An LDPC coded cooperative MIMO scheme over Rayleigh fading channels with unknown channel state information

This paper describes a coded cooperative multiple-input multiple-output(MIMO) scheme,where structured low-density parity-check(LDPC) codes belonging to a family of repeat-accumulate(RA) codes are employed.The outage probability of the scheme over Rayleigh fading channels is deduced.In an unknown channel state information(CSI) scenario,adaptive transversal filters based on a spatio-temporal recursive least squares(ST-RLS) algorithm are adopted in the destination to realize receive diversity gain.Also,a joint ’Min-Sum’ iterative decoding is effectively carried out in the destination.Such a decoding algorithm agrees with the bilayer Tanner graph that can be used to fully characterize two distinct structured LDPC codes employed by the source and relay.Simulation results verify the effectiveness of the adopted filter in the coded cooperative MIMO scheme.Theoretical analysis and numerical simulations show that the LDPC coded cooperative MIMO scheme can well combine cooperation diversity,multi-receive diversity,and channel coding gains,and clearly outperforms coded noncooperation schemes under the same conditions.     

基于最小均方误差的多用户MIMO下行预编码

在多输入多输出(MIMO)多用户下行链路中,可联合收发处理和功率分配,从而获得更好的通信性能。提出的多用户MIMO预编码方法分解成两步实现:首先消除用户间的干扰使之等效为多个单用户系统,在此基础上以最小均方误差(MMSE)为准则,进行收发的联合优化和功率分配,从而改善系统的误码率(BER)性能。仿真表明,所提出的方法与现有的一些方法相比,可以获得较大的信噪比(SNR)增益。     

统一框架下协作MIMO系统预编码方案

针对在协作MIMO系统中多小区及多用户间的干扰对系统性能的影响,一般都会基于某种设计准则进行预编码处理,但是由于设计目标的单一性会导致系统性能的不均衡,为了优化系统的整体性能,均衡各种性能指标,提出了统一数学框架下的预编码方案,并基于凸优化理论借助拉格朗日方程给出了预编码矩阵的求解方法。经仿真证明,算法在目标函数的收敛速度以及系统容量方面具有一定的优势,表明了所提方案实施的可行性。     

Investigation on Key Technologies in Large-Scale MIMO

Large-scale MIMO (multiple-input multiple-output) systems with numerous low-power antennas can provide better performance in terms of spectrum efficiency, power saving and link reliability than conventional MIMO. For large-scale MIMO, there are several technical issues that need to be practically addressed (e.g., pilot pattern design and low-power transmission design) and theoretically addressed (e.g., capacity bound, channel estimation, and power allocation strategies). In this paper, we analyze the sum rate upper bound of large-scale MIMO, investigate its key technologies including channel estimation, downlink precoding, and uplink detection. We also present some perspectives concerning new channel modeling approaches, advanced user scheduling algorithms, etc.     

分布式MIMO系统容量最优的预编码设计

针对分布式多入多出(MIMO)系统各天线功率无法共享的问题,基于分布式MIMO系统各天线功率受限的假设,提出容量准则下的最优预编码矩阵设计方案。采用Lagrange对偶优化的方法,将原优化问题转化为其对偶问题,给出求解预编码矩阵的迭代算法流程,以及在不同天线配置和容量准则下的信号发射策略。仿真结果表明,在发射天线数目多于接收天线数目时或信道矩阵缺秩时,该迭代算法求得的预编码矩阵的容量性能优于已有次优解析解。     

多小区MIMO系统中的迫零型预编码方法

针对多小区下行链路中的多 用户之间的同频干扰问题,提出了一种新的迫零型预编码方法。该方法基于线性代数的正交 投影原理,构造联合干扰信道矩阵,将目标用户的信道矩阵投影到联合干扰信道的零空间。 由此构造的预编码矩阵可以使得目标用户不会对其他用户产生干扰。相比于传统的预编码方 法,此方法在低信噪比区域性能较优,但是在高信噪比区域不如传统方法,所以结合两种方 法的特点,本文进一步提出了一种自适应预编码方法,并且分析对比了3种方法的计算复杂 度。仿真结果表明：自适应预编码方法在整个信噪比区域均优于传统方法,而运算复杂度略 高于传统方法。     

多用户MIMO系统预编码技术的研究

多天线基站和多用户可以组成一个多用户MIMO系统,为获得更高的系统容量和高效传输性能。预编码是一种应用于发射端以达到抑制多用户间干扰的预均衡方法。基于非理想信道状态信息(CSI)条件,针对多用户MIMO系统的下行信道,研究了线性ZF预编码和非线性THP预编码的性能,给出了接收端信号与干扰和噪声比SINR的近似表达式。对两种THP变体结构,dTHP结构和cTHP结构进行了性能仿真比较。仿真结果表明,cTHP结构的性能要优于dTHP结构。同时,给出了信道估计误差对cTHP结构BER影响的定量仿真结果。对非理想CSI下的预编码技术选择提供了一定的依据,并对限定信道估计误差的偏差程度具有参考意义。     

MIMO信道中基于功率分配的SLNR预编码算法

为了消除或降低多用户MIMO系统下行链路存在的共信道干扰（ CCI）,提出一种结合功率分配的基于最大化信漏噪比（ SLNR）的预编码算法。首先,根据SLNR算法求出最优预编码矩阵,再结合最优功率分配算法,借助拉格朗日乘数法,优化分配每个用户的发送功率,从而提高系统和容量以及降低误码率（ BER）性能。为了简化计算复杂度,还提出了SLNR算法结合次优化功率分配算法。仿真表明,所提出的算法比块对角化（ BD）算法和最小均方误差准则（ MMSE）算法在系统和容量以及误码率性能上都有所改善。     

A computationally efficient genetic algorithm for MIMO broadcast scheduling

In conventional single-input single-output (SISO) systems, the capacity is limited as base station can provide service to only one user at any instant. However, multiuser (MU) multiple-input multiple-output (MIMO) systems deliver optimum system capacity by providing service to multiple users (as many as transmit antennas) simultaneously according to dirty paper coding (DPC) scheme. However, DPC is an exhaustive search algorithm (ESA) where the user encoding sequence is important to transmit data to multiple users. Exhaustive search becomes imperative as the search space grows with number of users and number of transmit antennas in the MU MIMO system. This can be treated as an optimization problem of maximizing the achievable system sum-rate. In this paper, it has been demonstrated that combined user and antenna scheduling (CUAS) with binary genetic algorithm (BGA) adopting elitism and adaptive mutation (AM) achieves about 97–99% of system sum-rate obtained by ESA (DPC) with significantly reduced computational and time complexity. It has been shown that BGA is able to find the globally optimum solution for MU MIMO systems well within the time interval of modern wireless packet data communications. However, it is interesting to observe that BGA is able to find a solution to CUAS close to the optimum value quite rapidly. In this paper, it is also shown that BGA with elitism and AM achieves higher throughput than limited feedback scheduling schemes as well.     

互耦宽带MIMO系统容量分析

研究了宽带通信系统中,互耦现象对多输入多输出（MIMO）通信容量的影响。首先分析了天线阵列间的互耦作用,并基于此推导出互耦条件下宽带通信系统的容量表达式。然后通过蒙特卡洛方法,仿真了不同天线阵列配置下的系统容量。结果表明,在天线阵列间距很近时,天线阵列间的互耦效应能够改善天线阵列之间的相关性,从而提高系统的容量。并且天线数目越多,系统容量改善越大。该结论对宽带MIMO通信系统的天线设计起到一定的指导作用。     

MIMO系统多包接收性能分析

多包接收MPR(Multi-Packet Reception)技术能够极大地提高无线网络吞吐量,它可以同时接收多个数据包并进行有效的分离。多输入多输出系统MIMO(Multiple-Input Multiple-Output)是一种实现MPR的高效技术,对提高无线通信系统性能具有重要意义。文章对4×4天线配置下的MIMO系统MPR进行仿真并分析其误码性能。仿真分析表明,MIMO不仅能获得分集和复用增益,而且能实现一种多包接收功能并能同时正确分离多个数据包。     

MIMO雷达最大似然参数估计

多输入多输出(MIMO)雷达使用多个天线同时发射多个独立探测信号,并使用多个天线接收目标回波信号.本文考虑了发射空域分集、相干接收MIMO雷达模型及其最大似然(ML)参数估计方法.基于最大似然准则,本文推导了两种渐近最大似然算法.仿真实验的结果表明,在均匀噪声模型中,其中一种渐近算法与基于延迟求和波束形成的最大似然算法性能接近,而另一种渐近算法性能略差,但具有较低的计算复杂度.而在非均匀噪声模型中,本文所提出的两种渐近最大似然算法的性能均优于基于延迟求和波束形成的最大似然算法.     

Per-layer transmit and receive filters design for Tomlinson-Harashima precoding in multiuser MIMO systems

In this paper,the per-layer design for Tomlinson-Harashima precoding (THP) in the downlink of multiuser multiple-input multiple-output (MIMO) systems is investigated.In these systems,the number of the receivers is equal to that of the transmit antennas.Based on the criterion of maximum system sum-capacity,we study two per-layer joint transmit and receive filters design schemes with receive antenna beamforming (RAB) and receive antenna selection (RAS),respectively.Moreover,the differences of the equivalent channel gains and capacities between these two schemes are analyzed theoretically.Simulation results show that by these per-layer schemes,the system sum-rate is improved significantly with respect to the per-user processing scheme.     

MIMO多跳无线网络

综述了多跳无线网络MIMO(multiple-input multiple-output)技术的研究进展,分析了MIMO技术的引入对多跳无线网络各层及整体设计的影响.以跨层协议设计为重点,详细介绍了当前典型的基于MIMO的多跳无线网络协议算法的核心机制,并比较分析了这些算法的特点和性能差异与存在的缺陷.最后,结合本领域内的研究现状,指出了基于MIMO的多跳无线网络走向实际工程应用环境所亟待解决的关键问题,指出了基于MIMO技术设计自适应调整、高性能多跳无线网络跨层模型及协议的重要性.     

通道参数对MIMO雷达估计性能的影响效果分析

为在目标参数估计中正确处理MIMO雷达收发通道网的未知系数,提出并建立了3种通道参数模型,分别推导了各模型下的目标估计和克拉美罗界(Cramer-Rao lower bound,CRLB),比较了各模型下的参数估计性能,研究了通道参数的不同处理方法对MIMO雷达估计性能的影响效果。理论分析表明,在目标估计时如果将通道系数作为未知参量与目标参数联合估计,可比将其作为随机参量求期望获得更高的参数估计精度,而且随着信噪比的降低两者的参数估计性能差异会随之扩大。仿真实验也验证了上述结论的正确性。     

改进的IRS辅助毫米波MIMO级联信道估计

为改善智能反射表面（Intelligent reflective surface,IRS）辅助的毫米波多输入多输出（Multiple-input multiple-output,MIMO）级联信道的估计精度和收敛速度,基于平行因子（Parallel factor,PARAFAC）分解模型,把常规的双线性交替最小二乘（Bilinear alternating least squares,BALS）算法改进为带松弛因子的ω-BALS算法和正则化的T-BALS,加快了收敛速度和算法稳定性。当基站、IRS元件或用户侧的阵列天线数目较大时,提出改进的奇异值（Singular value decomposition,svd）-BALS算法。该算法通过奇异值分解压缩张量,再利用低维度的核心张量来重构模式n矩阵。仿真结果表明,该算法的归一化均方误差性能有所提高,并且加快了收敛速度。