基于预处理共轭梯度法的低复杂度信号检测算法

大规模多输入多输出系统中,最小均方误差信号检测算法是近似最优的,但由于其涉及矩阵求逆,计算复杂度随着天线数量增加呈指数增长.提出了低复杂度的预处理共轭梯度信号检测算法,该算法通过预处理技术降低矩阵条件数,从而加快共轭梯度信号检测算法的收敛速度.仿真结果显示,该算法在小数量的迭代中能够达到和最小均方误差检测算法相似的误码率,算法复杂度下降了一个数量级.相比直接用共轭梯度法,能够更快收敛到最佳值.     

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)。     

基于Kaczmarz迭代的大规模MIMO系统低复杂度软输出信号检测

大规模MIMO系统上行链路中,最小均方误差（MMSE）算法能获得接近最优的线性检测性能,但是涉及复杂度较高的矩阵求逆运算.本文基于Kaczmarz迭代提出一种低复杂度软输出信号检测算法,在算法实现中避免了矩阵求逆运算,将实现复杂度由O（K³）降为O（K²）.同时,引入了最优松弛参数进一步加快算法收敛,最后给出了两种用于信道译码的LLR的近似计算方法.仿真结果表明：所提出的Kaczmarz迭代软输出信号检测算法经过两到三次简单的迭代即可较快地收敛,并达到接近MMSE检测算法的误码率性能的水平,其性能与复杂度均优于基于矩阵近似求逆的一类检测算法.     

基于传输信号源非圆特性的MIMO检测算法研究

针对传输非圆信号的多输入/输出(MIMO)通信系统提出了几种新检测算法,可估计来自接收信号及其复共轭的传输信号,并针对性能改进给出了相关分析结果。所提算法的计算复杂度在阶数方面与传统迫零(ZF)相同。仿真结果表明,扩展迫零(EZF)及扩展最小均方误差(EMMSE)检测器均优于最小均方误差(MMSE)检测器,且EZF或EMMSE的排序串行干扰消除(OSIC)法在误码率(BER)方面具有准最优性能。     

一种改进的NLMS算法在声回波抵消中的应用

收敛速度和残余均方误差是衡量最小均方算法性能的重要指标。在声回波抵消算法中,为了寻求收敛速度快和计算量小的自适应算法,在归一化最小均方误差算法基础上,把当前时刻以前的误差引入归一化收敛因子中得到一种新算法,可以减小信号样本波动对权重带来的影响。该算法比传统的归一化最小均方算法收敛性能更好,稳态失调也比其小。计算机仿真结果表明,新算法在自适应回波抵消中的综合性能要优于传统的归一化最小均方误差算法。     

鲁棒总体均方最小自适应滤波:算法与分析

本文研究了在输入输出观测数据均含有噪声的情况下如何有效地进行鲁棒自适应滤波的问题.以总体均方误差(TMSE)最小为准则,基于最速下降原理,通过对总体均方误差梯度进行修正,提出了一种鲁棒的总体均方最小自适应滤波算法.通过与已有算法的对比分析表明,该算法能够有效地降低权向量的每步调整量对噪声的敏感程度.仿真实验的结果进一步表明,该算法的鲁棒抗噪性能和稳态收敛精度明显地高于其它同类方法,而且可以使用较大的学习因子,在高噪声环境下仍然保持良好的收敛性.     

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。     

少模光纤通信系统中的自适应频域均衡算法

少模光纤模式复用存在模式耦合和差分模式时延,必须通过自适应均衡算法补偿。为了降低长距离少模光纤通信系统中自适应均衡算法的复杂度,采用基于变步长-频域块最小均方算法的多输入多输出均衡器对2×2模分复用系统解复用。利用频域块最小均方自适应算法修正均衡器权系数,并通过变步长函数调整步长因子,兼顾算法收敛速度和收敛性能。算法可通过快速傅里叶变换降低计算复杂度。在112Gbit/s的1000km少模光纤高速通信仿真系统中,保证相同收敛速度情况下,提高信号Q2因子3.7dB,并在可编程现场门阵列上验证了100km少模光纤通信系统时的算法性能。结果表明,该算法能够实现模分复用系统的信号解复用,达到快速收敛、低稳态失调的目的。     

全双工射频自干扰改进时变步长NLMS对消算法

针对现有基于最小均方误差准则的全双工射频域自干扰对消算法存在收敛速度与干扰对消比相互制约的矛盾,提出一种改进时变步长归一化最小均方算法。该算法通过建立最小均方误差步长因子与改进时变sigmod函数的非线性关系,利用实时误差信号自相关和时间参量t协同控制步长因子μ(t),较好的兼顾了收敛速度与干扰对消比。分析与仿真表明:在干信比为80dB、步进间隔Δt=1/32ms、信噪比Eb/N0=10 dB的2FSK全双工系统模型下,该算法能够实现88dB的自干扰消除高出同类算法至少1.5dB且收敛速度和抗突发干扰能力提升显著。      

卫星通信中改进型变步长LMS预失真算法

针对传统多项式预失真算法解决高功率放大器非线性失真时存在收敛速度慢和稳态均方误差大的缺点,将多项式预失真应用于卫星通信系统中,提出了一种改进型变步长LMS(Improved Variable Step-Size Least Mean Square,IP-VSSLMS)算法。该算法通过利用误差信号及误差信号与输入信号的平均能量,获得了一种新的变步长因子,有效地增强了步长因子的动态调整能力,提高了收敛速度和降低了均方误差。仿真结果表明,IP-VSSLMS算法能够有效地抑制信号的星座扭曲和频谱再生,解决了高功率放大器非线性失真的问题,相比传统算法,能够获得更快的收敛速度和较小的稳态均方误差。     

Minimum probability of error-based methods for adaptive multiuser detection in multipath DS-CDMA channels

Direct-sequence code-division multiple-access (DS-CDMA) is a popular multiple-access technology for wireless communications. However, its performance is limited by multiple-access interference and multipath distortion. Multiuser detection and space-time processing are two signal processing techniques employed to improve the performance of DS-CDMA. Two minimum probability of error-based space-time multiuser detection algorithms are proposed in this paper. The first algorithm, minimum joint probability of error (MJPOE), aims to minimize the joint probability of error for all users. The second algorithm, minimum conditional probability of error (MCPOE), minimizes the probability of error of each user conditioned on the transmitted bit vector, for each user individually. In both the algorithms, the optimal filter weights are computed adaptively using a gradient descent approach. The MJPOE algorithm is blind and offers a bit-error-rate (BER) performance better than the nonadaptive minimum mean squared error (MMSE) algorithm, at the cost of higher computational complexity. An approach for reducing the computational overheads of MJPOE using Gram-Schmidt orthogonalization is suggested. The BER performance of the MCPOE algorithm is slightly inferior to MMSE, however, it has a computational complexity linear in the number of users. Both blind and training-based implementations for MCPOE are proposed. Both MJPOE and MCPOE have a convergence rate much faster than earlier known adaptive implementations of the MMSE detector, viz. least mean square and recursive least squares. Simulation results are presented for synchronous single path channels as well as asynchronous multipath channels, with multiple antennas employed at the receiver.     

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(K 3),其中K表示用户数。为此,针对极化信道编码的大规模MIMO系统,基于无转置极小残差(Transpose-Free Quasi-Minimal Residual,TFQMR)方法,提出了一种低复杂度次优信号检测算法。该算法有效地避免了矩阵求逆运算,使其计算复杂度降至约O(K 2)。仿真结果表明,基于TFQMR的信号检测算法的误比特率性能与计算复杂度均优于基于Neumann级数展开的信号检测算法;同时,最多经5次迭代该方法可取得接近MMSE检测算法的性能。     

Transform domain conjugate gradient algorithm for adaptive filtering

This paper proposed a new normalized transform domain conjugate gradient algorithm (NT-CGA), which applies the data independent normalized orthogonal transform technique to approximately whiten the input signal and utilises the modified conjugate gradient method to perform sample-by-sample updating of the filter weights more efficiently. Simulation results illustrated that the proposed algorithm has the ability to provide a fast convergence speed and lower steady-error compared to that of traditional least mean square algorithm (LMSA), normalized transform domain least mean square algorithm (NT- LMSA), Quasi-Newton least mean square algorithm (Q-LMSA) and time domain conjugate gradient algorithm (TD-CGA) when the input signal is heavily coloured.     

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).     

Reduce‐complexity fuzzy‐inference‐based iterative multiuser detection for wireless communication systems

In recent years, combining multiuser detection and intelligence computer scheme have received considerable attention. In this paper, adaptive fuzzy‐inference multistage matrix wiener filtering (FI‐MMWF) techniques, based on the minimum mean‐square error criterion, are proposed for ultra‐wideband (UWB) impulse radio communication systems. These FI‐MMWF‐based algorithms employ a time‐varying fuzzy‐inference‐controlled filter stage. Consequently, the proposed approaches accomplish a substantial saving in complexity without trading off the system performance and dynamic‐tracking characteristic. In addition, the fuzzy‐logic‐controlled matrix conjugate gradient algorithm is adopted to reduce the system complexity without trading off the bit‐error‐rate (BER). Simulations are conducted to evaluate the convergence and tracking behavior of the proposed MMWF algorithm, and the BER of the time‐hopping‐UWB system in a realistic UWB channel is investigated. Copyright © 2011 John Wiley & Sons, Ltd.     

5G系统中基于解调参考信号的信道估计方法

5G系统使用解调参考信号(Demodulation Reference Signal,DMRS)用于下行信道估计,工程中通常采用插值法得到数据位置信道响应,考虑实际信道中噪声的影响,频域采用线性最小均方误差(Linear Minimum Mean Squared Error,LMMSE)插值算法。针对LMMSE需要获取信道先验统计特性以及存在矩阵求逆运算量大问题,利用信号时域内能量集中特点估计出信道均方根时延、信噪比和信道自相关矩阵,对于DMRS信号在频域上间隔较大、DMRS信号所在子载波相关性不强的问题,通过引入导频加密方法提升DMRS信号所在子载波间的相关性以提升信道估计性能,并通过滑动窗口插值方法进一步降低LMMSE算法求逆运算复杂度。仿真结果表明,所提算法均方误差和误码率总体均优于线性插值和基于矩阵奇异值分解的LMMSE算法,并与传统LMMSE算法相比性能极为接近,而且复杂度降低了99.85%,适合实际工程应用。     

Conjugate gradient acceleration of maximum-likelihood imagerestoration

A maximum-likelihood iterative deconvolution algorithm is accelerated using a conjugate gradient technique with a minimum mean square error criterion. Significant acceleration over previous techniques is achieved with little additional computation. Restorations with and without noise are presented which indicate the number of iterations required reduces from O(N) to O(√N)     

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。     

Minimum symbol error rate multiuser detection using an effective invasive weed optimization for MIMO/SDMA–OFDM system

Space division multiple access–orthogonal frequency division multiplexing system has become a potential wireless communication system by offering high spectral efficiency, performance and capacity. This article deals with minimum symbol error rate (MSER)‐based multiuser detection (MUD) technique for the space division multiple access–orthogonal frequency division multiplexing system using an efficient invasive weed optimization (IWO) algorithm. The IWO algorithm is used for finding optimal weights such that the probability of error is directly minimized rather than minimizing the mean square error. Because of this, the MSER MUD is able to detect users even in overload scenario, where the number of users is more than the number of receiving antennas, unlike several classical detection techniques. The IWO is inspired from the nature of invasive colonization of weeds and relatively simple compared with other optimization techniques. The bit error rate performance of the proposed IWO‐aided MSER MUD is found to be better than minimum means square error and differential evolution algorithm‐aided MSER MUDs. Simulation results show that the proposed IWO MSER achieves faster convergence and lower complexity compared with the differential evolution MSER MUD. Copyright © 2013 John Wiley & Sons, Ltd.