MIMO Channel Research Articles

Massive MIMO Channels With Inter-User Angle Correlation: Open-Access Dataset, Analysis and Measurement-Based Validation

In practical propagation environments, different massive MIMO users can have correlated angles in spatial paths. In this paper, we study the effect of angle correlation on inter-user channel correlation via a combination of measurement and analysis. We show three key results. First, we collect a massive MIMO channel dataset for examining the inter-user channel correlation in a real-world propagation environment; the dataset is now open-access. We observed channel correlation higher than 0.48 for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</i> close-by users. Additionally, over 30% of far-away users, even when they are tens of wavelengths apart, have inter-user channel correlation that is at least twice higher than the correlation in the i.i.d. Rayleigh fading channel. Second, we compute the inter-user channel correlation in closed-form as a function of inter-user angle correlation, the number of base-station antennas, and base-station inter-antenna spacing. Our analysis shows that inter-user angle correlation increases the inter-user channel correlation. Inter-user channel correlation reduces with a larger base-station array aperture, i.e., more antennas and larger inter-antenna spacing. Third, we explain the measurements with numerical experiments to show that inter-user angle correlation can result in significant inter-user channel correlation in practical massive MIMO channels.

A 3d Non-Stationary Geometry-Based Stochastic Model for Uav Air-to-Ground Mimo Channels

A 3d Non-Stationary Geometry-Based Stochastic Model for Uav Air-to-Ground Mimo Channels

Overview of Channel Bonding and MIMO of ATSC 3.0 Broadcasting System

Overview of Channel Bonding and MIMO of ATSC 3.0 Broadcasting System

Millimetre wave 3-D channel modelling for next generation 5G networks

&lt;abstract&gt;&lt;p&gt;Millimetre wave (mm-wave) spectrum (30-300GHz) is a key enabling technology in the advent of 5G. However, an accurate model for the mm-wave channel is yet to be developed as the existing 4G-LTE channel models (frequency below 6 GHz) exhibit different propagation attributes. In this paper, a spatial statistical channel model (SSCM) is considered that estimates the characteristics of the channel in the 28, 60, and 73 GHz bands. The SSCM is used to mathematically approximate the propagation path loss in different environments, namely, Urban-Macro, Urban-Micro, and Rural-Macro, under Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions. The New York University (NYU) channel simulator is utilised to evaluate the channel model under various conditions including atmospheric effects, distance, and frequency. Moreover, a MIMO system has been evaluated under mm-wave propagation. The main results show that the 60 GHz band has the highest attenuation compared to the 28 and 73 GHz bands. The results also show that increasing the number of antennas is proportional to the condition number and the rank of the MIMO channel matrix.&lt;/p&gt;&lt;/abstract&gt;

Hybrid Massive MIMO Channel Model Based on Edge Detection of Interacting Objects and Cluster Concept

This paper presents a novel channel model based on the edge detection of the interacting objects (IOs) for massive multiple-input and multiple-output (MIMO) systems considering different propagation phenomena such as reflection, refraction, diffraction, and scattering occur at the edges of the IOs. This channel model also uses cluster concepts to model multipath components (MPCs). The time-variant condition of the channel as well as the Doppler effect is considered in this channel model. Also, the non-stationary property of the channel across the array axis can be observed in the simulations. Due to a large number of antenna elements utilized in the massive MIMO systems, the spherical wavefront is assumed instead of the plane wavefront which is used in the conventional MIMO channel model. The central limit theory is also utilized to model the MPCs of each cluster. Furthermore, specific channel characteristics such as channel impulse response (CIR), angle of arrival (AoA) as well as time of arrival (ToA) are extracted in the simulations of the channel.

The Tensor Multi-Linear Channel and Its Shannon Capacity

Tensors are multi-way arrays which can be used to model systems spanning many domains. This work proposes to use tensors for characterizing, analyzing, and designing multi-domain communication systems. Most modern day communication systems make use of coding and modulation across different domains such as space, frequency, time. Hence a unified mathematical framework characterizing such a multiple-domain system in an intuitive manner is well needed. In this paper, we present such a unified framework that characterizes a communication system with <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> input domains, <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> output domains and an <inline-formula> <tex-math notation="LaTeX">$M+N$ </tex-math></inline-formula> domains multi-linear tensor channel. The proposed framework is generic where the physical interpretation of the domains is system specific. We illustrate a few examples from multi-antenna multi-carrier and multi-user systems that fit the proposed framework. Assuming a fixed tensor channel, we provide an information theoretic analysis by deriving its Shannon capacity and input power allocation under a variety of power constraints. In this paper we show how the tensor framework&#x2019;s suitability to mathematically describe a family of power constraints can be used to design and analyze various multiple domain communication systems. The tensor based approach extends water-filling from a matrix setting to tensors, encapsulating the effects of multiple domains thereby allowing joint multi-domain precoding. We show that the capacity pre-log for a tensor channel increases exponentially in the number of domains, indicating the potential of tensor based multi-domain communication systems to provide the large information transmission rates envisaged for 5G and beyond systems. We also show the application of the tensor framework in characterizing the capacity and rate regions of multi-user MIMO channels. Both multiple access and interference channels are considered where the tensor based approach leads to a coordinated users transmission scheme. Such a scheme ensures higher achievable sum rates as compared to independent user transmissions.

Capacity Detection Of Massive Mimo Channel In 5G Environment Based On Symmetric Correlation Matrix

Capacity Detection Of Massive Mimo Channel In 5G Environment Based On Symmetric Correlation Matrix

Noncoherent Space-Time Coding for Correlated Massive Mimo Channel with Riemannian Distance

Noncoherent Space-Time Coding for Correlated Massive Mimo Channel with Riemannian Distance

A Comparison Between Concentrated and Distributed Massive MIMO Channels at 26 GHz in a Large Indoor Environment Using Ray-Tracing

A Comparison Between Concentrated and Distributed Massive MIMO Channels at 26 GHz in a Large Indoor Environment Using Ray-Tracing

Optimization of Channel Estimation Using ELMx-based in Massive MIMO

In communication channel estimation, the Least Square (LS) technique has long been a widely accepted and commonly used principle. This is because the simple calculation method is compared with other channel estimation methods. The Minimum Mean Squares Error (MMSE), which is developed later, is devised as the next step because the goal is to reduce the error rate in the communication system from the conventional LS technique which still has a higher error rate. These channel estimations are very important to modern communication systems, especially massive MIMO. Evaluating the massive MIMO channel is one of the most researched and debated topics today. This is essential in technology to overcome traditional performance barriers. The better the channel estimation, the more accurate it is. This paper investigated machine learning (ML) for channel estimation. ML channel estimations based on the Extreme Learning Machine (ELMx) group are also implemented. These estimations, known as the ELMx group, include Regularized Extreme Learning Machine (RELM) and Outlier Robust Extreme Learning Machine (ORELM). Then, it was compared with LS and MMSE. The simulation results reveal that the ELMx group outperforms LS and MMSE in channel capacity and bit error rate. Additionally, this paper has proven complexity for verified computational times. The RELM method is less time consuming and has low complexity which is suitable for future use in large MIMO systems.

Machine Learning for CSI Recreation in the Digital Twin Based on Prior Knowledge

Knowledge of channel state information (CSI) is fundamental to many functionalities for mobile communication systems. With the advance of machine learning (ML) and digital maps, i.e., digital twins, we have a big opportunity to learn the propagation environment and design novel methods to derive and report CSI. In this work, we propose to combine untrained neural networks (UNNs) and conditional generative adversarial networks (cGANs) for MIMO channel recreation based on prior knowledge. The UNNs learn the prior-CSI for some locations which are used to build the input to a cGAN. Based on the prior-CSI estimates, their locations and the location of the desired channel, the cGAN is trained to output the channel expected at the desired location. This combined approach can be used for low overhead CSI reporting as, after training, we only need to report the desired location. Our results show that our CSI recreation method is successful in modelling the wireless channel under different configurations of prior-CSI spatial sampling. In addition, the results consider a real world measurement campaign for indoor line of sight and non-line of sight channels. The signal to noise ratio (SNR) achieved by our CSI recreation is better than the SNR reported by the measured campaign providers. Moreover, our CSI recreation provides means for low overhead CSI reporting as the UNN structure is underparameterized compared to the full explicit CSI, and only the desired location is needed for the cGAN to recreate the desired CSI.

Statistics of the Effective Massive MIMO Channel in Correlated Rician Fading

Massive multiple-input multiple-output base stations use multiple spatial diversity branches, which are often assumed to be uncorrelated in theoretical work. Correlated branches are seldom considered since they are mathematically less tractable. For correlated Rician fading, only the first- and second-order moments have been explored. To describe propagation environments more accurately, full distribution functions are needed. This manuscript provides distribution functions for the maximum ratio combining effective channel, a quadratic form of a random complex normal channel vector. Its mean vector and covariance matrix are based on a plane wave model incorporating array geometry, antenna element pattern, power angular spectra and power delay profiles. Closed-form approximations of the distribution functions are presented, to allow the fast evaluation of many real-world scenarios. The statistical framework is used to show that low-directivity antenna elements provide better performance in angular constricted Rician fading with off-axis incidence than high-directivity elements. Moreover, two base station array layouts are compared, showing that a half-circle array illuminates a cell more evenly than a uniform linear array. With the full distribution functions available, performance can be compared over the full range of received powers and not only based on the average signal-to-noise ratio.

Capacity detection of massive MIMO channel in 5G environment based on symmetric correlation matrix

Capacity detection of massive MIMO channel in 5G environment based on symmetric correlation matrix

Joint dictionary and deep learning for cosparse recovery of the millimeter wave massive MIMO channels

The channel estimation (CE) for millimeter wave (mmW) massive multiple input multiple output (mMIMO) is a challenging task because of the important number of transmit and receive antennas, which results in high pilot overhead. In conventional CE algorithms, the channel is modeled using pre-constructed dictionary. This often leads to a suboptimal solution which cannot guarantee CE accuracy. In this paper, an iterative two-stage CE algorithm is presented. In the first stage, training measurements under different conditions are collected and it is proposed to estimate the virtual sparse mmW mMIMO channel using a deep residual learning based orthogonal approximate message passing (DRL-OAMP) algorithm from these measurements. The estimated channel is used in the second stage to learn the dictionary via a projected gradient algorithm. Simulation results show that the proposal improves the CE accuracy with low pilot overhead.

Performance evaluation of MIMO channel model using the open-source model implementation

In this paper the wireless MIMO channel modeling using the open-source model implementation QUADGIGA is considered. The obtained impulse responses are presented and used for channel parameters estimation. The validation of the MIMO channel modeling was evaluated by calculating the correlation matrix and CDF of channel capacity in different configurations. Statistical distribution of magnitude and the elements phase of the MIMO channel matrix are evaluated numerically by histogram.

Calculating Beamforming Vectors for 5G System Applications

The growing demand for broadband Internet services is forcing scientists around the world to seek and develop new telecommunication technologies. With the transition from the fourth generation to the fifth generation wireless communication systems, one of these technologies is beamforming. The need for this technology was caused by the use of millimeter waves in data transmission. This frequency range is characterized by heavy path loss. The beamforming technology could compensate for this significant drawback. This paper discusses basic beamforming schemes and proposes a model implemented on the basis of QuaDRiGa. The model implements a MIMO channel using symmetrical antenna arrays. In addition, the methods for calculating the antenna weight coefficients based on the channel matrix are compared. The first well-known method is based on the addition of cluster responses to calculate the coefficients. The proposed one uses the singular value decomposition of the channel matrix into clusters to take into account the most correlated information between all clusters when calculating the antenna coefficients. According to the research results, the proposed method for calculating the antenna coefficients allows an increase in the SNR/SINR level by 8–10 dB on the receiving side in the case of analog beamforming with a known channel matrix.

Modeling and Performance of Mine MIMO Channel Based on Geometric Stochastic Characteristics

MIMO technology has become one of the most promising content in mobile wireless communication, and it has shown positive application value in both application and innovation. On the basis of understanding the current development status of MIMO channel technology, this paper analyzes the standardization of MIMO to the future development trend according to the modeling of broadband dryness ratio of GBSM.

Sparsity-Exploiting Blind Receiver Algorithms for Unsourced Multiple Access in MIMO and Massive MIMO Channels

We propose new transmission schemes and receiver algorithms for unsourced multiple access (UMA) in MIMO and massive MIMO channels. Each active transmitter’s information bits are first channel encoded. The coded bits are divided into sub-blocks and each sub-block is modulated and transmitted. For both MIMO and massive MIMO channels, the conventional nonlinear modulation can be employed where each sub-block of coded bits is mapped to a transmitted signal vector. For the massive MIMO channel, we propose a new hybrid modulation scheme to reduce the receiver complexity, where the first sub-block is nonlinearly modulated, and the subsequent sub-blocks are linearly modulated and spread by the first sub-block signal. We also propose sparsity-exploiting blind receiver algorithms. Specifically, for the MIMO case, we exploit the codeword sparsity inherent in the UMA system, and a channel clustering technique, to estimate the channel and the transmitted signal of each transmitter. For the massive MIMO, in addition to the codeword sparsity, we further exploit the channel sparsity and user sparsity in estimating the channel and transmitted signal of each transmitter. The proposed receiver algorithms for both MIMO and massive MIMO channels output either hard or soft estimates of the coded bits, and therefore single-user channel decoding of the information bits can be performed for each transmitter. Extensive simulation results are provided to demonstrate the performances of the proposed algorithms.

Efficient Channel Charting via Phase-Insensitive Distance Computation

Channel charting is an unsupervised learning task whose objective is to encode channels so that the obtained representation reflects the relative spatial locations of the corresponding users. It has many potential applications, ranging from user scheduling to proactive handover. In this letter, a channel charting method is proposed, based on a distance measure specifically designed to reduce the effect of small scale fading, which is an irrelevant phenomenon with respect to the channel charting task. A nonlinear dimensionality reduction technique aimed at preserving local distances (Isomap) is then applied to actually get the channel representation. The approach is empirically validated on realistic synthetic multipath MIMO channels, achieving better results than previously proposed approaches, at a lower cost.

Study of the STBC based MIMO System Modeling and Evaluation

There are many applications using the small MIMO antenna structure for performance improvement. The Long Term Evolution (LTE) and Wi-Fi wireless modems are good example of the MIMO based modeling. Many coding techniques have been designed for enhancing the performance of the MIMO channels. This paper has presented the comprehensive study of the Coding techniques used for MIMO system specially using the Space Time Block Codes (STBC). Basically the coding techniques are used for improving the estimation of the MIMO Channel states information (CSI). Various design challenges of the channel estimation for wireless communication are addressed and problems are identified. The comparison of various SCBC based MIMO channel estimation methods have been tabulated based on the BER and MSE performances.