Massive Massive Multi-input Multi-output Channel Research Articles

A novel blind spatial-based random access to pilots (BSB-RAP) in overloaded massive MIMO systems

In this study, to increase the success rate of active user admission in overloaded massive multi-input multi-output (MIMO) systems, a new spatially based random access to pilots (RAP) is proposed to assign orthogonal pilots to the users requesting network access. Therefore, by increasing the acceptance rate of users in a cell, this approach reduces the training overhead and waste of resources. In the massive MIMO for crowd scenarios, the main issue is the limited number of available orthogonal pilots employed by the users in the channel estimation process. This novel approach as spatially based random access enables us to have more connected users during every coherence interval (CI) despite the mentioned limitation. Intrinsic angular domain sparsity of massive MIMO channels and the sporadic traffic of users can help us obtain the spatial features of active UEs in a blind continuous compressed sensing (CCS) approach. Proposed approach is to use a continuous compressed sensing technique based on a prior optimization that provides users’ angle of arrival (AoA) and an innovative space-based RAP protocol to assign orthogonal pilots to active users in coherent transmission. Unlike the previous works, this strategy does not need to limit the number of users to the number of available orthogonal pilots due to the employed spatial degrees of freedom.

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.

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.

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.

Spatial Based Pilot Allocation (SBPA) in Crowded Massive MIMO Systems

In this study, a new approach of random access and pilot allocation in crowded scenarios of massive multi-input multi-output (MIMO) systems is proposed. When the number of users in a cell is more than number of orthogonal pilots, random access to pilots (RAP) is a promising solution to solve the massive access problem of users; though the collision of pilots is a by-product of this method. There are several methods for pilot collision resolution such as strongest user collision resolution (SUCR), but they have one major limitation; i.e. they cannot serve users more than the number of orthogonal pilots simultaneously in coherent transmission mode. In this paper, angle of arrival (AOA) and angular spread (AS) of the received pilots in a compressed sensing approach are examined by applying sparsity of massive MIMO channels in angular domain. That allows to allocate same orthogonal pilot to non-overlapped users in spatial domain for coherent data transmission. By applying this approach in newly proposed protocol (SBPA), the limitation in number of adopted simultaneous users in crowded scenarios of a massive MIMO system would be resolved. Numerical results show improvement in access failures rate compared to SUCR in crowded massive MIMO systems.

Compression-Based LMMSE Channel Estimation With Adaptive Sparsity for Massive MIMO in 5G Systems

Massive multi-input multi-output (MIMO) has been regarded as one of the key technologies for fifth generation (5G) mobile communication systems, as it can significantly enhance the system capacity with high spectrum and energy efficiency. For massive MIMO systems, accurate channel estimation is a challenging problem, especially when the number of parameters to be estimated is large and the number of pilots is limited. In this paper, a compression-based linear minimum mean square error (CLMMSE) channel estimation algorithm is proposed for massive MIMO in 5G systems. Compared with the traditional linear minimum mean square error (LMMSE) algorithm, the proposed approach calculates the channel autocorrelation matrix by investigating the channel prior information based on compressive sensing (CS) theory, utilizing the block sparsity of massive MIMO channels, to reduce the complexity for obtaining autocorrelation matrix. Then it substitutes matrix inverse operation by singular value decomposition to further reduce the computational complexity. In addition, a block sparsity adaptive matching pursuit (BSAMP) method is also proposed to adaptively estimate the block sparsity of the channel in the first step of the proposed CLMMSE algorithm, which can make it more efficient. The sparsity-adaptive processing is achieved by setting a threshold and finding the position of the maximum backward difference, then using the regularized method to solve channel estimation as a convex optimization problem. Analyses and simulations indicate that with slight performance degradation, the proposed algorithm reduces the computational complexity significantly compared with the traditional LMMSE algorithm. And compared with pure CS methods, CLMMSE has an obviously better performance, which is beneficial to solve the pilot pollution problem of massive MIMO in 5G systems. Furthermore, the BSAMP-based CLMMSE algorithm has better performance and lower time complexity than the algorithm based on other CS methods, which further improves the system performance.

Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems

Pilot contamination is the reuse of pilot signals, which is a bottleneck in massive multi-input multi-output (MIMO) systems as it varies directly with the numerous antennas, which are utilized by massive MIMO. This adversely impacts the channel state information (CSI) due to too large pilot overhead outdated feedback CSI. To solve this problem, a compressed sensing scheme is used. The existing algorithms based on compressed sensing require that the channel sparsity should be known, which in the real channel environment is not the case. To deal with the unknown channel sparsity of the massive MIMO channel, this paper proposes a structured sparse adaptive coding sampling matching pursuit (SSA-CoSaMP) algorithm that utilizes the space–time common sparsity specific to massive MIMO channels and improves the CoSaMP algorithm from the perspective of dynamic sparsity adaptive and structural sparsity aspects. It has a unique feature of threshold-based iteration control, which in turn depends on the SNR level. This approach enables us to determine the sparsity in an indirect manner. The proposed algorithm not only optimizes the channel estimation performance but also reduces the pilot overhead, which saves the spectrum and energy resources. Simulation results show that the proposed algorithm has improved channel performance compared with the existing algorithm, in both low SNR and low pilot overhead.

Massive MIMO multi-pair two-way half-duplex AF FDD relaying: channel estimation

We consider two-way amplify-and-forward FDD massive multi-input-multi-output (MIMO) relaying system, where multiple half-duplex (HD) user-pairs exchange information via a shared HD relay equipped with massive MIMO. To design and study the performance of these systems, we require channel state information in uplink as well as in downlink. For the uplink of the system model, we use conventional low-complexity MMSE estimator for the channel estimation, wherein the number of orthogonal pilots required for uplink channel estimation are of the order of the number of users. However, in the massive MIMO relay systems, the downlink channel estimation becomes challenging, because of increased pilot overhead in proportion to the number of antennas at the relay and employing conventional channel estimation approaches will result in poor spectral efficiency. The massive MIMO channels exhibits sparse structure in the angular domain, due to limited scattering environment. We exploit this channel sparsity to reduce the pilot overhead by performing sparse Bayesian learning-based downlink channel estimation. Furthermore, practical massive MIMO systems are built with low-cost hardware components which makes the massive MIMO system prone to hardware impairments like phase and quantization errors. In this paper, we numerically analyze the effect of hardware impairments on the two-way multi-pair half-duplex massive MIMO relay systems.

Directional Training for FDD Massive MIMO

A key challenge for frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) is the large overhead in acquiring channel state information (CSI) for transmits beamforming. In this paper, we propose a scalable method called directional training to obtain downlink CSI. Directional training is motivated by two empirical results derived from massive MIMO channel measurements. First, the number of dominant angle-of-arrivals (departures) is much smaller than and nearly independent of the number of base-station antennas. Second, there is a strong correlation between uplink arrival and downlink departure angles even in FDD systems, which leads to the idea of directional training, where a small number of training symbols can be sent to estimate the dominant components of the downlink channel. Therefore, directional training measures much fewer complex coefficients than full-training-based methods, and as a result, compared with full-training, the overall channel acquisition overhead for directional training scales much slower with the number of base-station antennas. We evaluate directional training with extensive experiments with a 64-antenna base-station at two bands separated by approximately 72 MHz. Our results show that directional training-based downlink beamforming outperforms full-training systems by 150% in terms of average spectral efficiency, and loses only 5.3% average spectral efficiency from genie-aided systems.

Spatial Correlation Models of Large-Scale Antenna Topologies Using Maximum Power of Offset Distribution and its Application

In this paper, we present an approximate expression for spatial correlation of cylindrical and uniform rectangular arrays regarding the maximum power of offset distributions useful for evaluating geometry-based stochastic channel models. The underlying concept of the proposed spatial correlation expression is that the maximum power of arrival varies relative to the offset distribution in the azimuth and zenith domains. Verification is accomplished with the help of computer simulation, where perfect agreement between theoretical and Monte Carlo simulation results is found. Toward the implementation of large-scale MIMO transmitters in constrained physical spaces to accomplish the demands for the next generation of wireless systems, we examine the effect of the proposed spatial correlation model on the downlink (DL) performance of massive multi-input-multi-output (MIMO) system. Results illustrate that the DL capacity does not demonstrate any sensitivity to the effect of correlation by increasing the number of elements in a realistic massive MIMO channel. Surprisingly, while the spatial correlation restricts the DL capacity at a reduced number of transmit antennas, the restriction becomes insignificant as the number of transmit antennas increases. This supports existing developments that user channels decorrelate when the number of antennas at the base station increases. Finally, experimental results show that separation of more than half wavelength is inadequate to decorrelate the antenna elements.

EM-based parameter iterative approach for sparse Bayesian channel estimation of massive MIMO system

One of the main challenges for a massive multi-input multi-output (MIMO) system is to obtain accurate channel state information despite the increasing number of antennas at the base station. The Bayesian learning channel estimation methods have been developed to reconstruct the sparse channel. However, these existing methods depend heavily on the channel distribution. In this paper, based on sparse Bayesian method, an expectation maximization-based parameter iterative approach is proposed to estimate the massive MIMO channel with unknown channel distribution. Using the approximate sparse feature, the massive MIMO channel is modeled as a non-zero Gaussian mixture and the sparse Bayesian channel estimation is introduced. The channel marginal probability density function is expressed by using the general approximate message-passing algorithm. All of the required channel parameters are iteratively estimated by the EM method. Simulation results show that the proposed scheme enables evident performance in channel estimation accuracy with a lower complexity when channel distribution is unknown.

Weighted compressive sensing based uplink channel estimation for time division duplex massive multi‐input multi‐output systems

In this paper, the channel estimation problem for the uplink massive multi-input multi-output (MIMO) system is considered. Motivated by the observations that the channels in massive MIMO systems may exhibit sparsity and the channel support changes slowly over time, we propose one efficient channel estimation method under the framework of compressive sensing. By exploiting the channel impulse response (CIR) estimated from the previous OFDM symbol, we firstly estimate the probabilities that the elements in the current CIR are nonzero. Then, we propose the probability-weighted subspace pursuit (PWSP) algorithm exploiting these probability information to efficiently reconstruct the uplink massive MIMO channel. Moreover, noting that the massive MIMO systems also share a common support within one channel matrix due to the shared local scatterers in the physical propagation environment, an antenna collaborating method is exploited for the proposed method to further enhance the channel estimation performance. Simulation results show that compared to the existing compressive sensing methods, the proposed methods could achieve higher spectral efficiency as well as more reliable performance over time-varying channel.

Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér-Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.

Indoor Massive MIMO Channel Modelling Using Ray-Launching Simulation

Massive multi-input multioutput (MIMO) is a promising technique for the next generation of wireless communication networks. In this paper, we focus on using the ray-launching based channel simulation to model massive MIMO channels. We propose one deterministic model and one statistical model for indoor massive MIMO channels, both based on ray-launching simulation. We further propose a simplified version for each model to improve computational efficiency. We simulate the models in indoor wireless network deployment environments and compare the simulation results with measurements. Analysis and comparison show that these ray-launching based simulation models are efficient and accurate for massive MIMO channel modelling, especially with application to indoor network planning and optimisation.