一种MIMO时变信道下的低反馈量有限反馈预编码方法

本文提出了一种新颖的适用于MIMO时变信道环境的有限反馈预编码方法.该方法利用子空间跟踪算法自适应跟踪时变信道的发射预编码矩阵,同时结合Jacobi迭代算法有效降低了有限反馈预编码的反馈量.通过计算机仿真,该方法在ARI时变信道模型,以及更实际的Jake's时变信道模型下的跟踪性能得到很好的验证.在更低的反馈量下,该方法可以获得比传统的存在反馈延时的Grassmannian预编码方法更好的系统容量性能,而且该方法在相同的反馈量下,无论是收敛性能还是误码率性能都明显优于将Jacobi迭代算法直接应用于时变信道跟踪的预编码方法.     

多用户MIMO系统在有限反馈下结合预编码与调度算法的研究

捅要：研究了多用户多输入多输出（MIMO）系统在有限反馈下的一种结合单位预编码与用户调度算法的方案。在该系统中每一用户都具有多个接收天线,该方案具有较大的多用户分集增益和较小的计算复杂度,从而能够减少系统中的用户间干扰。每个用户独立地决定自己的天线合并矢量,并将最优波束矢量以及对应的估计信干噪比通过有限反馈信道反馈给基站,此时基站获取了相应的用户信道信息和用户间干扰信息。基于接收到的反馈信息,基站从预定义的码本中选取和速率最大的最优子集进行系统预编码,然后依照预编码矩阵调度欧氏距离最小的用户且用户个数不超过发送天线的数目。仿真结果显示该方案有效地改善了系统速率,特别是在用户数目较多或者信道环境较好的情况下。     

基于有限反馈的MIMO系统自适应调制

该文利用信道的特征矢量,将MIMO高斯信道转化成混合信道,结合功率分配给出了自适应调制方案,使系统容量在发射功率和服务质量约束下达到最大。发射端所需的波束成形和功率分配等信息利用有限的比特反馈到发射端。仿真结果表明：在小信噪比的情况下,该自适应调制方案对系统容量有明显改善。     

有限反馈MIMO系统中一种基于双码本的功率分配与预编码方案

研究表明，有限反馈预编码能有效利用信道信息提高MIMO系统频谱效率，在此基础上，在发射端进行功率控制将使频谱效率得到进一步提升．为了最大化有限反馈MIMO系统的频谱效率，本文提出了一种基于自适应调制的功率分配与预编码方案．为了减少反馈量，该方案通过双码本方式来反馈预编码矩阵和功率控制矢量，并给出了一种基于K-均值聚类的功率码本设计方法．仿真结果表明，该方案的反馈信息量少，且获得了较好频谱性能。     

基于有限反馈的MIMO系统鲁棒性均匀信道分解

 在MIMO系统中,当假设发送端和接收端已知完美信道状态信息的情况下,均匀信道分解(UCD)被证明在误码率性能上是最优的且是容量无损的.然而在实际系统中,接收端或发送端获得的信道信息通常存在着一定的估计误差.本文提出一种基于有限反馈的鲁棒性均匀信道分解方案,在设计方案中充分考虑了信道估计误差的影响,并利用香农率失真定理和广义Lloyd矢量量化算法(GLA)得到了信道估计误差的近似值,使得本文提出的方案有很好的实用性.仿真结果表明与传统均匀信道分解方案相比,本文提出的鲁棒性均匀信道分解方案有效地降低了系统的误码平层,提高了系统容量.     

有限反馈MIMO系统中用户选择算法

针对有限反馈多输入多输出（MIMO）系统中用户选择问题,提出了一种新的用户选择方法。首先推导出采用迫零波束赋形（ZFBF）预编码时容量损失上限,证明了最小容量损失与最大和容量优化目标的一致性。在此基础上给出了基于最小容量损失的用户选择方法,该方法以信道量化误差为选择依据进行用户筛选。与现有算法相比,降低了算法的复杂度,减少了反馈CQI带来的系统开销。仿真和分析表明,所提方法改善了系统和容量性能。     

认知MIMO系统基于有限反馈的干扰信道学习算法

针对认知多输入多输出(MIMO)系统中的干扰信道学习问题进行研究,提出了一种基于有限反馈的干扰信道学习算法。算法用干扰信道的零空间代替整个信道矩阵作为反馈量,在推导最优零空间码字选择准则的基础上,分析干扰信道零空间量化结果的时间相关性,并在前一帧量化结果上加一个基于旋转码本的扰动构建当前帧零空间码本。为防止量化误差的传播,进一步推导了旋转量参数的更新。理论分析和仿真结果表明,所提算法在慢变MIMO干扰信道下对零空间的跟踪效果比Grassmannian子空间包码本好。     

中继辅助MIMO系统中基于有限反馈的分布式波束成形

对协作式中继辅助的多输入多输出通信系统中基于有限反馈的联合波束成形问题进行了研究.首先推导了接收信噪比的下界,并基于该下界建立联合波束成形的数学模型.其次推导了分布式波束成形的最优方案.最后针对该最优方案,设计了一个迭代算法,优化得到分布式波束成形码本,从而分布式中继站的波束成形可基于码本序号的有限反馈来进行.实验结果仿真表明,所设计的联合波束成形码本在反馈开销很小的情况下获得明显的性能增益.     

MIMO广播系统中基于有限反馈的多用户选择新算法

在基于有限反馈多输入多输出(MIMO)广播系统的多用户选择研究中,目前已有的用户信噪干扰比(SINR)估计存在较大误差,这使得整个系统的性能受到制约。该文利用推导出的上下界来联合估计用户接收到的有用信号功率,得到了一种误差较小的SINR估计式,并基于该式给出了一种用户选择新算法。仿真结果表明,新的用户选择算法可以在低信噪比和高信噪比下同时达到较优性能,且复杂度较低。     

一种多输入多输出系统中有限反馈预编码的自适应跟踪测度

该文提出一种新的适用于无线相关信道中有限反馈预编码多输入多输出系统的预编码矩阵自适应跟踪测度。由于该测度在统计意义下对信道的相关性与预编码矩阵变化的关系具有更优化的描述,所以基于该测度的方案在保持较低反馈信息量的前提下,能够进一步改善预编码系统性能。文中给出优化自适应跟踪测度的理论推导。系统仿真分别针对无线信道的频域相关和时域相关,并结合空间分集和空间复用两类系统结构予以讨论,并验证了理论分析结果。另外,由于该测度方案算法复杂度更低,所以更具工程实用价值。     

信道反馈延迟时MIMO中继系统的线性预编码算法

针对采用放大转发(Amplify and Forward, AF)中继技术的多入多出(Multiple Input Multiple Output, MIMO)系统, 考虑信道估计误差及反馈存在延迟的情况, 提出一种基于最小均方误差(Minimum Mean Squared Error, MMSE)准则的预编码设计方案.假设基站-中继端及中继端-终端的信道均存在估计误差与反馈延迟, 在基站和中继端功率都受限条件下, 以MMSE为准则, 推导得到了基站预编码矩阵、中继转发矩阵和终端解码矩阵的闭式解.数值仿真结果表明, 该方案所提出的预编码算法能有效地改善系统的误比特率与均方误差.     

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。     

Interference-aware uplink transmission schemes for multicell MIMO system

In this paper we consider interference-aware uplink transmission schemes for multicell multiple-input multiple-output (MIMO) system.Unlike conventional transmission schemes without considering the interference probably caused to other cell,we jointly optimize the transceiver beamforming vectors to maximize the desired signals while removing the intercell interference.Specifically,for a two-cell system where each transmitter is equipped with two antennas,we derive the closed-form expression for the transmit scheme called coordinated beamforming (CBF) via generalized-eigen analysis.Moreover,when asymmetric interference is considered,we give a balancing beamforming (BBF) scheme where the interfering transmitter is to strike a compromise between maximizing the desired signal andminimizing the generated interference.Simulation results show that both schemes perform better than conventional schemes under different scenarios.     

一种基于子空间跟踪的认知MIMO预编码算法

针对多用户认知MIMO下行系统,提出一种适用于慢衰落信道环境的预编码算法。该算法基于子空间跟踪思想,利用信道时域相关性,通过递推方式自适应更新预编码矩阵来跟踪和适应慢衰落信道环境的变化。分别从算法收敛速度、收敛精度、跟踪性能以及计算复杂度四个方面进行了理论推导和仿真分析。结果表明,所提算法可以有效的跟踪信道环境的动态变化,与传统算法相比,在保证系统性能的同时显著降低了处理复杂度。     

面向大规模MIMO系统的下行预编码技术研究进展

大规模多入多出(Multiple-Input Multiple-Output,MIMO)技术在移动通信系统中发挥着至关重要的作用,但在部署中却引入了干扰。为了消除大规模MIMO干扰,业界围绕低复杂度的预编码算法开展了大量的研究工作。将已有大规模MIMO系统的预编码技术进行了归纳,重点分析了线性预编码和非线性预编码中经典算法的复杂度和优缺点,最后对大规模MIMO预编码技术的发展趋势进行了探讨。     

Performance evaluation of channel inversion precoding for downlink multi-user MIMO system

In downlink multi-user multi-input multi-output (MU-MIMO) system, not every user (user equipment (UE)) can calculate accurately signal to interference and noise ratio (SINR) without prior knowledge of the other users' precoding vector. To solve this problem, this article proposes a channel inversion precoding scheme by using the lower bound of SINR and zero-forcing (ZF) algorithm. However, the SINR mismatch between lower bound SINR and actual SINR causes the inaccurateness of adaptive modulation and coding (AMC). As a result, it causes degradation in performance. Simulation results show that channel inversion precoding provides lower throughput than that of single user multi-input multi-output (SU-MIMO) at high signal-to-noise ratio (SNR) (>14 dB), due to the SINR mismatch, although the sum-rate of channel inversion precoding is higher than that of SU-MIMO at full SNR regime.     

Clustered linear precoding for downlink network MIMO systems with partial CSI

We propose two novel clustered linear precoding schemes applicable to network multi‐input multi‐output systems using only partial channel state information to enhance the sum‐rate of the system. Using a channel model that decomposes a multi‐input multi‐output channel matrix into transmit and receive steering vectors and assuming that only transmit steering vectors are available at the base transmit stations, we, first, propose a regularized channel inversion precoding scheme to enhance the sum‐rate assuming only single‐antenna users are available in the system. Next, because of the limitation of regularized channel inversion to handle users with multiple receive antennas, a novel block diagonalization method is proposed. We construct the precoding matrices that jointly eliminate inter‐cell interference and maximize the sum‐rate for a given input covariance matrix. Assuming total power constraint and per‐base‐station power constraints, optimal power allocation schemes are further developed to optimize the sum‐rate. We analytically show that the sum‐rate increases linearly with the number of users when only single‐antenna users are present in the system. Numerical results show that at low signal‐to‐noise ratios, the block diagonalization precoding outperforms the regularized channel inversion in terms of the bit error rate; while at high signal‐to‐noise ratios, the regularized channel inversion provides a better performance. Copyright © 2016 John Wiley & Sons, Ltd.     

多用户MIMO下行收发联合波束成形

针对随机波束成形(RBF)只有在包含大量用户的系统中才能获得较好性能的局限性,提出了一种新的收发联合波束成形方案,用于多用户多入多出(MIMO)下行链路传输。该方案有效地结合了RBF与接收天线合并技术。分析及仿真结果表明,即使在用户数为中等甚至较小时仍然能获得较高的和速率性能。由于每个用户的反馈开销没变,从而使获得同样和速率时系统的总反馈开销明显减少。仿真结果还显示,所提方案的和速率性能不仅优于原RBF,也比RBF结合最大比合并或者等增益合并的性能有明显改善,同时还保持了最优的随用户数变化的增长速度。     

Performance analysis of MIMO beamforming with imperfect feedback

The quality of channel state information at the transmitter (CSIT) is critical to MIMO beamforming systems. However, in practical wireless systems, CSIT suffers from imperfections originating from quantization effects, feedback error and feedback delay. In this paper, we study the impact of feedback error and delay on the symbol error rate of MIMO beamforming systems with finite rate feedback. The feedback channel is modeled as a uniform symmetric channel. We derive an symbol error rate upper bound that is tight for a good beamformer. We also quantify the diversity gain and array gain loss due to the feedback error and delay. The codebook design method that is applicable to the beamforming systems with error or delay feedback is discussed. Both analytical and simulation results show that feedback error and delay will make the system behave badly at high signal‐to‐noise ratios. Copyright © 2012 John Wiley & Sons, Ltd.     

Interference alignment scheme for massive MIMO system

The performance of massive multiple-input multiple-output （MIMO） system is limited by pilot contamination. To reduce the pilot contamination, uplink and downlink precoding algorithms are put forward based on interference alignment criterion. In the uplink receiving processing, the target function aligns the pilot contamination and the interference signals to the same null space and acquires the maximal space degree of the desired signals. The uplink receiving precoding matrix is solved on maximal signal to interference plus noise ratio （SINR） criterion considering the impact of the pilot contamination on channel estimations. The uplink receiving precoding matrix is used as the downlink transmitting precoding matrix. Exploiting the channel reciprocity, it is proved that, if the uplink receiving precoding matrix achieves maximal S1NR, the identical precoding matrix can be used in the downlink transmission and acquires maximal signal to leakage plus noise ratio （SLNR）. Simulations show that the spectrum efficiency of the proposed algorithm can reach about 1.5 times higher than that of popular matched filtering （MF） precoding algorithm, and about 1.1 times higher than multi-cell minimum mean square error （MMSE） precoding algorithm. The performance of the proposed algorithm can be improved approximately linearly with the increasing of the number of antennas.