利用干扰提高容量的低复杂度预编码方法

在接收用户配置单天线的多输入多输出广播系统(MIMO BC)中,提出了一种利用干扰提高系     

基于GMD-DPC/THP的两组Alamouti非线性预编码系统

针对Alamouti空时块编码复用增益损失的问题,提出了两组Alamouti编码方案;在此基础上,为了改善系统的误码率（Bit Error Rate,BER）性能和简化接收端复杂度,提出将几何均值分解（Geometric Mean Decomposition,GMD）算法和非线性预编码技术相结合的两组Alamouti传输方案。本方案的设计方法为：首先等效出两组Alamouti空时块编码系统的信道矩阵;进而,通过GMD算法对等效信道矩阵进行收发端联合设计;最后,在发射端应用脏纸（Dirty Paper Coding,DPC）和Tomlinson-Harashima Precoding（THP）非线性预编码技术,消除发送信号间的干扰,从而使系统获得更好的误码率性能。通过仿真结果对比发现,提出的系统可以显著地改善误码率性能。     

一种TDD-MIMO系统混合预编码传输机制

针对TDD-MIMO系统中,天线之间存在相关性时会影响系统性能的问题,提出一种混合预编码传输机制.在该机制中,基站首先采用一种低复杂度的信道估计算法来计算信道矩阵,然后计算信道矩阵的相关度来判断相关性的高低并选择相应的预编码方式.当相关度大于等于转换系数时,采用基于协方差矩阵的预编码;反之,则利用基于LR的预编码.仿真结果表明,该机制充分利用了2种预编码分别适用于不同相关性范围的特点,在降低了系统的误码率的同时保持了低复杂度,提高了系统在天线之间存在相关性时的容量.仿真验证了该机制的有效性.     

大规模MIMO系统下的低复杂度迫零预编码技术

针对大规模天线技术导致传统迫零(zero-forcing, ZF)预编码复杂度上升的问题,提出了一种低复杂度预编码技术。首先利用对称超松弛迭代(semi-iteration symmetric successive overrelaxation method, SSOR)技术优化信道矩阵求逆复杂度的问题;然后利用切比雪夫半迭代法(semi-iteration, SI)加速SSOR迭代(semi-iteration symmetric successive overrelaxation method, SI-SSOR)的收敛速度,使得ZF预编码可以快速的收敛。实验结果表明,SI-SSOR预编码在误码率和传输速率上都有非常好的性能。切比雪夫半迭代法加速的SSOR预编码技术通过2次迭代就可以近似达到传统ZF预编码性能的95%以上,同时利用相对于迫零预编码更少的计算资源,因此,SI-SSOR预编码技术可以作为无线通信抑制干扰的重要手段。     

多中继通信系统中量化误差补偿的MMSE中继预编码方案

为了补偿量化误差造成的系统性能的降级,文中提出将Grassmannian码本量化误差的统计特性引入多中继系统有限反馈中继预编码的设计,首先在QoS要求下的完全反馈中继预编码与接收均衡MMSE两步优化算法的基础上,给出了有限反馈下量化误差补偿的中继预编码与接收均衡MMSE两步优化方案,进而文中在中继和功率约束下,提出了一种有限反馈下量化误差补偿的中继预编码与接收均衡MMSE联合优化方案,并给出了中继预编码矩阵设计的闭合表达式.计算机仿真表明,文中提出的中继和功率约束下量化误差补偿的MMSE联合优化方案的系统性能优于QoS要求下量化误差补偿的MMSE两步优化方案,而且相比没有采用量化误差补偿的MMSE联合优化方案可以获得更低的误码率性能.     

MIMO-CPSC系统中预编码盲信道估计算法研究*

在频域均衡系统中,基于预编码的子空间盲信道估计算法可以有效地对信道进行估计。针对多天线单载波循环前缀系统,提出了一种基于预编码的盲信道估计算法。该算法通过分离出连续两个接收符号块的互相关矩阵所包含的信道信息实现信道盲估计。仿真结果表明,该算法具有较低的计算复杂度以及对信道阶数过估计有很好的鲁棒性,并且该算法利用较少的数据块个数就得到了一个可靠的信道估计值。     

Complexity-performance trade-off of algorithms for combined lattice reduction and QR decomposition

Over the last years, numerous equalization schemes for multiple-input/multiple-output channels have been studied in the literature. New low-complexity approaches based on lattice basis reduction are of special interest, since they achieve the optimum diversity behavior. Although the per-symbol equalization complexity of these schemes is very low, the initial calculation of the required matrices may impose an enormous burden in arithmetic complexity. In this paper, we give a tutorial overview and assess algorithms, which, given the channel matrix, result in the feedforward, feedback, and unimodular matrix required in lattice-reduction-aided decision-feedback equalization or precoding. To this end, via a unified exposition of the Lenstra–Lenstra–Lovász (LLL) algorithm, the LLL with deep insertions, and the reversed Siegel approach similarities and differences of these approaches are enlightened. A modification of the LLL swapping criterion, better matched to the equalization setting, is discussed. It is shown that using lattice-reduction-aided equalization/precoding better performance can be achieved at lower complexity compared to classical equalization or precoding approaches.     

An Enhanced Jacobi Precoder for Downlink Massive MIMO Systems

Linear precoding methods such as zero-forcing (ZF) are near optimal for downlink massive multi-user multiple input multiple output (MIMO) systems due to their asymptotic channel property. However, as the number of users increases, the computational complexity of obtaining the inverse matrix of the gram matrix increases. For solving the computational complexity problem, this paper proposes an improved Jacobi (JC)-based precoder to improve error performance of the conventional JC in the downlink massive MIMO systems. The conventional JC was studied for solving the high computational complexity of the ZF algorithm and was able to achieve parallel implementation. However, the conventional JC has poor error performance when the number of users increases, which means that the diagonal dominance component of the gram matrix is reduced. In this paper, the preconditioning method is proposed to improve the error performance. Before executing the JC, the condition number of the linear equation and spectrum radius of the iteration matrix are reduced by multiplying the preconditioning matrix of the linear equation. To further reduce the condition number of the linear equation, this paper proposes a polynomial expansion precondition matrix that supplements diagonal components. The results show that the proposed method provides better performance than other iterative methods and has similar performance to the ZF.