Mimo Channels(咪咪频道)研究综述
Mimo Channels 咪咪频道 - 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]然而，瞬时信噪比因信念而异，因为 MUD 问题中的 MIMO 信道是随机的；因此，LUT的创建是不可行的。 [1] 其次，利用MIMO信道的时空公共稀疏特性和获得的PCCS信息，我们提出了先验信息辅助分布式结构化稀疏自适应匹配追踪（PA-DS-SAMP）算法，以实现在频域上的准确信道估计。 [2] 对空间和时间相关的 UAV-MIMO 信道进行了全面的容量研究。 [3] 此外，使用所提出的基于几何的相关模型研究了 MIMO 信道的容量性能。 [4] 鉴于在高 SNR 的限制情况下，MIMO 信道的 DMT 由其概率密度函数 [1] 的尾部行为决定，并且基于对非常小的 σx« 的对数正态和正态随机变量的尾部行为之间的相似性1 dB，以及小到中等 σx ≤ 1 的对数正态和 Gamma 随机变量。 [5] 然而，现有的工作很少集中在毫米波 (mmWave) 视距 (LOS) MIMO 信道中的 URA。 [6] 提出了一种基于 SIP 的方法，利用接收信号的一阶统计量来估计频率选择性稀疏大规模 MIMO 信道。 [7] 由于真正的高数据传输率，已经有许多关于通过 MIMO 信道的块传输系统的研究。 [8] 我们的贡献在于 (1) 制定矩阵设计问题，使得为 MIMO 信道提出的多级注水解决方案可以适应 LVC； (2) 提出了三种次优功率分配技术，在复杂性和效率之间进行了不同的权衡； (3) 多接收机场景的预编码矩阵设计； (4) 功率分配方法的广泛模拟。 [9] 对 MIMO 信道的扩展由 I 给出。 [10] 它可以用来提供一个实用的标准或阈值标准，分别用于选择或消除一些质量高或低的 MIMO 信道。 [11] 因此，取决于所使用的 MIMO 阵列几何形状，SAR 测量时间可以减少多达 MIMO 通道的数量。 [12] 本文旨在对非视距 (NLOS) 条件下室内环境中 MIMO 信道的混合分集（空间和极化）性能进行分析。 [13] 我们证明了由单个方程组成的线性约束总是消除信道识别问题中的单个自由度，因为预先知道这些约束允许识别支持大小大于基本限制的 MIMO 信道。 [14] 对于无人机（UAV）多输入多输出（MIMO）通信系统的实际应用和准确分析，提出相应的非各向同性散射UAV-MIMO信道仿真模型具有重要意义。 [15] 本文的作者还评估了 MIMO 信道的两个重要的二阶统计性能指标，即电平交叉率 (LCR) 和平均衰落持续时间 (AFD)。 [16] 此外，为了获得上行链路中信道状态信息 (CSI) 的估计，通过结合基于 CS 的正交匹配追踪 (OMP) 算法来利用 MIMO 信道表现出的稀疏性。 [17] 所提出的预编码器实现了两个用户的 MIMO 信道的同时对角化，假设接收器之一处的自干扰消除，从而降低了整体解码复杂度。 [18] 由于我们的分析考虑了宽带信道，因此在每种情况下都执行了 MIMO 信道到抽头延迟线的时域分解。 [19] 使用 MIMO 信道的平坦度因子，我们提出了格码，可以普遍实现复合 MIMO 窃听信道的保密能力，直到等于发射天线数量的恒定间隙（以 nat 为单位）。 [20] 在本文中，对 MIMO 信道的中断概率进行了渐近研究，以深入研究传输可靠性。 [21] 根据这些值，我们可以预测实际传输时间的 MIMO 信道。 [22] 该提案允许异构无线系统共享它们的天线，在它们之间动态分配共享天线，并增加它们的 MIMO 信道容量之和，并通过绑定它们的 MIMO 信道来增加应用程序吞吐量。 [23] 混合预编码已成为一种广为接受的方法，用于提高不利的毫米波 (mm-Wave) MIMO 信道中的可实现速率。 [24] 通过考虑衰落参数和不完美 CSI 对 MIMO 信道的影响，从各种 QAM 方案的比较 ASER 分析中得出有用的见解。 [25] 有几种模型努力匹配 M-MIMO 信道中的空间相关性，指数相关性模型就是其中之一。 [26]
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]在本文中，我们研究了在脉冲多小区 MIMO 信道中的迫零 (ZF) 和最小均方误差 (MMSE) ZF 检测方法的性能。 [1] 在本文中，我们研究了在脉冲多小区 MIMO 信道中的迫零 (ZF) 和最小均方误差 (MMSE) ZF 检测方法的性能。 [2] nan [3]
Massive Mimo Channels 海量 Mimo 频道
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]可以提取 mmWave 大规模 MIMO 信道固有的稀疏特征，并且可以通过基于 CNN 的多层网络通过训练来学习稀疏信道支持。 [1] 仿真结果表明，所提出的信道估计方案可以在几个训练时隙内有效地估计相关的大规模 MIMO 信道。 [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] 更具体地说，调用以多元伯努利-高斯分布为特征的先验信道模型来利用大规模 MIMO 信道的共同稀疏性，并使用 VAMP 技术来联合估计空间相关信道。 [12] 考虑到大规模 MIMO 信道中存在信道硬化特性，本文开发了一种基于菊花链架构的新型分布式算法，其中基站天线被划分为集群，每个集群拥有独立的计算硬件进行信号处理。 [13] nan [14] nan [15] nan [16] nan [17] nan [18] nan [19] nan [20] nan [21] nan [22] nan [23] nan [24] nan [25] nan [26] nan [27] nan [28] nan [29] nan [30] nan [31] nan [32] nan [33] nan [34] nan [35] nan [36] nan [37] nan [38] nan [39]
Fading Mimo Channels 衰落的 Mimo 频道
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]我们已经找到了在具有均匀分布的线性天线阵列的 Ricean 衰落 MIMO 信道上的遍历保密多播容量的明确数学解释，该天线阵列采用优雅的 Jakes 相关模型。 [1] 此外，在任何 SNR 水平下，针对所有发射天线使用单个功率放大器的实际发射机配置，分析了衰落 MIMO 信道界限的数值模拟。 [2] nan [3] nan [4]
Mmwave Mimo Channels 毫米波 Mimo 频道
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]DTL 方法使用两个 3D 场景进行评估，其中执行光线追踪以生成模拟中使用的毫米波 MIMO 通道。 [1] 双选择性毫米波 MIMO 通道，通过基于 MMV 稀疏卡尔曼滤波的 SIP (MK-SIP) 技术来跟踪 CSI，随后还通过基于联合卡尔曼滤波的 SIP (JK-SIP) 进行数据辅助 CSI 采集。 [2] 我们考虑利用统计和/或混合信道状态信息 (CSI) 设计模拟和数字预编码器，这涉及在理论上解决一个极其困难的问题：首先，在 RF 链上设计天线的最佳分区是一个组合优化问题，其最佳解决方案需要对所有天线分区解决方案进行详尽搜索；其次，mmWave MIMO 信道下的平均互信息缺乏封闭式表达，计算负担过重；第三，给定天线分区的混合预编码问题对于模拟和数字预编码器是非凸的。 [3] 该公式考虑了透镜天线阵列的能量聚焦特性以及毫米波 MIMO 信道中的多径稀疏性。 [4]
Selective Mimo Channels 选择性 Mimo 频道
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]PGSVD 可以联合分解两个频率选择性 MIMO 信道，生成一组虚拟信道 (VC)，分为：专用信道 (PC) 和公共信道 (CC)。 [1] 虽然我们之前的工作在独立和相同分布 (IID) 频率选择性 MIMO 信道的背景下提出了一种运行时高效的误差模型，但这里我们考虑随时间相关的时变频率选择性 MIMO 信道。 [2] nan [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]在本文中，我们研究了在脉冲多小区 MIMO 信道中的迫零 (ZF) 和最小均方误差 (MMSE) ZF 检测方法的性能。 [1] 在本文中，我们研究了在脉冲多小区 MIMO 信道中的迫零 (ZF) 和最小均方误差 (MMSE) ZF 检测方法的性能。 [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]对于时不变的 MIMO 信道，例如由许多两线电线或光纤组成的电缆，延迟扩展函数变为脉冲响应，仅取决于延迟变量 \(\tau \)，然后将其重命名为吨。 [1] 提出了一种频率选择性时不变MIMO信道的空间相关系数估计器。 [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]数值模拟表明，所提出的检测器实现了与大规模过载 MIMO 信道的现有算法相当的检测性能，例如。 [1] 数值实验表明，TPG-detector 的检测性能与已知的大规模过载 MIMO 信道算法具有相当的检测性能，且计算成本较低。 [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 不同的 Mimo 频道
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]我们使用通过关联不同 MIMO 通道获得的速度信息，并将其与从小型集成低质量 IMU 获得的旋转和加速度数据融合。 [1] 采用计算流体动力学（CFD）方法研究了不同MIMO通道配置的U型和Z型冷却流场的最高温度、温度均匀性和压降特性。 [2]
Paired Mimo Channels 配对的 Mimo 频道
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]我们提出了新的和改进的 Raymobtime 数据集，其中包括成对的 MIMO 通道和多模态数据。 [1] 我们提出了获取配对 MIMO 通道和多模态数据的新策略。 [2]