Massive Multiple-input Multiple-output Channel Model Research Articles

A Novel 3D Non-Stationary Massive MIMO Channel Model for Shortwave Communication Systems

In this paper, a novel three-dimensional (3D) non-stationary massive multiple-input multiple-output (MIMO) channel model for shortwave communication systems is proposed. Three transmission modes, i.e., groundwave, near vertical incident skywave (NVIS), and long-distance skywave are considered to eliminate the blind area and realize the full-coverage for shortwave communication. The ionospheric absorption loss and surface reflection loss during multi-hop transmissions are explored in the proposed channel model. In addition, new massive MIMO channel characteristics including the near-field spherical wavefront effect and spatial non-stationarity are considered. Temporal and frequency non-stationarities are also modeled due to the receiver (Rx) mobility and large relative bandwidth, respectively. The analytical and simulated space cross-correlation function (SCCF), time autocorrelation function (TACF), and frequency correlation function (FCF) of the proposed model are compared. The simulated path loss and singular value spread (SVS) are compared with those of the corresponding channel measurements, illustrating good fittings. In addition, the delay power spectral density (PSD) and Doppler PSD, and channel capacity are also simulated and analyzed. The proposed model can be used as a basis for the design and construction of shortwave communication systems.

Classification and Comparison of Massive MIMO Propagation Channel Models

Considering great benefits brought by massive multiple-input–multiple-output (MIMO) technologies in the Internet of Things (IoT), it is of vital importance to analyze new massive MIMO channel characteristics and develop corresponding channel models. In the literature, various massive MIMO channel models have been proposed and classified with different but confusing methods, i.e., physical versus analytical method and deterministic versus stochastic method. To have a better understanding and usage of massive MIMO channel models, this work summarizes different classification methods and presents an up-to-date unified classification framework, i.e., artificial intelligence (AI)-based predictive channel models and classical nonpredictive channel models, which further clarify and combine the deterministic versus stochastic and physical versus analytical methods. Furthermore, massive MIMO channel measurement campaigns are reviewed to summarize new massive MIMO channel characteristics. Recent advances in massive MIMO channel modeling are surveyed. In addition, typical nonpredictive massive MIMO channel models are elaborated and compared, i.e., deterministic models and stochastic models, which include the correlation-based stochastic model (CBSM), geometry-based stochastic model (GBSM), and beam-domain channel model (BDCM). Finally, future challenges in massive MIMO channel modeling are given.

A 3D Non-Stationary Wideband Massive MIMO Channel Model Based on Ray-Level Evolution

In this paper, a novel space-time non-stationary three-dimensional (3D) wideband massive multiple-input multiple-output (MIMO) channel model is proposed. We then propose a ray-level process to model the spatial-temporal evolution of individual multipath components (MPCs), including near-field effects and (dis)appearance, and cluster-level large-scale fading. The proposed evolution process can flexibly control rays’ lifespans and smoothness of (dis)appearance in both space and time domains. In addition, we propose an improved Rayleigh-distance criterion to determine the most adequate wavefront for each cluster and ray. Existing models can easily implement the proposed criterion and make a more efficient use of computation resources. Also, a Gamma-Poisson mixture distribution is introduced to model the distribution of the number of clusters when multiple locations of the mobile station are considered. Key statistical properties of the channel, including the autocorrelation function (ACF), Doppler power spectral density (PSD), spatial cross-correlation function (S-CCF), and frequency correlation function (FCF), are derived and the impact of the ray-level evolution process on them is analyzed. We demonstrate the correctness of the derived statistical properties through numerical and simulation results.

Measurements and Characterization for Millimeter-Wave Massive MIMO Channel in High-Speed Railway Station Environment at 28 GHz

The millimeter-wave (mmWave) and massive multiple-input multiple-output (MIMO) wireless communication technologies provide vital means to resolve many technical challenges of the fifth-generation (5G) or beyond 5G (B5G) network. Analyzing the measured datasets extracted from the channel measurements can provide insight into the characteristics of radio channels in different scenarios. Therefore, mmWave massive MIMO channel measurements, simulation, and modeling are carried out in the high-speed railway waiting hall environments at 28 GHz. The multipath components (MPCs) parameters are estimated for line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios based on the space-alternating generalized expectation-maximization (SAGE) algorithm. Delay spread (DS), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) are analyzed. And they are processed by using the K-mean algorithm. In addition, propagation characteristics are simulated based on the improved ray tracing method of shooting and bouncing ray tracing/image (SBR/IM). The correctness of the improved ray tracing method is verified by comparing the measured results with the simulated results. The large-scale path loss (PL) is characterized based on close-in (CI) free-space reference distance model and the floating-intercept (FI) path loss model. Furthermore, statistical distributions for root-mean-square delay spread (RMS DS) are investigated. The Gaussian distribution best fits the measured data of RMS delay spread. Finally, multipath clustering is identified using the multipath component distance (MCD). The analysis of these results from mmWave massive MIMO channel measurements and simulation may be instructive for the deployment of the 5G or B5G wireless communications systems at 28 GHz.

A 3D Cluster-Based Channel Model for 5G and Beyond Vehicle-to-Vehicle Massive MIMO Channels

In this paper, we propose a three-dimensional (3D) cluster-based model for the fifth generation (5G)/beyond 5G (B5G) vehicle-to-vehicle (V2V) massive multiple-input multiple-output (MIMO) wideband channels. It is the first cluster-based irregular shaped geometry-based stochastic model (IS-GBSM) to distinguish the dynamic clusters and static clusters in vehicular massive MIMO communication scenarios. This model not only considers the high time-variance, the time non-stationarity, and the vehicular traffic density (VTD) of V2V channels, but also models the massive MIMO channel characteristics, such as spherical wavefronts by 3D vector calculation and space non-stationarity. Meanwhile, this model for the first time integrates the VTD into birth-death process to model the channel characteristics of V2V and massive MIMO jointly and deeply, where a novel VTD-combined time-array cluster evolution algorithm for 5G/B5G V2V massive MIMO channel model is developed. Based on the proposed model, we derive some expressions of channel statistical properties, including time-space-frequency correlation function (STF-CF) and Doppler power spectral density (DPSD). The influence of several parameters, e.g., VTDs, vehicle moving directions, and antenna spacings, on the channel characteristics are sufficiently explored, which can provide assistance for the design of vehicular massive MIMO communication systems. Finally, the utility of the proposed model is verified by the close agreement between simulation results and measurement data.

Shallow Underwater Acoustic Massive MIMO Communications

The potential benefits of massive multiple-input multiple-output (MIMO) make it possible to achieve high-quality underwater acoustic (UWA) communications. Nevertheless, due to the wideband nature of UWA channels, existing massive MIMO techniques for radio frequency cannot be directly applied to UWA communications. This paper investigates a UWA massive MIMO system in the shallow-water environment, deploying large array apertures at both the transmitter and the receiver. We propose a beam-based UWA massive MIMO channel model and analyze its properties. Based on this model, we reveal that the transmit design for rate maximization can be performed in a dimension-reduced space related to the channel taps. Then, we prove that the beam-domain transmission is optimal to maximize the rate when with unlimited numbers of transducers. Furthermore, if the number of hydrophones also tends to infinity, the optimal power allocation can be obtained just by the water-filling algorithm and the corresponding rate positively correlates with the number of channel taps for the high signal-to-noise-ratio regime. Moreover, we devise a low-complexity algorithm to optimize the input covariance matrix for general cases. Simulation results illustrate the significant performance of the proposed algorithm and the high throughput achieved by massive MIMO.

A Novelty Massive MIMO Channel Model Based on the Birth‐and‐Death Process of Spatial Multipath Cluster

AbstractIn this paper a novelty Massive Multiple Input Multiple Output (MIMO) channel model based on the birth‐and‐death process of multipath clusters is proposed. First, Massive MIMO measurements are conducted in typical outdoSor scenarios with the bandwidth of 100 MHz at frequencies of 1.4725 and 6 GHz, respectively, following with the investigation of radio propagation characteristics over antenna array. Then we research on the subchannel correlation and nonstationary based on the channel sounding experiments. Furthermore, we build up a Markov chain to characterize the birth‐and‐death process of the multipath clusters over the array, which is helpful to bring deeply insight into the cluster evolution over the antenna array axis. And the analysis results are consistent with the theory of the cluster's visible region. At last, the paper systematically demonstrates how does the spatial correlation influence the capacity of the communication system. The whole research work aims to contribute the radio channel modeling of the fifth‐generation (5G) Massive MIMO communication system.

Networked Optical Massive MIMO Communications

The low cost and versatility of optical devices make it possible to pack a large number of optical transceivers into arrays and exploit massive multiple-input multiple-output (MIMO) transmission in optical wireless communications. Nevertheless, optical massive MIMO presents several distinct challenges such as, the line-of-sight propagation and intensity modulation, incompatible with existing radio frequency massive MIMO techniques. This paper presents a networked optical massive MIMO system that consists of multiple base stations (BSs), each equipped with a transmit lens and an optical transmitter array, cooperatively serving a number of user terminals (UTs), each equipped with a receive lens and a photodetector array. We establish the optical massive MIMO channel model, analyze its asymptotic behavior, and evaluate the potential of networked optical massive MIMO on system throughput improvement. To achieve high throughput, we propose optical beam division multiple access (BDMA) transmission schemes under the total and per transmitter power constraints with the asymptotic optimality. Our results show that the system sum rate increases proportionally to the number of BSs and UTs using the optical BDMA transmission. Further numerical results show that the proposed optical massive MIMO system along with the optical BDMA transmission is able to achieve high throughput with low complexity.

BER Performance of Spatial Modulation Systems Under a Non-Stationary Massive MIMO Channel Model

In this paper, the bit error rate (BER) performance of spatial modulation (SM) systems is investigated both theoretically and by simulation in a non-stationary Kronecker-based massive multiple-input-multiple-output (MIMO) channel model in multi-user (MU) scenarios. Massive MIMO SM systems are considered in this paper using both a time-division multiple access (TDMA) scheme and a block diagonalization (BD) based precoding scheme, for different system settings. Their performance is compared with a vertical Bell labs layered space-time (V-BLAST) architecture based system and a conventional channel inversion system. It is observed that a higher cluster evolution factor can result in better BER performance of SM systems due to the low correlation among sub-channels. Compared with the BD-SM system, the SM system using the TDMA scheme obtains a better BER performance but with a much lower total system data rate. The BD-MU-SM system achieves the best trade-off between the data rate and the BER performance among all of the systems considered. When compared with the V-BLAST system and the channel inversion system, SM approaches offer advantages in performance for MU massive MIMO systems.

Power Leakage Elimination for Wideband mmWave Massive MIMO-OFDM Systems: An Energy-Focusing Window Approach

When the large antenna array and the large bandwidth are deployed in a massive multiple-input multiple-output (MIMO) system, the spatial-wideband effect and the related beam squint effect make the energy of each multipath component distributed mainly within a square area in angle-delay domain rather than distributed like an impulse. With the finite numbers of antennas and subcarriers in practical massive MIMO-OFDM systems, the power leakage of each path outside the corresponding square area is non-negligible. In this paper, we first derive the millimeter-wave (mmWave) massive MIMO channel model for OFDM transceivers and then design an optimal energy-focusing window, which shapes the channel in spatial-frequency domain to maximally concentrate the energy of each multipath component inside the corresponding square area in angle-delay domain. With the help of this window, the power leakage can be significantly reduced. As a result, the sparsity of mmWave channels can be primely preserved and the inter-user interference can be significantly alleviated for angle- and delay-based channel estimation and precoding. Simulation results demonstrate the effectiveness of the proposed window design.

On Modeling of Dense Multipath Component for Indoor Massive MIMO Channels

Elaborate channel modeling is an essential prerequisite for design, testing, and improvement of the massive multiple-input–multiple-output (MIMO) system. In this letter, dense multipath component (DMC) modeling for massive MIMO channels is presented based on measurement campaigns in three different indoor scenarios. The parameters for DMC are extracted by applying a RiMAX-based estimator. The distance-dependence and the variance behaviors of the DMC parameters across the large-scale array are analyzed and modeled. Moreover, the model validation is performed by using an empirical cluster-based massive MIMO channel model. Entropy capacity, condition number, channel ellipticity, and root-mean-square delay spread, which reflect the spatial structure and multipath richness of the channel, are used as the validation metrics. The validation shows that the incorporation of DMC can significantly improve the accuracy of channel modeling.

A Spatially Consistent MIMO Channel Model With Adjustable K Factor

In the area of research on massive multiple-input multiple-output (MIMO), two assumptions on the wireless channel dominate channel modeling. Either, a rich scattering environment is assumed and the channel is modeled as i.i.d. Rayleigh fading, or, a line of sight (LOS) channel is assumed, enabling geometric channel modeling under a farfield assumption. However, either of these assumptions represents an extreme case that is unlikely to be observed in practice. While there is a variety of MIMO channel models in literature, most of them, and even very popular geometry based stochastic channel models, are not spatially consistent. This is especially problematic for technologies in which channel correlation of adjacent users is an important factor, such as massive MIMO. In this work, we introduce a simple but spatially consistent MIMO channel model based on multiple scattering theory. Our proposed channel model allows to adjust the Rician $K$ factor by controlling the number and strength of scattering elements. This allows to perform spatially consistent simulations of wireless communications systems for a large range of scattering environments in between an i.i.d. Rayleigh fading assumption and pure LOS channels. A statistical analysis in terms of the Rician $K$ factor for the introduced model is provided and verified by simulations. By comparison to other channel models, we show that non spatially consistent channel models lead to an underestimation of inter-user correlation and therefore to an overestimation of achievable sum rate.

Novel 3-D Nonstationary MmWave Massive MIMO Channel Models for 5G High-Speed Train Wireless Communications

In fifth generation (5G) wireless communications, high-speed train (HST) communications is one of the most challenging scenarios. By adopting massive multiple-input multiple-output (MIMO) and millimeter wave (mmWave) technologies into HST communications, the underlying communication system design becomes more challenging and some new channel characteristics have to be studied, such as the non-stationarities in space, time, and frequency domains. This paper proposes a novel three-dimensional space-time-frequency non-stationary mmWave massive MIMO theoretical model, as well as a corresponding simulation model for 5G HST wireless channels based on WINNER II and Saleh–Valenzuela channel models. Cluster evolutions in space, time, and frequency domains are proposed and analyzed to ensure the models’ non-stationarities in three domains. Moreover, based on the proposed channel models, important time-variant channel statistical properties are investigated, such as the time autocorrelation function, space cross-correlation function, delay power spectrum density (PSD), angular PSD, and frequency correlation function. Results indicate that the statistical properties of the simulation model, verified by simulation results, can match well with those of the theoretical model.

A Novel 3-D Massive MIMO Channel Model for Vehicle-to-Vehicle Communication Environments

This paper presents 3-D vehicle massive multiple-input multiple-output (MIMO) antenna array model for vehicle-to-vehicle (V2V) communication environments. A spherical wavefront is assumed in the proposed model instead of the plane wavefront assumption used in the conventional MIMO channel model. Using the proposed V2V channel model, we first derive the closed-form expressions for the joint and marginal probability density functions of the angle of departure at the transmitter and angle of arrival at the receiver in the azimuth and elevation planes. We additionally analyze the time and frequency cross-correlation functions for different propagation paths. In the proposed model, we derive the expression of the Doppler spectrum due to the relative motion between the mobile transmitter and mobile receiver. The results show that the proposed 3-D channel model is in close agreement with previously reported results, thereby validating the generalization of the proposed model.

Measurement-Based Massive MIMO Channel Modeling for Outdoor LoS and NLoS Environments

In this paper, a measurement campaign for massive multiple-input multiple-output (MIMO) channel characterization in both line-of-sight (LoS) and non-LoS outdoor environments is introduced. The measurements are conducted at the center frequency of 15 GHz with a bandwidth of 4 GHz. A virtual $40\times 40$ planar antenna array formed by stepping a vertically-polarized bi-conical omni-directional antenna (ODA) along regularly-spaced grids is used in the receiver (Rx). The transmitter is equipped with a single ODA. To investigate channel variations over the Rx array, this 1600-element Rx array is split into multiple $7\times 7$ sub-arrays, and a maximum-likelihood parameter estimation algorithm implemented using the space-alternating generalized expectation-maximization principle is applied to extracting multipath components (MPCs) from sub-array outputs. The spatial variability of $K$ -factor, composite channel spreads in delay, azimuth, and elevation of arrival are investigated. Based on the estimated MPCs’ parameters, multipath clusters are identified and associated across the array to find the so-called spatial-stationary (SS) clusters. From several hundreds of SS-clusters extracted, we establish a stochastic model for their life distances in horizontal and vertical directions, two-dimensional (2-D) life region, and variations of cluster spreads. These findings are important for massive MIMO channel modeling in the cases, where 2-D large-scale arrays are considered.

Analysis of an Upper Bound on the Effects of Large Scale Attenuation on Uplink Transmission Performance for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is a potential candidate key technology for the 5G of wireless communication systems. In research to date, different power loss and shadowing effects on different antenna elements across the large arrays have been neglected. In this paper, based on an idealized propagation model, a new large scale attenuation (LSA) model is proposed, by which the large scale losses (path loss and shadowing effect) over the antenna array can be considered when establishing a massive MIMO channel model. By using this model, the spectral efficiency (in terms of bits/s/Hz sum-rate) of the maximum ratio combining (MRC) detector is derived for the uplink. The spectral efficiency performance of the zero forcing (ZF) detector also can be derived in the same manner. It can be found that the sum-rate performance (MRC and ZF) of our proposed channel model (assuming independent shadowing on all elements of the array) exceeds that of the conventional model (where the LSA effect is not included). Based upon our theoretical and simulation analysis, we have found that the spectral efficiency gap is mainly from the mean value of different shadowing effects across different elements, and the different path losses experienced by different antenna elements provide negligible contribution. This LSA model and the derived performance results could be beneficial and informative for the research, design and evaluation of the next generation of wireless communication system employing a massive MIMO configuration.

A limited feedback scheme for massive MIMO systems based on principal component analysis

Massive multiple-input multiple-output (MIMO) is becoming a key technology for future 5G cellular networks. Channel feedback for massive MIMO is challenging due to the substantially increased dimension of the channel matrix. This motivates us to explore a novel feedback reduction scheme based on the theory of principal component analysis (PCA). The proposed PCA-based feedback scheme exploits the spatial correlation characteristics of the massive MIMO channel models, since the transmit antennas are deployed compactly at the base station (BS). In the proposed scheme, the mobile station (MS) generates a compression matrix by operating PCA on the channel state information (CSI) over a long-term period, and utilizes the compression matrix to compress the spatially correlated high-dimensional CSI into a low-dimensional representation. Then, the compressed low-dimensional CSI is fed back to the BS in a short-term period. In order to recover the high-dimensional CSI at the BS, the compression matrix is refreshed and fed back from MS to BS at every long-term period. The information distortion of the proposed scheme is also investigated and a closed-form expression for an upper bound to the normalized information distortion is derived. The overhead analysis and numerical results show that the proposed scheme can offer a worthwhile tradeoff between the system capacity performance and implementation complexity including the feedback overhead and codebook search complexity.

Recent advances and future challenges for massive MIMO channel measurements and models

The emerging fifth generation (5G) wireless communication system raises new requirements on spectral efficiency and energy efficiency. A massive multiple-input multiple-output (MIMO) system, equipped with tens or even hundreds of antennas, is capable of providing significant improvements to spectral efficiency, energy efficiency, and robustness of the system. For the design, performance evaluation, and optimization of massive MIMO wireless communication systems, realistic channel models are indispensable. This article provides an overview of the latest developments in massive MIMO channel measurements and models. Also, we compare channel characteristics of four latest massive MIMO channel models, such as receiver spatial correlation functions and channel capacities. In addition, future challenges and research directions for massive MIMO channel measurements and modeling are identified.

A Non-Stationary Wideband Channel Model for Massive MIMO Communication Systems

This paper proposes a novel non-stationary wideband multi-confocal ellipse two dimensional (2-D) channel model for massive multiple-input multiple-output (MIMO) communication systems. Spherical wavefront is assumed in the proposed channel model, instead of the plane wavefront assumption used in conventional MIMO channel models. In addition, the birth-death process is incorporated into the proposed model to capture the dynamic properties of clusters on both the array and time axes. Statistical properties of the channel model such as the space-time-frequency correlation function and power imbalance on the antenna array are studied. The impact of the spherical wavefront assumption on the statistical properties of the channel model is investigated. Furthermore, numerical analysis shows that the proposed channel model is able to capture specific characteristics of massive MIMO channel as observed in measurements.

A Non-Stationary 3-D Wideband Twin-Cluster Model for 5G Massive MIMO Channels

This paper proposes a novel theoretical non-stationary three dimensional (3-D) wideband twin-cluster channel model for massive multiple-input multiple-output (MIMO) communication systems with carrier frequencies on the order of gigahertz (GHz). As the dimension of antenna arrays cannot be ignored for massive MIMO, near field effects instead of far field effects are considered in the proposed model. These include the spherical wavefront assumption and a birth-death process to model non-stationary properties of clusters such as cluster appearance and disappearance on both the array and time axes. Their impacts on massive MIMO channels are investigated via statistical properties including correlation functions, condition numbers, and angular power spectra. Additionally, the impact of elevation angles on correlation functions is discussed. A corresponding simulation model for the theoretical model is also proposed. Finally, numerical analysis shows that the proposed channel models are able to serve as a design framework for massive MIMO channel modeling.