## What is/are Mimo Channels?

Mimo Channels - However, the instantaneous SNR differs among beliefs because the MIMO channels in the MUD problem are random; hence, the creation of LUT is infeasible.^{[1]}Second, utilizing the spatiotemporal common sparse property of the MIMO channels and the obtained PCCS information, we propose the priori-information aided distributed structured sparsity adaptive matching pursuit (PA-DS-SAMP) algorithm to achieve accurate channel estimation in frequency domain.

^{[2]}A thorough capacity study of spatially and temporally correlated UAV-MIMO channels is presented.

^{[3]}Furthermore, the capacity performance of MIMO channels is investigated using the proposed geometry based correlation model.

^{[4]}Given that in the limiting-case of high SNR, the DMT for MIMO channels are dictated by the tail behaviour of their probability density function [1] and based on the similarities between the tail behavior of lognormal and normal random variables for very small σx« 1 dB, and lognormal and Gamma random variables for small to medium σx ≤ 1.

^{[5]}However, the existing works rarely concentrate on URA in millimeter-wave (mmWave) line-of-sight (LOS) MIMO channels.

^{[6]}An SIP based method, exploiting first-order statistics of the received signals, is proposed for the estimation of frequency selective sparse massive-MIMO channels.

^{[7]}There have been a number of researches on block transmission systems via MIMO channels due to really high data transmission rate.

^{[8]}Our contributions consist in (1) formulating the matrix design problem in such a way that a multi-level water-filling solution proposed for MIMO channels can be adapted to LVC; (2) the proposal of three suboptimal power allocations techniques, with different trade-offs between complexity and efficiency; (3) a precoding matrix design for the multiple receivers scenario; (4) extensive simulations of the power allocation methods.

^{[9]}The extension to MIMO channels was given by I.

^{[10]}It can be used to offer a practical standard or a threshold criterion for selecting or eliminating some MIMO channels with either high or inferior quality respectively.

^{[11]}Thus, the SAR measurement time can be reduced by a factor up to the number of MIMO channels, depending on the used MIMO array geometry.

^{[12]}This article is intended for the hybrid diversity (space and polarization) performance analysis of MIMO channels in indoor environment under non-line-of-sight (NLOS) condition.

^{[13]}We prove that linear constraints consisting of a single equation always remove a single degree of freedom in the channel identification problem, in the sense that preknowledge of such constraints allows identification of MIMO channels with support size one greater than the fundamental limit.

^{[14]}For the practical application and accurate analysis of unmanned aerial vehicle (UAV) multiple-input multiple-output (MIMO) communication systems, it is significant to propose corresponding simulation models for non-isotropic scattering UAV-MIMO channels.

^{[15]}The authors in this paper also evaluate the performance of two important second-order statistical performance indicators, the level crossing rate (LCR) and average fade duration (AFD), for MIMO channels.

^{[16]}Moreover, to attain the estimates of the Channel State Information (CSI) in the uplink, the sparsity exhibited by the MIMO channels is exploited by incorporating CS based Orthogonal Matching Pursuit (OMP) algorithm.

^{[17]}The proposed precoder achieves simultaneous diagonalization of the MIMO channels of both users, assuming self-interference cancellation at one of the receivers, thereby lowering the overall decoding complexity.

^{[18]}As our analysis accounts for wideband channels, a time domain decomposition of MIMO channels into tapped delay lines is performed in each case.

^{[19]}Using the flatness factor for MIMO channels, we propose lattice codes universally achieving the secrecy capacity of compound MIMO wiretap channels up to a constant gap (measured in nats) that is equal to the number of transmit antennas.

^{[20]}In this paper, the outage probability is asymptotically studied for MIMO channels to thoroughly investigate the transmission reliability.

^{[21]}From these values, we predict the MIMO channels at the actual transmission time.

^{[22]}The proposal lets heterogenous wireless systems share their antennas, dynamically distributes the shared antennas among them, and increases sum of their MIMO channel capacity, and increases application throughput by bonding their MIMO channels.

^{[23]}Hybrid precoding has become a well-accepted approach to enhance the achievable rate in the adverse millimetre-wave (mm-Wave) MIMO channels.

^{[24]}Useful insights are drawn from comparative ASER analysis of various QAM schemes by considering the impact of both the fading parameter and the imperfect CSI for MIMO channels.

^{[25]}There are several models that strive to match the spatial correlation in M-MIMO channels, the exponential correlation model being one of these.

^{[26]}The MIMO channels comprise binary phase shift keying-spatially modulated (BPSK-SM) single-input-multiple-output (SIMO) channels that are conceived from robust selective combining of transmit diversity channels.

^{[27]}

## minimum mean square

In this paper, We study the performance of zero forcing (ZF) and minimum mean square error (MMSE) ZF detection methods in Impulsive multi-cell MIMO channels.^{[1]}In this paper, We study the performance of zero forcing (ZF) and minimum mean square error (MMSE) ZF detection methods in Impulsive multi-cell MIMO channels.

^{[2]}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.

^{[3]}

## Massive Mimo Channels

The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training.^{[1]}Simulation results indicate that the proposed channel estimation scheme can efficiently estimate correlated massive MIMO channels within a few training time slots.

^{[2]}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.

^{[3]}By considering both the spatial and temporal correlation properties in HSR massive MIMO channels, a novel channel prediction model that combines the convolutional long short-term memory (CLSTM) and convolutional neural network (CNN) is proposed and called as Conv-CLSTM.

^{[4]}In this paper, three types of user grouping algorithms in which our own performance metric is utilized are investigated for single carrier downlink wideband spatially correlated massive MIMO channels by using hybrid beamforming structure motivated by the joint spatial division and multiplexing (JSDM) framework.

^{[5]}We propose a novel optimization-based decoding algorithm for LDPC-coded massive MIMO channels.

^{[6]}However, the conventional ZF is of high complexity as a result of ignoring the structure of the FD massive MIMO channels while the 2D ZF has poor performance due to neglecting the elevation angular spread.

^{[7]}Moreover, to take into account the low-rank property of the millimeter-wave (mmWave) massive MIMO channels, we add a nuclear-norm based penalty term to the negative log-likelihood function and solve the resulting problem efficiently using the MM approach (referred to as 1bMM-LR).

^{[8]}Specifically, a Markov prior is used to model the temporal correlation in massive MIMO channels over different time slots.

^{[9]}After outlining the principle and functionality of the sounder, we present sample measurements that demonstrate the capabilities, and give first insights into air-to-ground massive MIMO channels in an urban environment.

^{[10]}Specifically, we design a low-complexity iterative channel estimation and tracking algorithm by fully exploiting the sparsity of mmWave massive MIMO channels, where the signal eigenvectors are estimated and tracked based on the received signals at the base station (BS).

^{[11]}More specifically, an a-priori channel model characterized by a multivariate Bernoulli–Gaussian distribution is invoked for exploiting the common sparsity of massive MIMO channels, and the VAMP technique is used for jointly estimating the spatially correlated channels.

^{[12]}Considering the channel hardening characteristics occurs in massive MIMO channels, this paper develops a novel distributed algorithm based on a daisy chain architecture, where the BS antennas are divided into clusters and each owns independent computing hardware for signal processing.

^{[13]}In this paper, an efficient hybrid beamforming architecture together with a novel spatio-temporal receiver processing is proposed for single-carrier (SC) mm-wave wideband massive MIMO channels in time-domain duplex (TDD) mode.

^{[14]}Our proposed protocol will able to resolve the channel collisions in a scalable and distributed process, though providing special properties of the massive MIMO channels.

^{[15]}On this basis, the authors investigated the massive multiple-input and multiple-output (MIMO) channel estimation algorithm using the complementary sequence as measurement matrix and obtained the compressed sensing (CS) signal model through the analysis on traditional massive MIMO channels.

^{[16]}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.

^{[17]}To statistically model the spatial non-stationary massive MIMO channels, a cluster-based channel model is proposed in this paper.

^{[18]}In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems.

^{[19]}First, exploiting the sparsity of massive MIMO channels and the timescale separation of channels and IQI, we derive a two-timescale sparse maximum a posterior (MAP) formulation for the joint estimation, where the IQI parameter is the long- term variable and the sparse channel is the short- term variable.

^{[20]}We propose a parallel factor (PARAFAC)-based estimation scheme, which exploits the low-rank property of massive MIMO channels caused by the finite scattering in a physical environment.

^{[21]}This paper proposes a burst-form estimation approach, referred to as the burst-form least squares (BFLS) algorithm, to fully utilize the burst-sparsity property of massive MIMO channels.

^{[22]}In this paper, realistic massive MIMO channels are evaluated both in single and multi-cell environments.

^{[23]}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.

^{[24]}In this letter, dense multipath component (DMC) modeling for massive MIMO channels is presented based on measurement campaigns in three different indoor scenarios.

^{[25]}Firstly, we propose a temporal Markov group-sparse (TMGS) model based on a grid reference to capture the probabilistic temporal correlation and group sparsity of the massive MIMO channels jointly.

^{[26]}CsiNet-LSTM considerably enhances recovery quality and improves tradeoff between compression ratio (CR) and complexity by directly learning spatial structures combined with time correlation from training samples of time-varying massive MIMO channels.

^{[27]}We present the closed-form expressions of the proposed beamformer and its performance by leveraging the favorable propagation condition of massive MIMO channels, and we prove that our proposed scheme can achieve the performance of fully digital zero-forcing when the number of employed phases shifter networks is twice the resolvable multipath components in the time domain.

^{[28]}The objective is to characterize both measured and simulated Massive MIMO channels and optimize this propagation model to ensure agreement between simulations and measurements.

^{[29]}In this work, we propose a low-overhead characteristic learning mechanism for the time-varying massive MIMO channels.

^{[30]}The massive MIMO channels exhibits sparse structure in the angular domain, due to limited scattering environment.

^{[31]}In this paper, we propose a pilot decontamination method based on a spatial filter, which exploits the spatial sparsity of massive MIMO channels.

^{[32]}Finally, our numerical and the simulation results demonstrate that the proposed model can capture features of realistic massive MIMO channels and can be matched well to existing literatures.

^{[33]}In this paper, we investigate the impact of the human body on indoor massive MIMO channels, using practically measured channel data for a 32x8 massive MIMO system in a complex office environment.

^{[34]}This paper analyzes the channel hardening and favorable propagation behavior of frequency-selective massive MIMO channels.

^{[35]}In this paper, an efficient hybrid precoding architecture is proposed for single-carrier (SC) downlink wideband spatially correlated massive MIMO channels.

^{[36]}Recently, compressive sensing has been applied to reduce the pilot overheads by exploiting the spatial and/or temporal correlation of massive MIMO channels.

^{[37]}Due to the spatially non-stationary characteristics in massive MIMO channels, the channel vectors corresponding to different BS antennas may take on different sparsity patterns in delay domain.

^{[38]}The results in this paper can help to reveal the propagation mechanisms in massive MIMO channels, and provide a foundation for the design and application of the practical massive MIMO system.

^{[39]}

## Fading Mimo Channels

We have found the explicit mathematical interpretation for the ergodic secrecy multicast capacity over Ricean fading MIMO channels with uniformly distributed linear antenna array employing the elegant Jakes correlation model.^{[1]}Moreover, a numerical simulation of the bound for fading MIMO channels is analyzed, at any SNR level, for a practical transmitter configuration employing a single power amplifier for all transmitting antennas.

^{[2]}In this paper, in order to evaluate the performance of STLC in practical transmission environment, we analyse the bit-error rate (BER) performance of STLC in the context of correlated Rayleigh and Rician fading MIMO channels.

^{[3]}Through the proposed manifold, dynamic mechanical quantities of the tradeoff considering the receiver mobility are formulated, and an application of the proposed model to Rayleigh fading MIMO channels is demonstrated.

^{[4]}

## Mmwave Mimo Channels

The DTL approach is evaluated using two 3D scenarios where ray-tracing is performed to generate the mmWave MIMO channels used in the simulations.^{[1]}doubly-selective mmWave MIMO channels, via the MMV sparse Kalman filtering-based SIP (MK-SIP) technique for tracking the CSI, and subsequently also to the joint Kalman filtering-based SIP (JK-SIP) for data-aided CSI acquisition.

^{[2]}We consider the design of analog and digital precoders utilizing statistical and/or mixed channel state information (CSI), which involve solving an extremely difficult problem in theory: First, designing the optimal partition of antennas over RF chains is a combinatorial optimization problem, whose optimal solution requires an exhaustive search over all antenna partitioning solutions; Second, the average mutual information under mmWave MIMO channels lacks closed-form expression and involves prohibitive computational burden; and Third, the hybrid precoding problem with given partition of antennas is nonconvex with respect to the analog and digital precoders.

^{[3]}The formulation takes into account of the energy focusing property of the lens antenna array as well as the mutipath sparsity in mmWave MIMO channels.

^{[4]}

## Selective Mimo Channels

The PGSVD can jointly factorize two frequency-selective MIMO channels, producing a set of virtual channels (VCs), split into: private channels (PCs) and common channels (CCs).^{[1]}While our prior work proposes a runtime-efficient error model in the context of Independent and Identically Distributed (IID) frequency-selective MIMO channels, here we consider the time-varying frequency-selective MIMO channels that correlate over time.

^{[2]}The receiver consists of separate stages for inter-symbol interference (ISI) and inter-antenna interference (IAI) mitigation in frequency selective MIMO channels.

^{[3]}

## Cell Mimo Channels

In this paper, We study the performance of zero forcing (ZF) and minimum mean square error (MMSE) ZF detection methods in Impulsive multi-cell MIMO channels.^{[1]}In this paper, We study the performance of zero forcing (ZF) and minimum mean square error (MMSE) ZF detection methods in Impulsive multi-cell MIMO channels.

^{[2]}

## Invariant Mimo Channels

For time-invariant MIMO channels, such as cables composed of many two-wire electrical lines or optical fibers, the delay spread function turns into the impulse response and just depends on the delay variable \(\tau \), which is then renamed as t.^{[1]}An estimator of the spatial correlation coefficient for frequency selective time invariant MIMO channels is proposed.

^{[2]}

## Overloaded Mimo Channels

The numerical simulations show that the proposed detector achieves a comparable detection performance to those of the existing algorithms for the massively overloaded MIMO channels, e.^{[1]}The numerical experiments show that TPG-detector achieves comparable detection performance to those of the known algorithms for massive overloaded MIMO channels with lower computation cost.

^{[2]}

## 3d Mimo Channels

And 3D MIMO channels can be projected onto the x - and y -directions, respectively.^{[1]}In outdoor propagation environment, 3D MIMO channels between closely located antennas share the same delay support in temporal domain.

^{[2]}

## Different Mimo Channels

We use velocity information gained from correlating different MIMO channels and fuse this with the rotation and acceleration data gained from a small integrated low-quality IMU.^{[1]}The maximum temperature, temperature uniformity and pressure drop characteristics of the U-shaped and Z-shaped cooling flow fields with different MIMO channels configurations are researched by computational fluid dynamics (CFD) method.

^{[2]}

## Paired Mimo Channels

We present new and improved Raymobtime datasets, which consist of paired MIMO channels and multimodal data.^{[1]}We present new strategies for obtaining paired MIMO channels and multimodal data.

^{[2]}