多用户MIMO系统最优发送策略研究

研究一个收发双方都采用多天线的K用户MIMO系统的前向链路的几种最优发送策略.利用矢量广播信道和矢量多接入信道之间的对偶性交换两类信道的发送协方差矩阵以达到快速优化;分析了在各用户功率受限时总的信道容量最大的算法;研究在假定基站采用理想的线性多用户MMSE接收时的最优发送.在总功率受限时通过调整各用户的协方差矩阵实现平均标准MSE最优,采用自适应功率分配可以进一步优化MSE.分析最优化问题与KKT条件的关系,通过迭代计算单用户平均最小均方误差,利用内点法计算互协方差矩阵优化问题.     

MIMO-OFDM系统无线衰落信道容量分析

推导了MIMO-OFDM系统在衰落信道下的各态历经容量、最优发送策略、使用等功率分配时的容量上界以及相对于单天线OFDM系统的容量增益。结果表明：天线数和平均接收信噪比是决定MIMO-OFDM系统信道容量的关键因素。天线数越多或者接收信噪比越大,信道的容量越大,信道容量几乎不受多径时延扩展的影响。慢衰落信道下的最大信道容量可以使用空-频两维注水算法得到,当接收信噪比足够大时,最大信道容量也可以用平均分配发送功率的方法逼近。     

正交中继信道的信道容量及资源分配

 本文研究了一种正交中继信道的信道容量及资源分配问题。其中,源节点到中继节点之间的信道与源节点和中继节点到目的节点之间的信道在时间上相互正交。论文首先求出了系统的信道容量上界及下界,且中继策略为部分译码-转发时,上界和下界相等,从而给出了信道容量。对于高斯正交中继信道,为了最大化信道容量,论文还研究了各种系统资源的优化问题,包括时间、功率等。仿真结果表明,仅对信道的时间分配参数进行优化与优化所有的参数相比,信道容量损失很小,且给出了此时最优时间分配的解析解。     

频率选择性衰落MIMO信道容量分析

随着传输速率越来越高,信号所占用的带宽越来越宽,实际信道呈现出频率选择性衰落特性.根据频率选择性衰落信道特性,建立了频率选择性衰落MIMO信道模型,采用频域和时域的方法研究了在频率选择性衰落下,发射端已知和未知信道状态信息的MIMO信道容量,并给出了相关的仿真结果.研究结果表明:在相同发射功率和传输带宽下,MIMO系统可以不牺牲信号带宽而显著提高信道的容量,并且天线数量和输入信噪比的大小对信道容量具有不同的影响.研究结果为如何提高频率选择性衰落环境下MIMO信道容量提供了依据.     

一种适用于下行多用户MIMO信道的预处理方法

在研究了多用户MIMO下行信道矩阵的基础上,本文提出了一种基于信道矩阵奇异值分解的多用户预处理方法。该方法将预编码矩阵分解为一些特殊矩阵的加权和。利用这种分解方法,本文研究了信道反转(CI)方法和规则化反转(RCI)方法,结果显示这两种方法均是本文提出的方法的特殊解。在发射功率约束条件下,本文给出了基于最大平均信道容量准则的迭代的参数优化方法。计算机仿真结果显示,本文提出的方法具备了较好的性能:相对于CI和RCI算法,有效提升了系统的平均信道容量和中断容量并且能够更好的处理远近效应问题。     

一种基于无人机MIMO信道的容量分析方法

针对无人机通信环境特点以及多天线系统(Multi-Input Multi-Output,MIMO),建立了具有直射、反射以及散射分量的无人机MIMO三维GBSBCM信道模型,给出了无人机空时频信道相关函数的简要形式;采用信道矩阵分解、信道系数归一化的方法,推导了无人机的MIMO信道相关矩阵的计算方法;根据信道相关矩阵中参数定量分析法,仿真分析了在发射端信道未知条件下的多天线间隔对无人机MIMO信道容量的影响。通过容量的影响分析,对无人机MIMO天线间隔的合理配置具有一定的理论参考价值和实际应用价值。     

相关Rician衰落信道下紧凑MIMO系统的容量分析

研究了Rician衰落信道下采用紧凑型双偶极子阵列的MIMO系统容量,建立了相关Rician衰落信道下紧凑MIMO系统的模型,深入分析了天线互耦、空间相关性、功率分配方案以及信道衰落环境对信道容量的影响。数值结果表明天线间距较小时,互耦和相关性会导致信道容量的损失,而在某些情况下互耦和相关性的影响会使信道容量有所增加。     

在相关信道下MIMO-OFDM信道容量的分析

利用中心Wishart矩阵的性质得到相关信道下的多输入/输出正交频分复用系统(Multiple Input Multiple out-put;Orthogonal Frequency Division Multiplexing,MIMO OFDM)的信道容量的上下界限,同时分析天线相关对信道容量的影响。假设接收端有完全的信道状态信息(CSI),而发射端没有任何的CSI的情况下得到了信道容量的上下界限,通过仿真说明得到的界限是紧凑的,同时说明信道相关如何降低信道容量。     

基于MIMO几何信道模型的容量界研究

MIMO技术是LTE的最重要技术之一。为了研究MIMO系统中信道容量界限,提出了基于双反射的几何信道模型。基于此模型,推导了信道相关系数及信道容量的表达式,证明了收发相关的MIMO信道容量上、下界。MonteCarlo信道容量仿真结果表明,MIMO信道容量上界在低信噪比的条件下具有紧性;而在高信噪比的条件下,MIMO信道容量的下界具有紧性。     

TD—LTE与LTEFDD下行控制信道容量对比分析

LTE系统容量包括业务信道容量和控制信道容量两个方面,而业务信道容量受到控制信道配置的影响。文章详细分析了TD-LTEZ与LTEFDD制式TPCFICH、PHIcH及pDccH信道参数配置及系统容量,重点对LTE两种制式下行控制信道用户调度能力进行对比分析,为LTE容量规划及参数优化调整提供依据。     

Optimal transmission strategies and impact of correlation in multiantenna systems with different types of channel state information

We study the optimal transmission strategy of a multiple-input single-output (MISO) wireless communication link. The receiver has perfect channel state information (CSI), while the transmitter has different types of CSI, i.e., either perfect CSI, or no CSI, or long-term knowledge of the channel covariance matrix. For the case in which the transmitter knows the channel covariance matrix, it was recently shown that the optimal eigenvectors of the transmit covariance matrix correspond with the eigenvectors of the channel covariance matrix. However, the optimal eigenvalues are difficult to compute. We derive a characterization of the optimum power allocation. Furthermore, we apply this result to provide an efficient algorithm which computes the optimum power allocation. In addition to this, we analyze the impact of correlation on the ergodic capacity of the MISO system with different CSI schemes. At first, we justify the belief that equal power allocation is optimal if the transmitter is uninformed and the transmit antennas are correlated. Next, we show that the ergodic capacity with perfect CSI and without CSI at the transmitter is Schur-concave, i.e., the more correlated the transmit antennas are, the less capacity is achievable. In addition, we show that the ergodic capacity with covariance knowledge at the transmitter is Schur-convex with respect to the correlation properties. These results completely characterize the impact of correlation on the ergodic capacity in MISO systems. Furthermore, the capacity loss or gain due to correlation is quantified. For no CSI and perfect CSI at the transmitter, the capacity loss due to correlation is bounded by some small constant, whereas the capacity gain due to correlation grows unbounded with the number of transmit antennas in the case in which transmitter knows the channel covariance matrix. Finally, we illustrate all theoretical results by numerical simulations.     

Multiple-antenna capacity in correlated Rayleigh fading with channel covariance information

We analyze a mobile multiple input multiple output wireless link with M transmit and N receive antennas operating in a spatially correlated Rayleigh flat fading environment. Only the correlations between the channel coefficients are assumed to be known at the transmitter and the receiver. The channel coefficients are correlated in space and uncorrelated in time from one coherence interval to another. These coefficients remain constant for a coherence interval of T symbol periods after which they change to another independent realization according to the spatial correlation model. For this system we characterize the structure of the input signal that achieves capacity. The capacity achieving transmit signal is expressed as the product of an isotropically distributed unitary matrix, an independent nonnegative diagonal matrix and a unitary matrix whose columns are the eigenvectors of the transmit fade covariance matrix. For the case where the number of transmit antennas M is larger than the channel coherence interval T, we show that the channel capacity is independent of the smallest M-T eigenvalues of the transmit fade covariance matrix. In contrast to the previously reported results for the spatially white fading model where adding more transmit antennas beyond the coherence interval length (M>T) does not increase capacity, we find that additional transmit antennas always increase capacity as long as their channel fading coefficients are spatially correlated with the other antennas. We show that for fast hopping or fast fading systems (T=1) with only channel covariance information available to the transmitter and receiver, transmit fade correlations are beneficial. Mathematically, we prove this by showing that capacity is a Schur-convex function of the vector of eigenvalues of the transmit fade correlation matrix. We also show that the maximum possible capacity gain due to transmitter fade correlations is 10logM dB.     

MIMO Channel Capacity and Configuration Selection for Switched Parasitic Antennas

Multiple-input multiple-output (MIMO) systems offer significant enhancements in terms of their data rate and channel capacity compared to traditional systems. However, correlation degrades the system performance and imposes practical limits on the number of antennas that can be incorporated into portable wireless devices. The use of switched parasitic antennas (SPAs) is a possible solution, especially where it is difficult to obtain sufficient signal decorrelation by conventional means. The covariance matrix represents the correlation present in the propagation channel, and has significant impact on the MIMO channel capacity. The results of this work demonstrate a significant improvement in the MIMO channel capacity by using SPA with the knowledge of the covariance matrix for all pattern configurations. By employing the “water-pouring algorithm” to modify the covariance matrix, the channel capacity is significantly improved compared to traditional systems, which spread transmit power uniformly across all the antennas. A condition number is also proposed as a selection metric to select the optimal pattern configuration for MIMO-SPAs.     

On the ergodic capacity as a function of the correlation properties in systems with multiple transmit antennas without CSI at the transmitter

In this letter, the impact of correlation of the transmit antennas of a multiple-input single-output (MISO) system, with no channel state information (CSI) at the transmitter and perfect CSI at the receiver is analyzed. We show that the ergodic capacity for the single-user MISO system is Schur-concave with respect to the vector with eigenvalues of the channel covariance matrix, i.e., the more correlation that exists between the transmit antennas, the less is the achievable capacity. Furthermore, the capacity loss for fully correlated transmit antennas in comparison with the uncorrelated case is derived. The results for the ergodic capacity are compared with the impact of correlation on the outage probability. The relationship between correlation properties and outage probability is more complicated than the relationship between the correlation properties and the ergodic capacity. It is shown that the outage probability is Schur-convex in the high signal-to-noise ratio (SNR) regime, and Schur-concave in the low SNR regime.     

Transmitter optimization and optimality of beamforming for multiple antenna systems

We solve the transmitter optimization problem and determine a necessary and sufficient condition under which beamforming achieves Shannon capacity in a linear narrowband point-to-point communication system employing multiple transmit and receive antennas with additive Gaussian noise. We assume that the receiver has perfect channel knowledge while the transmitter has only knowledge of either the mean or the covariance of the channel coefficients. The channel is modeled at the transmitter as a matrix of complex jointly Gaussian random variables with either a zero mean and a known covariance matrix (covariance information), or a nonzero mean and a white covariance matrix (mean information). For both cases, we develop a necessary and sufficient condition for when the Shannon capacity is achieved through beamforming; i.e., the channel can be treated like a scalar channel and one-dimensional codes can be used to achieve capacity. We also provide a waterpouring interpretation of our results and find that less channel uncertainty not only increases the system capacity but may also allow this higher capacity to be achieved with scalar codes which involves significantly less complexity in practice than vector coding.     

MIMO Precoder Designs for Frequency-Selective Fading Channels Using Spatial and Path Correlation

Multiple-input–multiple-output (MIMO) precoder design for frequency-selective fading channels using partial channel information based on the spatial and path correlation matrices is presented. By representing a frequency-selective fading channel as a multipath model with $L$ effective paths, a general precoding structure is proposed and used to derive optimum precoding designs that maximize Jensen's upper bound on the channel ergodic capacity under the transmitted power constraint for two cases, i.e., uncorrelated and correlated channel paths. Analytical results show that, in the uncorrelated case, the precoder structure consists of a number of parallel precoders for frequency-flat fading channels. The power assignment to each precoder and the power allocation over the eigenmodes of each precoder are calculated based on the power of channel paths and the eigenvalues of the transmit correlation matrix. In the correlated case, the precoder structure is an eigenbeamformer with the beams referred to a function of eigenvectors of the Kronecker product of path and transmit correlation matrices. Furthermore, the power allocated to each eigenmode can be obtained from a statistical water-pouring policy that is specified by the product of eigenvalues of the transmit antenna and path correlation matrices. Simulation results for different scenarios indicate that the proposed precoder can increase the ergodic capacity of MIMO systems in a frequency-selective fading environment with spatial and path correlations, and its offered capacity gain is increased with the level of correlation and numbers of antennas and channel paths.      

二进制猫群算法在大规模MIMO天线选择中的应用

在多输入多输出(MIMO)系统中,天线选择技术平衡了系统的性能和硬件开销,但大规模MI-MO系统收发端天线选择复杂度问题一直没有得到很好的解决.基于信道容量最大化的准则,采用两个二进制编码字符串分别表示发射端和接收端天线被选择的状态,提出将二进制猫群算法(BCSO)应用于多天线选择中,以MIMO系统信道容量公式作为猫群的适应度函数,将收发端天线选择问题转化为猫群的位置寻优过程.建立了基于BCSO的天线选择模型,给出了算法的实现步骤.仿真结果表明所提算法较之于基于矩阵简化的方法、粒子优化算法具有更好的收敛性和较低的计算复杂度,选择后的系统信道容量接近于最优算法,非常适用于联合收发端天线选择的大规模MIMO系统中.     

Diversity eigenbeamforming for coded signals

Transmit antenna diversity without feedback has been widely investigated for various wireless communication systems. Especially, space-time codes are extensively studied to exploit the spatial diversity induced by multiple transmit antennas. Apart from these approaches, there can be different methods that provide a diversity gain using multiple antennas with conventional channel coding. In this paper, diversity eigenbeamforming is studied to exploit the spatial diversity when the channel covariance matrix is available at the transmitter. Diversity eigenbeamforming is based on eigenmode (of the spatial covariance matrix) switching that converts the spatial diversity into the temporal diversity which is exploited by channel coding. Optimized diversity eigenbeamforming is considered to take into account the spatial correlation. In addition, the trade-off between diversity and multiplexing gains is addressed. It is shown that the diversity eigenbeamforming can achieve optimal trade-off for the case of one receive antenna. Although, for simplicity, binary phase shift keying (BPSK) is used to derive diversity eigenbeamforming, any higher-order modulation scheme can also be used with diversity eigenbeamforming.     

Correlated MIMO wireless channels: capacity, optimal signaling, and asymptotics

The capacity of the multiple-input multiple-output (MIMO) wireless channel with uniform linear arrays (ULAs) of antennas at the transmitter and receiver is investigated. It is assumed that the receiver knows the channel perfectly but that the transmitter knows only the channel statistics. The analysis is carried out using an equivalent virtual representation of the channel that is obtained via a spatial discrete Fourier transform. A key property of the virtual representation that is exploited is that the components of virtual channel matrix are approximately independent. With this approximation, the virtual representation allows for a general capacity analysis without the common simplifying assumptions of Gaussian statistics and product-form correlation (Kronecker model) for the channel matrix elements. A deterministic line-of-sight (LOS) component in the channel is also easily incorporated in much of the analysis. It is shown that in the virtual domain, the capacity-achieving input vector consists of independent zero-mean proper-complex Gaussian entries, whose variances can be computed numerically using standard convex programming algorithms based on the channel statistics. Furthermore, in the asymptotic regime of low signal-to-noise ratio (SNR), it is shown that beamforming along one virtual transmit angle is asymptotically optimal. Necessary and sufficient conditions for the optimality of beamforming, and the value of the corresponding optimal virtual angle, are also derived based on only the second moments of the virtual channel coefficients. Numerical results indicate that beamforming may be close to optimum even at moderate values of SNR for sparse scattering environments. Finally, the capacity is investigated in the asymptotic regime where the numbers of receive and transmit antennas go to infinity, with their ratio being kept constant. Using a result of Girko, an expression for the asymptotic capacity scaling with the number of antennas is obtained in terms