大规模MIMO系统低复杂度混合迭代信号检测

在大规模MIMO系统上行链路信号检测算法中,最小均方误差(MMSE)算法能获得接近最优的线性检测性能.但是,传统的MMSE检测算法涉及高维矩阵求逆运算,由于复杂度过高而使其在实际应用中难以快速有效地实现.基于最速下降(steepest descent,SD)算法和高斯一赛德尔(Gauss-Seidel,GS)迭代的方法提出了一种低复杂度的混合迭代算法,利用SD算法为复杂度相对较低的GS迭代算法提供有效的搜索方向,以加快算法收敛的速度.同时,给出了一种用于信道译码的比特似然比(LLR)近似计算方法.仿真结果表明,通过几次迭代,给出的算法能够快速收敛并接近MMSE检测性能,并将算法复杂度降低一个数量级,保持在O(K2).     

基于切比雪夫-迹迭代的大规模MIMO系统软输出信号检测

在多用户大规模多输入多输出(MIMO)系统信号检测算法中,最小均方误差(MMSE)算法可取得近似最优性能,但MMSE算法中高维矩阵求逆的复杂度过高,导致在实际应用中难以快速有效地实现.同时,对于高阶正交幅度调制(HQAM),如果符号向比特的解映射采用硬判决,将会导致后续信道译码的性能明显下降.因此,该文针对采用格雷编码的HQAM的多用户大规模MIMO系统,提出一种基于切比雪夫-迹迭代(CTI)的低复杂度软输出信号检测算法.该算法不但有效地规避了信号检测所需的高维矩阵求逆,同时,利用格雷编码的调制信号的比特翻转特性和二叉树结构,给出了一种融合三叉链表搜索的比特对数似然比(LLR)简化计算方法.仿真结果表明,该文所提的软输出信号检测算法最多需要3次迭代就能收敛并可取得接近MMSE算法的性能,在复杂度和性能之间取得了很好的折中.     

大规模MIMO系统中块高斯-赛德尔检测算法

最小均方误差(Minimum Mean Square Error,MMSE)检测算法,虽然能在大规模多输入多输出系统中获得接近最优的线性检测性能,但是涉及高维矩阵求逆运算,难以在实际应用中快速有效地实现.提出了块高斯-赛德尔(Block Gauss-Seidel,BGS)低复杂度信号检测算法,将MMSE检测器的滤波矩阵先进行分块预处理,构造分裂矩阵,再通过迭代求解发送信号向量估计值,以提高算法检测性能.仿真结果表明,BGS迭代算法在调制方式为64QAM、用户侧的天线数量设置为16、基站侧的天线数量设置为256时,迭代2次后就能快速接近MMSE检测性能.在设置近似初始值后,BGS算法的性能得到了进一步的改善.当调制方式为256QAM时,设置近似初始值的BGS算法在迭代2次后就能逼近MMSE算法的误码率(Bit Error Ratio,BER)性能曲线,此时算法的复杂度仍然保持在O(K2).     

低精度ADC下大规模多输入多输出系统基于三对角迭代法的软输出信号检测

受硬件成本制约,大规模多输入多输出（massive Multiple Input Multiple Output, mMIMO）基站通常配置低精度模数转换器（Analog-to-Digital Converter, ADC）。低精度ADC下,如果多用户mMIMO系统采用最小均方误差（Minimum Mean Square Error, MMSE）检测,将导致过高复杂度。为此,本文基于三对角迭代法（Tridiagonal Iterative Method, TDIM）,结合分块矩阵求逆的初始值确定,提出一种低复杂度的MMSE软输出信号检测算法。数值仿真表明:基于TDIM的软输出信号检测算法经4次迭代即可达到收敛,同时,数值结果验证了ADC量化比特为4时,该算法可取得接近全精度ADC的性能,为低精度ADC下mMIMO系统的上行链路信号检测实现提供了切实可行的方案。      

大规模MIMO系统中的一种自适应SIC检测算法

与传统系统相比,大规模多入多出（MIMO）系统能更加有效地提高频谱效率。利用传统的最小均方误差（MMSE）信号检测算法求解大规模MIMO系统,虽然检测结果接近最优,但是矩阵的求逆运算导致计算的复杂度非常高。提出了一种自适应排序干扰消除（SIC）检测算法,在逐次超松弛（SOR）迭代运算的基础上,通过干扰消除降低待检测矩阵的维度。通过仿真分析,得出所提算法的复杂度低于Jacobi、SOR检测算法,且在迭代次数较少的情况下,算法的误码率（BER）性能明显优于SOR检测算法。     

用于大规模MIMO系统的改进CG检测算法

针对大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统中近似最优线性最小均方误差(Minimum Mean Square Error,MMSE)算法复杂度过高问题,提出了RC-CG(Region Constellation-Conjugate Gradient)低复杂度近似最优信号检测算法。该算法首先利用共轭梯度(Conjugate Gradient,CG)迭代算法避免MMSE信号检测算法的高维度矩阵求逆,降低计算复杂度;其次引入二分查找算法对星座图进行区域分块,优化迭代初始解,使算法在保证原来检测性能的基础上加快收敛速度。仿真结果表明,该算法不仅可以达到近似MMSE算法的检测性能,而且适用于高阶调制,算法复杂度从O(K3)降低到O(K2)。     

大规模MIMO系统中基于二对角矩阵分解的低复杂度检测算法

在大规模多输入多输出(MIMO)系统的上行链路检测算法中,最小均方误差(MMSE)算法是接近最优的,但算法涉及到大矩阵求逆运算,计算复杂度仍然较高。近年提出的基于诺依曼级数近似的检测算法降低了复杂度但性能有一定的损失。为了降低复杂度的同时逼近MMSE算法性能,该文提出基于二对角矩阵分解的诺依曼级数(Neumann Series)近似,即将大矩阵分解为以两条主对角线上元素组成的矩阵与空心矩阵之和。理论分析与仿真结果表明所提算法检测性能逼近MMSE检测算法,且其复杂度从O(K3)降低到O(K2),这里K是用户的数目。     

基于软输出固定复杂度球形译码的高效迭代检测算法

该文在软输出固定复杂度球形译码(SFSD)算法的基础上,提出一种低复杂度高性能的MIMO迭代检测方法。该算法利用迭代过程中译码器的反馈信息更新SFSD检测算法的软输出,获得明显的迭代增益,并利用多级比特映射星座图的特点大大降低分支度量的运算次数。针对SFSD算法预处理复杂度较高的问题,该文将检测顺序调整和QR分解两个预处理步骤相结合,从而减少了矩阵求逆运算。在长期演进方案(LTE)下行链路环境中的仿真结果表明,该文提出的算法性能十分接近最优的最大后验概率(MAP)检测,并且实现复杂度相对于MAP有显著的下降。     

用于大规模MIMO系统的改进自适应SOR检测算法

大规模多输入多输出(Multiple-Input Multiple-Output, MIMO)系统由于具备较多的天线数,会导致传统线性信号检测算法如最小均方误差(Minimum Mean Square Error, MMSE)的复杂度过高。针对以上问题,提出了F修正的自适应超松弛迭代(F-corrected Adaptive Successive over Relaxation, FA-SOR)检测算法。该算法首先利用超松弛迭代(Successive over Relaxation, SOR)算法避免高阶矩阵求逆运算,降低复杂度;其次使用F修正的公式自动更新SOR算法迭代使用的松弛参数,同时优化迭代的公式与初始解来加快收敛速度。仿真结果表明,不论在理想独立信道还是相关信道下,相比于现有的自适应SOR算法,FA-SOR都能以更低的复杂度达到更低的误码率,同时逼近MMSE算法的性能。     

顽健自适应波束形成算法

针对方向向量偏差会导致最小均方(LMS)算法的性能急剧下降这一问题,提出了一种基于可变对角载入的顽健自适应波束形成算法.采用最陡下降法对信号方向向量进行优化求解,并在每次迭代过程中更新对角载入值,进而求出最优的权重向量,避免了矩阵求逆运算和特征值分解运算,大大降低了计算复杂度.通过建立步长与输入信号的关系得到可变的步长因子,克服了收敛速度和稳态误差之间的矛盾.该算法收敛速度快,抗扰动性强,对信号方向向量偏差具有很强的顽健性,从而改善了阵列输出的信干噪比,使其更接近最优值.理论分析和仿真结果表明与传统自适应波束形成算法相比,所提顽健算法具有更好的性能.     

Low complexity detection algorithm based on optimized Neumann series for massive MIMO system

An optimized Neumann series ( NS ) approximation isdescribed based on Frobenius matrix decomposition, this method aims to reduce the high complexity, which caused by the large matrix inversion of detection algorithm in the massive multiple input multiple output (MIMO) system. The large matrix in the inversion is decomposed into the sum of the hollow matrix and a Frobenius matrix, and the Frobenius matrix has the diagonal elements and the first column of the large matrix. In order to ensure the detection performance approach to minimum mean square error (MMSE) algorithm, the first three terms of the series approximation are needed, which results in high complexity as O(K³), where K is the number of users. This paper further optimize the third term of the series approximation to reduce the computational complexity from O(K³) to O(K²). The computational complexity analysis and simulation results show that the performance of proposed algorithm can approach to MMSE algorithm with low complexity O(K²).     

大规模MIMO系统中基于TFQMR的低复杂度信号检测算法

在大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统上行链路检测算法中,最小均方误差(Minimum Mean Square Error,MMSE)算法可取得近似最优的性能,然而MMSE算法涉及高维矩阵求逆问题,其计算复杂度高达O(K3),其中K表示用户数.为此,针对极化信...     

Low complexity affine MMSE detector for iterative detection-decoding MIMO OFDM systems

Iterative turbo processing between detection and decoding shows near-capacity performance on a multiple-antenna system. Combining iterative processing with optimum front-end detection is particularly challenging because the front-end maximum a posteriori (MAP) algorithm has a computational complexity that is exponential. Sub-optimum detector such as the soft interference cancellation linear minimum mean square error (SIC-LMMSE) detector with near front-end MAP performance has been proposed in the literature. The asymptotic computational complexity of SIC-LMMSE is O(n_t ²n_r + n_tn_r ³ + n_tM_c2^Mc) per detection-decoding cycle where n_t is number of transmit antenna, nr is number of receive antenna, and Mc is modulation size. A lower complexity detector is the hard interference cancellation LMMSE (HIC-LMMSE) detector. HIC-LMMSE has asymptotic complexity of O(n_t ²n_r + n_tM_c2^Mc) but suffers extra performance degradation. In this paper, two front-end detection algorithms are introduced that not only achieve asymptotic computational complexity of O(n_t ²n_r + n_tn_r ² [Gamma (beta) + 1] + n_tM_c2^Mc) where Gamma(beta) is a function with discrete output {-1, 2, 3, ...,n_t} and O(n_tM_c2^Mc) respectively. Simulation results demonstrate that the proposed low complexity detection algorithms offer exactly same performance as their full complexity counterpart in an iterative receiver while being computational more efficient.     

基于消息传递的大规模多用户MIMO低复杂度的检测算法

针对大规模多用户多输入多输出(MIMO)系统中基站端检测复杂度高的问题,提出了一种低复杂度、基于强制收敛的变量节点全信息高斯消息传播迭代检测(VFI-GMPID-FC)算法.首先对传统的GMPID算法进行改进,得到VFI-GMPID算法,VFI-GMPID算法的检测性能逼近最小均方误差检测(MMSE)算法,但复杂度要大大低于MMSE算法.然后结合强制收敛思想和VFI-GMPID,提出VFI-GMPID-FC算法,进一步降低算法复杂度,提升检测效率.最后通过仿真结果表明,所提算法在保证检测性能的同时,能有效地降低算法的复杂度.     

Tree pruning for MIMO sphere detection based on MMSE detection

Tree pruning is an effective algorithm to reduce the complexity of sphere detection(SD) for multiple-input multiple-output(MIMO) communication systems.How to determine the tree pruning rule,as well as by what the tradeoff between the performance and the complexity can be achieved,is still an open problem.In this paper,a tree pruning algorithm is proposed based on minimum mean square error(MMSE) detection.The proposed algorithm first preforms MMSE detection since the complexity of MMSE detection is very low.Then the pruning constraints will be set according to the scaled path metrics of the MMSE solution.The choice of the scale factors and their influences on the complexity and performance are also discussed.Through analysis and simulations,it is shown that the complexity is reduced significantly with negligible performance degradation and additional computations.     

Reliability feedback–aided low‐complexity detection in uplink massive MIMO systems

Massive multiple‐input multiple‐output (MIMO) plays a crucial role in realizing the demand for higher data rates and improved quality of service for 5G and beyond communication systems. Reliable detection of transmitted information bits from all the users is one of the challenging tasks for practical implementation of massive‐MIMO systems. The conventional linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) achieve near‐optimal bit error rate (BER) performance. However, ZF and MMSE require large dimensional matrix inversion which induces high computational complexity for symbol detection in such systems. This motivates for devising alternate low‐complexity near‐optimal detection algorithms for uplink massive‐MIMO systems. In this work, we propose an ordered sequential detection algorithm that exploits the concept of reliability feedback for achieving near‐optimal performance in uplink massive‐MIMO systems. In the proposed algorithm, symbol corresponding to each user is detected in an ordered sequence by canceling the interference from all the other users, followed by reliability feedback‐based decision. Incorporation of the sequence ordering and the reliability feedback‐based decision enhances the interference cancellation, which reduces the error propagation in sequential detection, and thus, improves the BER performance. Simulation results show that the proposed algorithm significantly outperforms recently reported massive‐MIMO detection techniques in terms of BER performance. In addition, the computational complexity of the proposed algorithm is substantially lower than that of the existing algorithms for the same BER. This indicates that the proposed algorithm exhibits a desirable trade‐off between the complexity and the performance for massive‐MIMO systems.     

Maximum likelihood sequence estimation from the lattice viewpoint

Considers the problem of data detection in multilevel lattice-type modulation systems in the presence of intersymbol interference and additive white Gaussian noise. The conventional maximum likelihood sequence estimator using the Viterbi algorithm has a time complexity of O(m^ν+1) operations per symbol and a space complexity of O(δm^ν) storage elements, where m is the size of input alphabet, ν is the length of channel memory, and δ is the truncation depth. By revising the truncation scheme and viewing the channel as a linear transform, the authors identify the problem of maximum likelihood sequence estimation with that of finding the nearest lattice point. From this lattice viewpoint, the lattice sequence estimator for PAM systems is developed, which has the following desired properties: 1) its expected time-complexity grows as δ² as SNR→∞; 2) its space complexity grow as δ; and 3) its error performance is effectively optimal for sufficiently large m. A tight upper bound on the symbol error probability of the new estimator is derived, and is confirmed by the simulation results of an example channel. It turns out that the estimator is effectively optimal for m⩾4 and the loss in signal-to-noise ratio is less than 0.5 dB even for m=2. Finally, limitations of the proposed estimator are also discussed