMO in 5G Network Analysis for Spectral Efficiency with Beamforming Based Channel Estimation

5G network has its high energy efficiency and spectrum efficiency, massive multiple-input and multiple-output (MIMO) has been envisioned as a key technology.This research work is centred on optimal method creation of energy-efficient massive MIMO methods, which is most active research technology in the communication industry.The suggested model, which takes into account a multi-cell model scenario, is a realistic method that improved spectral efficiency (SE) of huge MIMO methods.Base stations (BSs) do channel estimate based on uplink (UL) transmission using least-square (LS), element-wise MMSE, and minimum mean-squared error (MMSE) estimators.This research propose novel technique in MMwaveMIMO 5G network based spectral efficiency and channel estimation. The aim of this research is to enhance the spectral efficiency of MIMO channel using HetNets zero forcing Multiuser propagation models. The channel estimation is carried out based on beamforming using matched filter channel estimation with wide band antenna.Finally, simulation results demonstrate the high channel estimate accuracy and spectrum efficiency that the suggested systems can accomplish.Proposed technique attained sum rate of 85%, spectral efficiency of 93%, DoF of 79%, energy efficiency of 98% and detection accuracy of 96% for number of cells and sum rate of 77%, spectral efficiency of 85%, DoF of 71%, energy efficiency of 92% and detection accuracy of 95% for number of users.

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.

A low‐power‐consumption massive MIMO scheme based on multibeam antenna array

AbstractIn this paper, a low‐power‐consumption massive multiple‐input and multiple‐output (MIMO) scheme with an integrated multibeam antenna array is proposed for the green fifth‐generation (5G) communication base stations. The referenced antenna array includes 8 × 16 dual‐polarized antennas with Butler matrix networks, leading to low radiofrequency (RF) complexity and low consumption. The measured results demonstrate that the antenna array achieves four beam clusters with a 45° coverage in elevated plane, with gain of 15.4 dBi at 3.6 GHz. Based on this, the generated beam clusters and corresponding RF chains can be switched independently according to the real‐time user scenario. It is analyzed that the consumption for both RF chains and computation can be reduced with a similar channel capacity compared with the conventional phased array. With the merits of low complexity and low consumption, the proposed massive MIMO scheme exhibits promising usage for green 5G base station applications.

Codebook Design for Vertical Beamforming by Multi-Cell Coordination

Fifth-generation (5G) mobile communication systems employ beamforming technology using massive multiple-input and multiple-output (MIMO) to improve the reception quality and spectrum efficiency within a cell. Meanwhile, coordinated beamforming among multiple base stations is an effective approach to improving the spectrum efficiency at the cell edges, in which massive MIMO is deployed at geographically distant base stations and beamforming control is conducted in a cooperative manner. Codebook-based beamforming is a method for realizing multi-cell coordinated beamforming, in which each base station selects one of multiple beams that are predefined in a codebook. In codebook-based beamforming, it is important to design an efficient codebook that takes into account the beam allocation and the number of beams. In general, the larger the number of beams defined in a codebook, the more finely tuned the beam control can be and a greater improvement in spectrum efficiency can be expected. However, it requires a huge signal processing to optimize the beam combinations with a large number of beams by coordinated beamforming. This paper proposes a novel codebook design that efficiently assigns beam directions and widths in a vertical plane. Computer simulations showed that the proposed codebook performs as well as the conventional method while requiring fewer beam combinations.

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.