An Overview of Non.Stationary Property for Massive MIMO Channel Modeling

Massive multiple-input multiple-output(MIMO)emerges as one of the most promising technologies for 5G mobile communication systems.Compared to the conventional MIMO channel models,channel researches and measurements show that significant nonstationary properties rise in massive MIMO channels.Therefore,an accurate channel model is indispensable for the sake of massive MIMO system design and performance evaluation.This article presents an overview of methods of modeling non-stationary properties on both the array and time axes,which are mainly divided into two major categories:birth-death(BD)process and cluster visibility region(VR)method.The main concepts and theories are described,together with useful implementation guidelines.In conclusion,a comparison between these two methods is made.

Capacity analysis of high-rank line-of-sight MIMO channels

Matrix perturbation theory is utilized to investigate high-rank line of sight multiple input multiple output channels in a microwave relay system. The upper and lower bounds of channel capacity are derived based on space time block codes technique and singular values decomposition. A useful constraint for designing LOS MIMO channels is developed by the use of the condition number of the MIMO channel matrix. The theoretical analysis of channel capacity is confirmed by the simulation. The results show that the proposed method is able to give a physical explanation of the high-rank LOS MIMO channel matrix characteristics.

MIMO Channel Measurement and Characterization at 6.0-6.4 GHz Under Typical Classroom Environment

An extensive 4 × 4 MIMO channel measurement is carried out at 6. 0- 6. 4 GHz under a typical classroom environment with channel sounder based on vector network analyzer. Both LOS and NLOS scenarios are considered. The results on path loss,delay spread and spatial correlation are presented. The measurement shows that,for corridor coverage,2×2 MIMO is more economical than 4×4 MIMO due to high correlation. In order to identify the unique characteristics at the high frequency band,the measured channel parameters at 6. 0-6. 4 GHz are compared with those at 2. 45 GHz. The comparison shows that the shortened wavelength of this higher frequency band results in a great difference of channel characteristics. Therefore,our measurement results provide new guidance for the design and development of the system working on 6. 0-6. 4 GHz band.

3D Geometry-Based UAV-MIMO Channel Modeling and Simulation

A more general narrowband regular-shaped geometry-based statistical model(RS-GBSM) combined with the line of sight(LoS) and single bounce(SB) rays for unmanned aerial vehicle(UAV) multiple-input multiple-output(MIMO) channel is proposed in this paper. The channel characteristics, including space-time correlation function(STCF), Doppler power spectral density(DPSD), level crossing rate(LCR) and average fade duration(AFD), are derived based on the single sphere reference model for a non-isotropic environment. The corresponding sum-of-sinusoids(SoS) simulation models including both the deterministic model and statistical model with finite scatterers are also proposed for practicable implementation. The simulation results illustrate that the simulation models well reproduce the channel characteristics of the single sphere reference model with sufficient simulation scatterers. And the statistical model has a better approximation of the reference model in comparison with the deterministic one when the simulation trials of the stochastic model are sufficient. The effects of the parameters such as flight height, moving direction and Rice factor on the characteristics are also studied.

Eigenvalue Modeling and Cross-Correlation Analysis for 6. 0- 6. 4 GHz MIMO Channel

In Multiple-Input Multiple-Output( MIMO) system, the number of positive channel matrix eigenvalues is directly related to system performance. In order to characterize and model channel matrix eigenvalues,channel measurements at 6. 0- 6. 4GHz by using 4 × 4 MIMO structure were conducted in a typical classroom environment. Based on measured data, the eigenvalues were modeled as Log-Normal distributed random variables and parameterized. Furthermore, Cross-Correlation( CC) coefficients of eigenvalues were estimated. The measurement results show that,under both Light-Of-Sight( LOS) and NonLight-Of-Sight( NLOS) scenarios,eigenvalues are highly de-correlated so that CC can be ignored for eigenvalue model.

Lattice-reduction-aided MMSE precoding for correlated MIMO channels and performance analysis

The lattice-reduction （LR） has been developed to im- prove the performance of the zero-forcing （ZF） precoder in multiple input multiple output （MIMO） systems. Under the assumptions of uncorrelated flat fading channel model and perfect channel state information at the transmitter （CSIT）, an LR-aided ZF precoder is able to collect the full transmit diversity. With the complex Lenstra- Lenstra-Lov^sz （LLL） algorithm and limited feedforward structure, an LR-aided linear minimum-mean-square-error （LMMSE） pre- coder for spatial correlated MIMO channels and imperfect CSIT is proposed to achieve lower bit error rate （BER）. Assuming a time division duplexing （TDD） MIMO system, correlated block flat fad- ing channel and LMMSE uplink channel estimator, it is proved that the proposed LR-aided LMMSE precoder can also obtain the full transmit diversity through an analytical approach. Furthermore, the simulation results show that with the quadrature phase shift keying （QPSK） modulation at the transmitter, the uncoded and coded BERs of the LR-aided LMMSE precoder are lower than that of the traditional LMMSE precoder respectively when Eb-No is greater than 10 dB and 12 dB at all correlation coefficients.

Impact of Depolarization Phenomena on Polarized MIMO Channel Performances

The performance and capacity of multiple-input multiple-output (MIMO) wireless channels are limited by the spatial fading correlation between antenna elements. This limitation is due to the use of mono polarized antennas at receiver and transmitter sides. In this paper, in order to reduce the antenna correlation, the polarization diversity technique is employed. Although the spatial antenna correlation is attenuated for multi-polarization configurations, the cross-polar components appear. This paper highlights the impact of depolarization effect on the MIMO channel capacity for a 4×4 uniform linear antenna array. We assume that the channel is unknown at the transmitter and perfectly known at the receiver so that equal power is distributed to each of the transmit antennas. The numerical results illustrate that for low depolarization and spatial correlation, the capacity of single-polarization configuration behaves better than that of multi-polarization configuration.

VARIABLE-RATE MULTIUSER DIVERSITY IN CORRELATED MIMO CHANNEL VIA VIRTUAL CHANNEL REPRESENTATION

This paper studies the multiuser diversity with constellation selection based on a virtual representation of realistic Multiple Input Multiple Output (MIMO) correlated channels. To realize multiuser diversity in slow fading channels, random beamforming is adopted. Random beamforming matrix exploiting virtual channel representation is constructed, which can match the channel matrix of the desired user better. Simultaneously, adaptive coded modulation is applied to each sub-channel of the selected user to improve the system performance further.

Performance Improvement in Estimation of Spatially Correlated Rician Fading MIMO Channels Using a New LMMSE Estimator

In most of the previous researches on the multiple-input multiple-output (MIMO) channel estimation, the fading model has been assumed to be Rayleigh distributed. However, the Rician fading model is suitable for microcellular mobile systems or line of sight mode of WiMAX. In this paper, the training based channel es-timation (TBCE) scheme in the spatially correlated Rician flat fading MIMO channels is investigated. First, the least squares (LS) channel estimator is probed. Simulation results show that the Rice factor has no effect on the performance of this estimator. Then, a new linear minimum mean square error (LMMSE) technique, appropriate for Rician fading channels, is proposed. The optimal choice of training sequences with mean square error (MSE) criteria is investigated for these estimators. Analytical and numerical results show that the performance of proposed estimator in the Rician channel model compared with Rayleigh one is much better. It is illustrated that when the channel Rice factor and/or the correlation coefficient increase, the per-formance of the proposed estimator significantly improves.

基于AAT模型的毫米波大规模MIMO系统信道估计

针对毫米波大规模多输入多输出信道具有时间相关性、系统易受噪声因素影响导致信道估计精度低的问题,提出了一种基于改进的时序卷积神经网络信道估计方法。该方法将仿真获得的信道矩阵以二维图像数据方式输入系统;利用时间相关性进行特征融合,构建集中注意力机制网络,增强系统模型对信道深层特征的提取能力;将AAN嵌入时序卷积神经网络中进行训练;系统输出去噪后的二维图像,即信道估计矩阵。仿真结果表明,所提信道估计方法在性能和复杂度方面优于传统的信道估计方法,并且当测试场景发生改变时依旧具有鲁棒性。

基于CNN的毫米波无蜂窝大规模MIMO信道估计

针对小区间干扰导致蜂窝边缘无法满足不断增长的数据速率需求问题,毫米波无蜂窝大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统被认为是一种很有前途的解决方案。然而,毫米波的高频率、大带宽以及接入点配置的大量天线给信道估计带来了较大挑战。将毫米波大规模MIMO信道矩阵视为二维图像,结合图像去噪方法提出一种基于改进去噪卷积神经网络(Improved-Denoising Convolutional Neural Network,I-DnCNN)的信道估计算法。通过具有注意力机制的压缩与激励(Squeeze-and-Excitation,SE)模块,自适应调整提取的全局特征以增强对信道噪声特征的学习,根据接收信号估计出噪声等级图且增添为输入,提升对噪声的鲁棒性。最后,采用残差学习的方式获得估计信道矩阵。利用理论信道模型和基于波束追踪的信道数据集进行的仿真实验结果表明,与去噪卷积神经网络(Denoising Convolutional Neural Network,DnCNN)算法相比,所提算法在两个数据集下的信道估计精度可分别平均提升2.27 dB和2.60 dB。

融合残差SENet的毫米波大规模MIMO信道估计

在户外光线追踪场景下,针对毫米波大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统受户外环境噪声干扰导致估计精度低的问题,提出了一种融合残差挤压激励网络(Squeeze-and-Excitation Network,SENet)的条件生成对抗网络的信道估计方法。该方法采用条件生成对抗网络将低分辨率接收信号重建为高分辨率的原始信号完成信道估计,同时在生成器网络中引入SENet网络模块来抑制户外场景下显著性噪声干扰,提高估计精度;最后将残差网络中的残差块添加到SENet的放缩操作后,提高条件生成对抗网络的收敛速度。仿真结果表明,相较于正交匹配追踪算法、卷积神经网络、去噪卷积神经网络和条件生成对抗网络算法,所提方法在户外噪声环境下估计精度平均提高了约2.2 dB,且在高噪声强度下估计精度的提高更为显著。

低复杂度的大规模MIMO分布式信道估计

针对大规模多输入多输出(multiple-input multiple-output,MIMO)系统传统信道矩阵获取方式导频开销大、计算复杂度高的问题,提出了一种低复杂度的二阶段分布式信道估计方案。该方案的初始阶段在基站侧采用传统压缩感知算法恢复信道矩阵,第2阶段在用户端利用信道的时间相关性,将大规模MIMO的角度域信道分解为密集部分和稀疏部分,并分别估计以实现连续信道追踪。稀疏部分信道通过所提的分布式自适应弱匹配追踪(distributed adaptive weak matching pursuit,DAWMP)算法,利用子信道的联合稀疏性进行多维重建。相比于线性最小均方误差(linear minimum mean square error,LMMSE)算法,所提方案的信道分解策略有效减少了在用户端进行信道估计的计算复杂度。仿真结果表明,所提算法与经典压缩感知信道估计算法相比,计算复杂度降低了约33%,算法性能提升了约0.5 dB。

基于莱斯信道的大规模MIMO协作中继网络设计与优化

随着移动通信技术的不断发展,大规模多输入多输出(Multiple Input Multiple Output,MIMO)系统被广泛应用于提高网络容量和性能。而协作中继网络作为一种有效提高通信覆盖能力和网络容量的手段,也受到越来越多的关注。文章基于莱斯信道模型,针对大规模MIMO协作中继网络进行设计与优化,旨在提升网络的可靠性和传输效率。通过对信道特性的分析和系统参数的优化,可以实现更好的性能表现。

基于AI通信的大规模MIMO信道状态信息反馈网络

在大规模多输入多输出系统中,由于天线数量的增加导致信道状态信息反馈带宽开销增大。为了减少反馈开销,提出了一种基于深度学习的反馈网络。该网络将卷积注意力模块和快速迭代收缩阈值算法(Fast Iterative Shrinkage Thresholding Algorithm,FISTA)进行了结合。为了贴合实际应用,考虑到了噪声情况,分析了阈值敏感度。仿真结果表明,该网络在不同环境下其性能和鲁棒性可以得到进一步提高。

基于Transformer大规模MIMO的CSI反馈网络

针对频分双工大规模多输入多输出系统中信道状态信息反馈方法反馈精度低的问题,提出了一种名为CDTransformer的基于Transformer和卷积分解(convolutional decomposition,CD)的CSI反馈网络。CDTransformer将卷积分解融入改进的Transformer结构网络中,在不增加计算复杂度的情况下提升网络性能,并通过对网络的全连接层进行二值化实现轻量级部署。针对用户端功率有限的情况,提出了一种MixedTransformer网络模型。其中编码器采用计算成本较低、结构简单的单层卷积神经网络,而解码器则采用与CDTransformer模型相同的结构。CDTransformer模型融合了Transformer结构和卷积分解,提高了CSI反馈精度并实现轻量级部署。此外,引入了MixedTransformer模型,结合了CDTransformer和卷积神经网络的优点,以在功率有限情况下提供更好的性能。结果显示,相比于CsiTransformer网络模型,CDTransformer网络模型在归一化均方误差和余弦相似度方面分别提高了37.7%和0.2%。

太赫兹MIMO系统中基于SRCGAN的空时频信道估计方案

为了能有效利用THz-MIMO系统的多维信道特性,提出一种基于SRCGAN的THz-MIMO系统信道估计方案。在该方案中,由预估计模块获得的初始空时域信道响应矩阵被视作一张二维的低分辨率图像,利用SRCGAN网络提取太赫兹信道的空时特性进行空时域信道补全获得完整的信道信息,然后相邻子载波之间的频率相关性作为SRGAN提供的条件信息提升信道估计精度。为了增强SRCGAN网络对时变信道预测的鲁棒性,在线上估计阶段,基于最大均方误差准则采用梯度下降算法对输入的预估计信道信息矩阵进行迭代更新。仿真结果证明了基于SRCGAN的空时频信道估计方案性能的优越性,以及利用信道“空时频”的相关性提升估计精度的有效性。

无蜂窝大规模MIMO中的大规模随机接入

研究了以用户为中心的无蜂窝大规模多输入多输出(MIMO)架构下的大规模随机接入方案。为实现可扩展架构,讨论了接入点(AP)与用户设备(UE)的关联以及AP的分簇方法。针对活跃用户检测(AUD),一种基于最大似然检测(ML)的方案被提出以获取活跃用户。通过调整阈值,可以得到不同精度的检测结果。利用AUD的检测用户集合,系统利用基于狄利克雷过程的稀疏贝叶斯学习(DP-SBL)完成信道估计(CE)。该算法可以充分利用AP的空间聚散特性,提高系统准确性。基于以上工作,我们提出了联合AUD和CE算法。仿真结果验证了所提方案在性能上的优越性。

一种基于联合加权和截断的毫米波大规模MIMO信道估计

提出一种联合加权和截断核范数的毫米波大规模多输入多输出(MIMO)信道估计算法。针对毫米波大规模MIMO信道估计问题中训练和反馈开销大的问题,首先利用毫米波信道天线角度域稀疏的特性,把信道估计问题转化为低秩矩阵恢复问题。采用一种有效而灵活的秩函数——联合加权截断核范数作为核范数的松弛,构造出一种新的矩阵恢复模型用于信道估计问题,以最小化加权截断核范数为优化目标,并利用交替优化框架求解。仿真结果表明,该方法可以有效地提高信道估计的精度,并且具有可靠的收敛性。

采用通道注意力机制的RIS辅助MIMO级联信道估计

可重构智慧表面(RIS)辅助多天线毫米波无线通信系统的级联信道有直传和反射两条链路,系统维数大且复杂,获取信道状态信息(CSI)困难.针对RIS辅助下的大规模多输入多输出(MIMO)通信系统应用场景,提出了基于通道注意力机制的SE-ResNetV2网络来获取CSI,在残差网络(ResNet)中引入了通道注意力模块,通过挤压和激励策略来提高噪声特征的捕捉能力.仿真结果表明,相比于最小二乘(LS)估计算法和常规的注意力机制深度ResNet,所提出的深度学习网络具有更好的去噪效果和估计精度.