Massive Massive Multi-input Multi-output Research Articles

MGGAN-Based Hybrid Beamforming Design for Massive MIMO Systems Against Rank-Deficient Channels

The design of hybrid beamforming (HBF) is one of the key issues in millimeter wave (mmWave) and terahertz (THz) massive multi-input multi-output (MIMO) communications. In particular, only rank-deficient channel state information (CSI) can be acquired in hybrid MIMO architecture, which enhances design challenges. However, this practical problem of rank-deficient channels has been avoided by most of the existing studies on HBF. In this letter, we propose a new deep learning (DL) network model called Multi-Generator Generative Adversarial Network (MGGAN) to accomplish the design of HBF against rank-deficient channels. Specifically, MGGAN includes three generators for recovering the rank-deficient channels, achieving the analog and the digital beamforming matrices, respectively. In addition, a spectrum efficiency (SE) module is introduced to take system SE as the optimization goal of the network. Simulation results show the superiority of our proposed MGGAN model in SE performance over several DL network architectures and classical HBF algorithms. Moreover, it is more robust against the rank-deficiency of hybrid architecture MIMO channels.

Geometry-Based Stochastic Model and Statistical Characteristic Analysis of Cell-Free Massive MIMO Channels

Cell-free massive multi-input multi-output (MIMO) systems exhibit many characteristics that differ from those of traditional centralized massive MIMO systems, and there are still research gaps in the modeling of cell-free massive MIMO channels. In this paper, a geometry-based stochastic model (GBSM) that combines the double-ring model and hemisphere model to comprehensively consider the distribution of scatterers in the environment was proposed for cell-free massive MIMO channels. Combined with the line-of-sight (LoS) path, single scattering path, and double scattering path components, the channel matrix between the access points (APs) and the user was derived. The proposed model fully considers geometric parameters such as the arrival/departure direction, elevation angle, time delay, and distance, which can accurately characterize the channel. Then, we proved that the traditional channel model, standard block-fading model, and spatial basis expansion model (SBEM) adopted in a cell-free massive MIMO system could not describe the nonstationarity in the space, time, and frequency domains, whereas the proposed GBSM could. Statistical characteristics of the channel were analyzed, including the space cross-correlation function (CCF), time autocorrelation function (ACF), Doppler power spectral density (PSD), level crossing rate (LCR), and average fade duration (AFD). Then, we investigated the proposed model by simulating the space CCF, time ACF, Doppler PSD, LCR, and AFD under the conditions of two different scatterer densities. Through simulations and analyses, some new features of cell-free massive MIMO channels were identified, providing a theoretical basis for in-depth research on cell-free massive MIMO systems. Finally, the measurement-based scenario and the WINNER II channel model are compared to demonstrate that the GBSM is more practical to characterize real cell-free massive MIMO channels.

Compressive Channel Estimation Based on the Deep Denoising Network in an IRS-Enhanced Massive MIMO System.

Integrating large intelligent reflecting surfaces (IRS) into a millimeter-wave (mmWave) massive multi-input-multi-output (MIMO) technique has been a promising approach to enhance the performance of the wireless communication system with the channel state information (CSI). Most existing work assume that ideal channel estimation can be obtained, but the proposed high-dimensional cascaded MIMO channels and passive reflectors pose a great challenge to these methods. To address the abovementioned problems, we proposed a new method for the reduction of training overhead in IRS with a partial ON/OFF model and an optimizing strategy for pilot design approach. The energy consumption of large-scale antenna arrays and the pilot overhead in the training phase of signal transmission are greatly reduced. Besides, we proposed an improved deep residual shrinkage denoising network, which possesses better denoising performance with a soft thresholding model. The channel data can be denoised by deep learning methods, which greatly improve the accuracy of channel estimation. Simulation results demonstrate that the superiority of the proposed network over prior solutions.

Generalized Superimposed Training Scheme in Cell-Free Massive MIMO Systems

Regular pilots in a massive multi-input multi-output (MIMO) system with large number of users suffer from the pilot contamination effect due to limited training time. In this paper, for a cell-free massive MIMO system, we have proposed a generalized superimposed pilot (GSP) scheme, where the available number of pilots are equal to the coherence time slots, and the transmitting data symbols are spread over the coherence time with the help of simple precoding. Further, in order to keep the system scalable, a low complexity and distributed time processing approach is employed, and the corresponding rate components are analyzed. It is shown that with careful design of precoding matrix and number of data symbols, the GSP symbols can provide much better channel estimation and data detection performance, as compared to the regular pilot scheme and the conventional superimposed scheme. These results have been verified via simulations. It is also inferred that centralized processing in cell free system improves the data detection performance than localized processing. Iterative data detection at the central node also improves the MSE of data estimates. The pilot contamination effect, is significantly reduced due to availability of larger number of pilots, as compared to regular pilots transmission.

Multigroup Multicast Downlink Cell-Free Massive MIMO Systems With Multiantenna Users and Low-Resolution ADCs/DACs

In this article, the effects of multiantenna users and low-resolution analog-to-digital converters/digital-to-analog converters (ADCs/DACs) on multigroup multicast downlink cell-free massive multi-input multi-output (MIMO) systems are explored. Leveraging on the distributed conjugate beamforming receiver and the generalized additive quantization noise model, we first derive a closed-form expression for the achievable sum spectral efficiency (SE). The resultant expression enables us to precisely quantify the effects of key parameters, such as the transmit power of access points (APs), the numbers of groups, users’ antennas, APs’ antennas, and quantization bits. Due to the joint influence of channel hardening property, multiantenna gain, and intergroup interference, the research becomes sophisticated particularly. The result shows that when the sum-SE is small, the multicast is better than the unicast and vice versa. Typically, it reveals that the full-resolution DAC can be replaced by 5-bit low-resolution DAC to achieve the same performance, and the additional antenna at users can effectively enhance the system performance. Then, according to a generated energy cost model, the formula of the energy efficiency (EE) is obtained. When the DAC quantization is equal to 2-bit, it can achieve the maximum sum EE. Finally, the simulation results are provided to verify the derived results.

Domain selective two-stage beamforming in 3D massive MIMO

In this paper, a two-stage beamforming scheme called domain selective neighbor-based joint spatial division and multiplexing (DS-NJSDM) scheme is proposed to reduce the overhead of pilot training and uplink feedback in Frequency Division Duplexing (FDD) three-dimensional (3D) massive multi-input multi-output (MIMO) systems. Inspired by the Kronecker product structure of the 3D channel model, a DS pre-beamformer is proposed to sparsify the channel matrix, which selects horizontal or vertical pre-beamformer to mitigate the interference from non-neighbors. The signal space can be effectively utilized with the DS pre-beamformer, and the complexity of beamforming is significantly reduced. After the pre-beamformer, the zero entries of the downlink channel matrix are randomly distributed, making pilot design difficult. Then, a pilot allocation scheme is proposed to design the pilot matrix. Simulation results are provided to validate the superiority of the proposed scheme in enhancing the spectral efficiency over the existing schemes.

Generalized Superimposed Training Scheme in IRS-Assisted Cell-Free Massive MIMO Systems

In this article, for a cell-free massive multi-input multi-output (MIMO) system assisted with intelligent reflective surface (IRS) panels, a generalized superimposed pilot (GSP) training scheme is proposed, where the available number of pilots are equal to the coherence time slots, and the transmitting data symbols are spread over the coherence time with the help of simple precoding. Further, in order to keep the system scalable, a low complexity approach is employed for processing, and the corresponding rate components are analyzed. It is shown that with careful design of precoding and number of data symbols, the GSP symbols can provide much better channel estimation and data detection performance, as compared to the regular pilot scheme and the conventional superimposed scheme. These results, verified via simulations, shows that the centralized processing improves the data detection performance than localized processing. The pilot contamination effect, is significantly reduced due to the availability of larger number of pilots, as compared to the regular pilots transmission. For four IRS panels in the system, the proposed scheme is shown to reduce the channel estimation errors by 74% approximately.

Sub-6 GHz band massive MIMO antenna system for variable deployment scenarios in 5G base stations

A compact massive multi input multi output (MIMO) antenna system with 1 × 4 (sector) subarray configuration operating at sub-6 GHz range for 5th generation (5G) base stations has been designed and analysed in various geometries (rectangular, triangular, and hexagonal). The capacity of the system can be increased by more than 10 times whereas the energy efficiency can be increased 100 times using the massive MIMO system (Larsson et al. in IEEE Commun Mag 52(2):186–195, 2014). A maximum of 5 sectors has been used, with each sector comprising of 1 × 4 subarray elements. Each sector consists of three layers, in which 1 × 4 patches are located on its top layer whereas it’s feeding network and ground plane are placed at the bottom layer and middle layer respectively. The entire system can operate in two modes, individual port operation and massive MIMO array operation with beam steering capabilities. The measured bandwidth of the system is 140 MHz that covers the frequencies from 3.36 to 3.50 GHz in sub-6 GHz band. The overall dimension of a unit subarray in terms of length, width and height is 280.5 × 56.1 × 2 mm3. The gain of an individual port is found to be 12.77 dBi and the overall gain of a single panel with 5 sectors arranged in rectangular form is 19.73 dBi. Mutual coupling among all the ports has been kept less than − 16 dB. The operating frequency range of the antenna array system is chosen in the range of 3.3–3.8 GHz, as this band has been allocated and targeted across the globe to enable 5G in Sub-6 GHz band.

Robust Linear Precoder Design for 3D Massive MIMO Downlink With A Posteriori Channel Model

In this paper, we investigate the robust linear precoder design for three dimensional (3D) massive multi-input multi-output (MIMO) downlink with uniform planar array (UPA) and imperfect channel state information (CSI). In practical massive MIMO with UPAs, the number of antennas in each column or row is usually limited. The straightforward extension of the conventional DFT based beam domain channel model widely used in massive MIMO with uniform linear arrays (ULAs) can not apply. To overcome this issue, we establish a new beam domain channel model by using sampled steering vectors. Then, a novel method to obtain the beam domain channel power matrices and the instantaneous beam domain channel coefficients is proposed, and an a posteriori beam domain channel model which includes the channel aging and the spatial correlation is established. On the basis of the a posteriori channel model, we consider the robust precoder design with the expected weighted sum-rate maximization under a total power constraint. By viewing the power constraint as a Riemannian manifold, we transform the constrained optimization problem into an unconstrained optimization problem on the Riemannian manifold. Then, we derive an iterative algorithm to obtain the optimal precoders by setting the Riemannian gradient of the objective function to zero. Furthermore, we propose a low complexity robust precoder design by replacing the expected rates in the objective function with their upper bounds. Simulation results show that the proposed precoders can achieve significant performance gain than the widely used regularized zero forcing (RZF) precoder and signal to leakage noise ratio (SLNR) precoder.

Low-Complexity Precoding by Exploiting Spatial Sparsity in Massive MIMO Systems

A massive multi-input-multi-output (MIMO) system can bring substantial improvement in spectral and energy efficiency for wireless communication systems. The high array gain and fine spatial resolution allow the utilization of relatively simple processing at the base station, such as the near-optimal regularized zero-forcing (RZF) precoding. Nevertheless, such precoding schemes require computing the inverse and multiplication of matrices, which leads to a large burden for baseband processing when the system dimension grows large. To take full advantage of the spatial sparsity of massive MIMO channels in the finite scattering environment, we propose a virtual channel precoding strategy that directly utilizes the sparse virtual channel representation (VCR) in precoding. Furthermore, we develop a virtual channel RZF precoding algorithm based on the pre-conditioned conjugate gradient method, namely PCG-VC-RZF, which can directly use the sparse virtual channel matrix while avoiding the complex computation of the Gram matrix. The computational complexity and transmission delay are shown to be reduced tremendously by the proposed algorithm when operating in a large-dimension system. We further provide a beam selection strategy for improving the performance of the proposed approach under a complexity constraint. The simulation results verify the efficiency of the proposed precoding scheme.

Learning-Based User Clustering in NOMA-Aided MIMO Networks With Spatially Correlated Channels

This paper considers the integration of non-orthogonal multiple access (NOMA) into massive multi-input multi-output (MIMO) systems for downlink transmission. We consider the joint design of user clustering, transmit beamforming, and power allocation to minimize the total transmit power while meeting the signal-to-interference-and-noise ratio targets. We decompose this challenging mixed-integer programming problem into three separate subproblems to solve. We propose a low-complexity learning-based user clustering algorithm, which is a modified version of mean shift clustering with a new channel correlation based clustering metric. The proposed clustering algorithm determines the clusters to trade-off between spatial dimension and power dimension offered by respective MIMO and NOMA for user multiplexing. We then design zero-forcing transmit beamformers to eliminate inter-cluster interference and optimize power allocation to minimize the total transmit power. We provide two case studies for both co-located and distributed massive MIMO systems in spatially highly correlated prorogation environments. Simulation results show that our proposed algorithm forms NOMA clusters based on the available degrees of freedom in the system to effectively use both spatial and power dimensions, which results in a substantial performance improvement over MIMO-only methods or other existing clustering methods in such environments.

Joint polar with physical layer network coding and massive MIMO: performance analysis

Large spectral efficiency, reliability, coverage, and energy efficiency are all major requirements to meet the targets of fifth-generation (5G) and beyond communication networks. The transmitted signal is usually susceptible to errors that reduced reliability and throughput of the system. Physical layer network coding (PLNC) is a promising technology to achieve better throughput, low latency, and high transmission rate. This paper considers the combination of PLNC with polar coding using massive multi-input multi-output (MIMO) system to enhance the transmission reliability by reducing the bit error rate (BER) and improve system reliability. This arrangement is investigated in two-way wireless relay transmission over millimetre wave (mmWave) band channel model. The results of the extensive simulation tests demonstrated improvements in throughput and BER achieved using polar code with PLNC and a sufficient number of antenna elements in the mMIMO system. The BER performance of polar-coded PLNC arrangement outperformed PLNC without coding for 128 and 256 receiving antenna elements in the relay node regardless the number of antenna elements in the user equipment side. The tests showed that the combination of the polar code with PLNC and MIMO system is not encouraging at low Signal-to-noise ratio (SNR).

Cell-Free Massive MIMO with Energy-Efficient Downlink Operation in Industrial IoT

Cell-free massive Multi-input Multi-output (MIMO) can offer higher spectral efficiency compared with cellular massive MIMO by providing services to users through the collaboration of distributed APs, and cell-free massive MIMO systems with distributed operations are attracting a great deal of industry attention due to their simplicity and ease of deployment. This paper aims to find an optimal solution for energy efficiency in the downlink operation in the Industrial Internet based on cell-free massive MIMO systems with distributed operations. A system model is proposed, and a theoretical analysis on energy efficiency is presented. The optimization problem of efficient downlink operation is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is further decomposed into two sub-problems, i.e., maximizing the sum-rate of the downlink transmission and optimizing the total energy consumption. The two sub-problems are addressed via AP selection and power allocation, respectively. The simulation results demonstrate that our algorithms can significantly improve the energy efficiency with low computational complexity in comparison with traditional distributed cell-free massive MIMO. Even in the presence of pilot contamination, the proposed algorithms can still provide significant energy efficiency when a large number of IoTDs are connected.

TransNet: Full Attention Network for CSI Feedback in FDD Massive MIMO System

Channel state information (CSI) is a key aspect of massive multi-input multi-output (MIMO) system. It depicts important properties of transmission channels such as scattering, fading, the attenuation of power with distance, etc. The quality and cost of CSI feedback between user equipment (UE) and base station (BS) play vital roles in the quality of the whole communication system. In this letter, a new deep learning (DL) method based on Google’s famous Transformer architecture is presented for CSI feedback in frequency division duplex (FDD) massive MIMO system. Simulation results show that the presented inception network named TransNet outperforms other DL methods on the quality of CSI feedback.

Pilot Allocation and Data Power Optimization Based on Access Point Selection in Cell-Free Massive MIMO

Focusing on the pilot contamination problem of cell-free massive multi-input multi-output (MIMO), a pilot allocation algorithm based on access point (AP) selection is proposed. To improve the system performance further, data power optimization is carried out. First, the AP that serves each user is selected through the user-centered idea and the large-scale fading matrix. Use the same service AP numbers between users to measure the pilot contamination intensity and then assign orthogonal pilots to strong potential pilot contamination users. A spectrum efficiency (SE) scheme to maximize the minimum user is proposed for the fairness problem in data power optimization. It is proved that the objective function of data power optimization accords with linear programming, and a dichotomy is given to solve the problem. Simulation results show that the pilot allocation algorithm based on AP selection can significantly improve the total SE of the system and reduce the computational complexity of the system. At the same time, the data power optimization algorithm improves minimum SE for users in the system.

Energy and Delay Guaranteed Joint Beam and User Scheduling Policy in 5G CoMP Networks

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations.

HF Skywave Massive MIMO Communication

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. Considering the large antenna array aperture and increased signal bandwidth, the effect of the propagation delay across the large-scale antenna array cannot be ignored, and thus the steering vectors vary across different subcarriers. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error (MMSE) based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. In order to reduce the design complexities of the MMSE receiver and precoder, we derive a polynomial expansion based design using a deterministic equivalent. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

Energy Efficiency Optimization for Compact Massive MIMO Wireless Systems

In the face of the unprecedented demand for higher data rates while coping with the constraints from physical size (i.e., windward area), it is profitable to deploy compact planar arrays (CPAs) on the base stations (BSs). But massively densified antenna deployment not only leads to antenna mutual coupling (MC), but also consumes additional energy due to the equipment of more radio frequency (RF) chains. This paper studies energy efficiency (EE) optimization for massive multi-input multi-output (MIMO) wireless systems with a CPA in the presence of MC. In comprehensive consideration of the spectral efficiency (SE) and power consumption, a joint optimization problem of beamforming and antenna subset (AS) is proposed. Our motivation is to relieve the effects of MC and reduce the relevant energy consumption by constructing irregular AS. Particularly, considering the nonconvexity of the objective and the discrete 0-1 constraints, an available iterative algorithm, named the energy-efficient beamforming and AS (EE-BF&AS) algorithm, is developed. Numerical results illustrate that when MC is modeled, the proposed EE-BF&AS algorithm outperforms conventional related approaches in EE. By using the AS switching strategy, the irregular AS can be constructed to make MC less serious and let the relevant energy consumption decrease.

Energy Efficiency of User-Centric, Cell-Free Massive MIMO-OFDM with Instantaneous CSI.

In the user-centric, cell-free, massive multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, a large number of deployed access points (APs) serve user equipment (UEs) simultaneously, using the same time–frequency resources, and the system is able to ensure fairness between each user; moreover, it is robust against fading caused by multi-path propagation. Existing studies assume that cell-free, massive MIMO is channel-hardened, the same as centralized massive MIMO, and these studies address power allocation and energy efficiency optimization based on the statistics information of each channel. In cell-free, massive MIMO systems, especially APs with only one antenna, the channel statistics information is not a complete substitute for the instantaneous channel state information (CSI) obtained via channel estimation. In this paper, we propose that energy efficiency is optimized by power allocation with instantaneous CSI in the user-centric, cell-free, massive MIMO-OFDM system, and we consider the effect of CSI exchanging between APs and the central processing unit. In addition, we design different resource block allocation schemes, so that user-centric, cell-free, massive MIMO-OFDM can support enhanced mobile broadband (eMBB) for high-speed communication and massive machine communication (mMTC) for massive device communication. The numerical results verify that the proposed energy efficiency optimization scheme, based on instantaneous CSI, outperforms the one with statistical information in both scenarios.

Deep learning for fast channel estimation in millimeter-wave MIMO systems

Channel estimation has been considered as a key issue in the millimeter-wave (mmWave) massive multi-input multioutput (MIMO) communication systems, which becomes more challenging with a large number of antennas. In this paper, we propose a deep learning (DL)-based fast channel estimation method for mmWave massive MIMO systems. The proposed method can directly and effectively estimate channel state information (CSI) from received data without performing pilot signals estimate in advance, which simplifies the estimation process. Specifically, we develop a convolutional neural network (CNN)-based channel estimation network for the case of dimensional mismatch of input and output data, subsequently denoted as channel (H) neural network (HNN). It can quickly estimate the channel information by learning the inherent characteristics of the received data and the relationship between the received data and the channel, while the dimension of the received data is much smaller than the channel matrix. Simulation results show that the proposed HNN can gain better channel estimation accuracy compared with existing schemes.