基于联合用户分组及天线选择的大规模MIMO波束成形

大规模MIMO系统中,当小区用户数与基站天线数较大时,各用户的信道条件不尽相同,提出一种适用于大规模MIMO下行链路的基于联合用户分组及天线选择的迫零波束成形算法。将用户分成两组,选择信道条件较优的一组用户来接收信号,并为每一个发送数据流选择最优的基站天线组合进行通信,以较小的性能损失,换取大规模MIMO 射频电路的成本与功耗的大幅度降低。仿真结果证明,该算法能够较好地实现系统性能与硬件复杂度的折中。     

基于频谱效率的低复杂度自适应MIMO系统

在一定的误比特率(BER)下,使用自适应MQAM调制可以最大化MIMO系统的频谱效率。通过不同的算法可以得到自适应调制MIMO系统的离散速率频谱效率(DRSE)。为了进一步提高频谱效率,提出了一种基于DRSE,在不同的算法之间进行切换的低复杂度自适应机制。研究了在两种2×2信道下即:独立同分布(i.i.d)的平坦瑞利衰落信道和空间相关的瑞利衰落信道,自适应MIMO系统在正交空时块编码(OSTBC)和空间复用(SM)之间切换的自适应机制。仿真结果表明,通过选择一种最优的调制模式及发射模式的组合方式,此切换算法可以使系统的频谱效率得到有效提高,同时只增加有限的反馈信息,降低了系统复杂度。     

用于有限反馈MIMO系统中的统计多模发射天线选择算法

利用多输入多输出（MIMO）系统中的发射端天线选择算法,可以在降低复杂度的同时,有效地提高系统性能。考虑到基于线性接收机的空间复用系统,在通过已选择的发射天线上采用等功率分配等增益传输可以减少反馈信息量。因此,文章提出了空间相关衰落信道下可提升系统容量的统计多模发射天线选择方法。     

分布式MIMO中的最大似然频偏估计

在考虑分布式多入多出(MIMO)系统中各收发天线对之间的频偏均可能不同的一般情况下,推导出了平坦衰落MIMO信道模型下的最大似然频偏估计.针对收端是否采用分布式天线的两种不同情况,分别提出了一种只涉及一维最大化问题的较低复杂度频偏估计方法.仿真结果表明,所提频偏估计方法在平坦衰落MIMO信道下可以获得令人满意的估计性能;而在收端仍采用集中式天线这一特殊情况下,利用所有接收天线上的接收信号进行联合估计,可以进一步提高估计性能.     

天线匹配对莱斯信道下紧凑MIMO系统性能的影响

深入研究了存在天线互耦和空间相关时,天线匹配网络对莱斯信道下多输入多输出MIMO系统性能的影响,建立了包含相关莱斯信道、天线互耦和匹配网络的(MIMO)系统模型.通过仿真分析了匹配阻抗、天线互耦、空间相关性以及信道衰落环境对包络相关性和信道容量的影响.实验结果表明:莱斯信道下匹配阻抗对MIMO系统的影响与瑞利信道情况下有所不同,适当地选择天线匹配阻抗可以改善MIMO系统的性能.     

MIMO系统中球形解码算法性能仿真比较

球形解码(Sphere Decoder,SD)算法能以较低的复杂度实现多输入多输出(Multiple Input Multiple Output,MIMO)系统的最优检测,是当前受到普遍关注的MIMO检测算法。对当前球形解码的主要研究成果进行综述,根据搜索策略进行分类,重点分析基于深度优先策略的VB、CL和基于宽度优先策略的K-Best、FSD算法,并且讨论了几种初始半径的选择方法,最后在准静态平坦瑞利衰落环境下对上述算法进行了性能仿真比较。     

大规模MIMO时分双工系统的基站天线互易校准算法

对于采用大规模MIMO技术的时分双工系统,基站天线的互易误差会破坏上下行基带信道互易特性,大幅降低系统下行传输性能。考虑到大规模MIMO技术所带来的基站天线间的耦合效应,该文设计了基于总体最小二乘估计的基站天线互易校准算法,以实现对基站的天线互易误差的补偿。该算法以增加计算复杂度为代价,以及通过增加信道测量样本,克服了上下行信道估计误差对现有天线互易误差校准算法的影响。同时,该文通过瑞利商迭代求解降低了该算法的复杂度。若忽略用户天线互易误差,计算机仿真结果表明,该算法相对于现有的基站天线互易误差校正算法,具有1.8 dB左右的性能增益。若考虑用户天线互易误差,该算法相对于已有的算法,具有随信道估计误差方差减小而增大的增益。     

基于特征值估计的发射天线选择算法

现有的大多数发射天线选择算法都是假设信道是独立衰落的,这与实际的传播环境不相符。文章在相关信道的前提下研究MIMO(多输入多输出)系统,提出一种新的基于特征值估计的发射天线选择算法。算法通过特征值下界估计的方法,每次迭代中选择使信道矩阵最小特征值下界最大的列,来提高最小特征值并降低小天线间的相关性,使得容量最大化并使误码率最小化。理论分析结果表明,该算法在所选天线数目较多场合下具有较低的复杂度,同时其容量和误码性能优于随机选择算法,接近最优选择算法。     

大规模MIMO系统中收发联合阈值天线选择算法

在大规模多输入多输出(massive MIMO)系统中使用天线选择算法可提高能效和系统吞吐量,然而适用于传统MIMO系统的天线选择算法具有高复杂度,很难用于massive MIMO系统。为优化天线选择算法,以算法复杂度和系统容量为优化目标,提出了收发联合阈值天线选择算法。该算法在发射端使用最大范数双向天线选择算法进行天线选择,在接收端使用分组maxvol算法并通过仿真实验结果的预设阈值进行天线选择。仿真实验表明,收发联合阈值天线选择算法在降低复杂度的同时可以提高系统容量,与递增天线选择算法相比,系统容量最多可提高52.2 bit/s/Hz。提出的天线选择算法可以满足不同天线相关度和信噪比的传输环境。     

一种改进的MIMO-OFDM的信道估计算法

针对频率选择性衰落信道下MIMO—OFDM的系统,基于瑞利衰落信道的模型,利用m序列的自相关性,提出了一种基于m序列做梳状导频的多输入多输出正交频分复用最小二乘算法,该算法可以避免对大矩阵求伪逆,以减少计算复杂度,从而提高了信道估计的计算性能。通过对该算法的误码率性能分析和计算复杂度分析,结果表明,相比传统经典最小二乘算法,所提出信道估计算法在中低信噪比下,有效提高了信道估计性能,适合于在实际应用中实现。     

非相干天线选择算法

基于广义似然比检验(GLRT)与天线选择相结合,提出了平瑞利衰落信道的非相干天线选择(NON-AS)算法,天线选择和信号检测无需信道状态信息.NON-AS算法适用于多输入多输出(MIMO)系统的接收端,接收天线选择是基于每个天线瞬时接收到信号矢量的F-2范数.与相干检测的天线选择相比,NON-AS算法不需要估计信道,大大降低了系统复杂性.成对错误概率分析和仿真结果表明:在高信噪比情况下,选择有最大范数的接收天线,系统能实现和使用全部接收天线相同的分集增益.     

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.     

Joint antenna selection and link adaptation for MIMO systems

Multi-input multi-output (MIMO) systems, with multiple antennas at both the transmitter and the receiver, are anticipated to be widely employed in future wireless networks due to their predicted tremendous system capacity. To protect the transmitted data against random channel impairment, it is desirable to consider link adaptation, such as rate adaptation and power control, to improve the system performance and guarantee certain quality of service. Based on the observation that link adaptation and antenna selection problems are often coupled, we propose a joint antenna subset selection and link adaptation study for MIMO systems. After the formulation of the multidimensional joint optimization problem, the main contribution of this paper lies in the design of efficient algorithms approaching the optimal solution for both uncorrelated and correlated MIMO channels. Specifically, we propose one simplified antenna selection and link adaptation rule based on the expected optimal number of active antennas for uncorrelated MIMO with Rayleigh fading and one for correlated MIMO channels only based on the slowly varying channel correlation information. Our proposed algorithms are verified through numerical results, demonstrating significant gains over traditional MIMO signaling, while feasible for practical implementation.     

Receive antenna subset selection based on orthogonal components

A new receive antenna subset selection algorithm with low complexity for wireless Multipie-Input Multiple-Output （MIMO） systems is proposed, which is based on the orthogonal components of the channel matrix. Larger capacity is achieved compared with the existing antenna selection methods. Simulation results of quasi-static fiat fading channel demonstrate the significant performance of the proposed selection algorithm.     

Receive antenna selection for MIMO systems over correlated fading channels

In this letter, we propose a novel receive antenna selection algorithm based on cross entropy optimization to maximize the capacity over spatially correlated channels in multiple-input multiple-output (MIMO) wireless systems. The performance of the proposed algorithm is investigated and compared with the existing schemes. Simulation results show that our low complexity algorithm can achieve near-optimal results that converge to within 99% of the optimal results obtained by exhaustive search. In addition, the proposed algorithm achieves near-optimal results irrespective of the mutual relationship between the number of transmit and receive antennas, the statistical properties of the channel and the operating signal-to-noise ratio.     

Low-Complexity Throughput-Based Antenna Selection Method

Antenna selection is a low-cost low-complexity attractive approach in MIMO systems that capture many advantages of these systems. In this paper, our objective is to select the best antennas that maximize throughput with truncated selective repeat automatic repeat request at data link layer in zero-forcing MIMO receivers. We propose a novel binary particle swarm optimization method with throughput as its fitness function for joint transmit and receive antenna selection. The results of simulations demonstrate that the proposed throughput based antenna selection method has better performance compared to capacity based methods, and PSO algorithm can significantly reduce computational complexity.     

Joint estimation of the channel and I/Q imbalance for two-way relay networks

In modern day communication systems, the massive MIMO architecture plays a pivotal role in enhancing the spatial multiplexing gain, but vice versa the system energy efficiency is compromised. Consequently, resource allocation in-terms of antenna selection becomes inevitable to increase energy efficiency without having any obvious effect or compromising the system spectral efficiency. Optimal antenna selection can be performed using exhaustive search. However, for a massive MIMO architecture, exhaustive search is not a feasible option due to the exponential growth in computational complexity with an increase in the number of antennas. We have proposed a computationally efficient and optimum algorithm based on the probability distribution learning for transmit antenna selection. An estimation of the distribution algorithm is a learning algorithm which learns from the probability distribution of best possible solutions. The proposed solution is computationally efficient and can obtain an optimum solution for the real time antenna selection problem. Since precoding and beamforming are also considered essential techniques to combat path loss incurred due to high frequency communications, so after antenna selection, successive interference cancellation algorithm is adopted for precoding with selected antennas. Simulation results verify that the proposed joint antenna selection and precoding solution is computationally efficient and near optimal in terms of spectral efficiency with respect to exhaustive search scheme. Furthermore, the energy efficiency of the system is also optimized by the proposed algorithm, resulting in performance enhancement of massive MIMO systems.

     

Transmit antenna selection for decision feedback detection in MIMO fading channels

In this paper, we investigate a transmit antenna selection (TAS) approach for the decision-feedback detector (DFD) over Rayleigh fading channels. In particular, for a multipleinput multiple-output (MIMO) channel with M transmit and N (N ⩾ M) receive antennas, we derive a lower bound on the outage probability for the TAS approach. The selected transmit antennas are those that maximize the post-processing signalto- noise ratio (SNR) at the receiver end. It is shown that the proposed TAS approach achieves a performance close to optimal selection based on exhaustive search, introduced in the literature, but at a lower complexity. Simulation results are presented to validate and demonstrate the performance gain of the proposed TAS approach.     

GMD V-BLAST系统天线选择

基于最大容量和最小差错率准则,研究了在GMD V-BLAST系统中的天线选择问题。选用以信道矩阵非零奇异值的几何均值最大化为目标函数,可以避免容量与差错率性能之间的矛盾。在所有可用天线中进行选择的全搜索算法虽有最佳性能,但复杂度太高。基于贪婪算法,对发射天线采用快速的逐增选择策略,对接收天线采用快速的逐减选择策略,可以显著降低计算的复杂性。计算机仿真结果表明,所采用的快速天线选择算法可以较低的复杂度获得接近全搜索法的容量和分集增益。