Complex Adaptive LMS Algorithm Employing the Conjugate Gradient Principle for Channel Estimation and Equalization

The Complex Block Least Mean Square (LMS) technique is widely used in adaptive filtering applications because of its simplicity and efficiency from a theoretical and implementation standpoint. However, the limitations of the Complex Block LMS technique are slow convergence and dependence on the proper choice of the stepsize or convergence factor. Moreover, its performance degrades significantly in time-varying environments. In this paper, a novel adaptive LMS technique named the Complex Block Conjugate LMS algorithm, CBC-LMS, is presented. Based on the Conjugate Gradient Principle, the proposed technique searches orthogonal directions to update the filter coefficients instead of the negative gradient directions used in the Complex Block LMS algorithm. In addition, the CBC-LMS algorithm derives optimal stepsizes to adjust the adaptive system coefficients at each iteration. As a result, the developed method overcomes the inherent limitations of the existing Complex Block LMS algorithm. The performance of the CBC-LMS technique is tested in wireless channel estimation and equalization applications, using both computer simulations and laboratory experiments. Furthermore, the developed technique is compared to the Complex Block LMS method and a recently proposed method, which is called Complex Optimal Block Adaptive LMS (OBA-LMS). The experimental and simulation results confirm that the proposed CBC-LMS technique achieves faster convergence with comparable accuracy and reduced computational complexity, relative to the existing techniques.     

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。     

VoIP电话中的回声消除算法

声学回声是降低VoIP通话通信质量的重要问题之一,自适应回声抵消是抑制回声的最有效方法之一,其采用自适应滤波器评估回声路径。常用的NLMS(Normalized Least Mean Square)算法计算复杂度高,实用性差,本文利用FFT技术,实现了NLMS频域快速算法FDNLMS(Frequency Domain Normalized Least Mean Square),将自适应更新变换到频域,逐块进行累加更新,保证收敛性能的同时,极大的降低了运算复杂度。实验表明,在滤波器系数为1024阶时,FDNLMS算法的处理速度比NLMS快12倍。     

一种用于QAM解调信号的LMS自适应均衡器

设计了一种用于QAM(Quadrature Amplitude Modulation)解调信号的LMS自适应均衡器。此均衡器采用线性自适应算法中的最小均方算法(LMS)．其结构由线性横向滤波器和需要训练序列的滤波器抽头系数更新模块组成．它可实现16／64／256点的QAM解调。利用MATLAB／Simulink对LMS自适应均衡器的收敛速度、误码率等指标进行仿真模拟,仿真结果表明,此LMS自适应均衡器对通过非理想信道的QAM传输信号具有较好的均衡性能。     

A Robust Least Mean Square Algorithm for Adaptive Array Signal Processing

Least Mean Square (LMS) has been the most popular scheme in the realization of adaptive beamforming algorithms. In this paper a Robust Least Mean Square (R-LMS) algorithm is proposed which uses ratio parameters to control the contribution of product vectors in the weight upgrading process. The idea behind the proposed scheme is inclusion of previous information in place of relying solely on current sample. The performance enhancement by R-LMS algorithm is achieved with insignificant increase in computational complexity of LMS algorithm, so the crux of the conventional technique is not lost. Simulation results are also presented which illustrate that R-LMS provides relatively fast convergence, less Brownian motion and improved stability.     

MIMO-OFDM系统中一种基于自适应滤波的信道估计方法

该文提出了一种适用于MIMO-OFDM系统的基于自适应滤波器的信道估计方法,此方法在不需要任何信道统计信息的前提下,通过自适应滤波的方法对时变信道状态参数进行即时跟踪与估计。仿真结果表明该文提出的基于自适应滤波的信道估计方法,相比于不考虑噪声的基于LS算法的信道估计方法,MSE和BER性能均有很大的提高。其中基于LMS滤波器的信道估计方法具有计算复杂度小的特点;而基于RLS的信道估计方法具有收敛速度快,MSE和BER性能均优于基于LMS方法的特点。     

VoIP回声消除器设计及算法研究

本文提出了一种与线性预测编解码器相结合的新声学回声消除器,由去相关可变步长的NLMS自适应算法和基于回声路径失配方差的双端通话检测算法所组成.Matlab仿真结果表明,与Gordy所提出的回声消除算法相比,本文提出的算法在双端通话和回声路径改变时判别更准确,收敛速度更快;在收敛状态时,ERLE值平均提高了15dB,失调误差平均降低了10dB,具备更好的回声消除性能.     

SC-FDE系统中归一化FBLMS算法的研究

单载波频域均衡（SC-FDE）系统传统上采用最小均方误差（MMSE）算法和快速块最小均方（FBLMS）算法进行频域均衡,文中对单载波频域均衡系统中归一化FBLMS算法进行了仿真研究,并与MMSE算法和FBLMS算法进行比较。测试和分析了系统的均方误差和误码率。仿真结果表明：该算法克服了收敛速度和失调量之间的矛盾,提高了算法的收敛速度,降低了误码率,该结果对SC-FDE系统的进一步研究具有参考价值。     

Partial-Update L∞ -Norm Based Algorithms

The computational complexity of an adaptive filtering algorithm increases with increasing the filter tap length and therefore, the use of such a filter can become prohibitive for certain applications, especially for real-time implementation. In this paper, we develop low-complexity adaptive filtering algorithms by incorporating the concept of partial updating of the filter coefficients into the technique of finding the gradient vector in the hyperplane based on the L_infin-norm criterion. Two specific partial update algorithms based on the sequential and M-Max coefficient updating are proposed. The statistical analyses of the two algorithms are carried out, and evolution equations for the mean and mean-square of the filter coefficient misalignment as well as the stability bounds on the step size are obtained. It is shown that the proposed partial update algorithm employing the M-Max coefficient updating can achieve a convergence rate that is closest to that of the full update algorithm. Finally, simulations are carried out to validate the theoretical results and study the convergence rate of the proposed algorithms     

Low computational complexity family of affine projection algorithms over adaptive distributed incremental networks

This paper presents the problem of distributed estimation in an incremental network based on the family of affine projection (AP) adaptive algorithms. The distributed selective partial update normalized least mean squares (dSPU-NLMS), the distributed SPU-AP algorithm (dSPU-APA), the distributed selective regressor APA (dSR-APA), the distributed dynamic selection of APA (dDS-APA), dSPU-SR-APA and dSPU-DS-APA are introduced in a unified way. These algorithms have low computational complexity feature and close convergence speed to ordinary distributed adaptive algorithms. In dSPU-NLMS and dSPU-APA, the weight coefficients are partially updated at each node during the adaptation. In dSR-APA, the optimum number of input regressors is selected during the weight coefficients update. The dynamic selection of input regressors is used in dDS-APA. dSPU-SR-APA and dSPU-DS-APA combine SPU with SR and DS approaches. In these algorithms, the weight coefficients are partially updated and the input regressors are optimally/dynamically selected at every iteration for each node. In addition, a unified approach for mean-square performance analysis of each individual node is presented. This approach can be used to establish a performance analysis of classical distributed adaptive algorithms as well. The theoretical expressions for stability bounds, transient, and steady-state performance analysis of various distributed APAs are introduced. The validity of the theoretical results and the good performance of dAPAs are demonstrated by several computer simulations.     

Adaptive sigmoidal plant identification using reduced sensitivity recursive least squares

Logistic models, comprising a linear filter followed by a nonlinear memoryless sigmoidal function, are often found in practice in many fields, e.g., biology, probability modelling, risk prediction, forecasting, signal processing, electronics and communications, etc., and in many situations a real time response is needed. The online algorithms used to update the filter coefficients usually rely on gradient descent (e.g., nonlinear counterparts of the Least Mean Squares algorithm). Other algorithms, such as Recursive Least Squares, although promising improved characteristics, cannot be directly used due to the nonlinearity in the model. We propose here a modified Recursive Least Squares algorithm that provides better performance than competing state of the art methods in an adaptive sigmoidal plant identification scenario.     

新的变步长LMS算法在系统辨识中的应用

在分析最小均方误差(LMS)自适应滤波算法和变步长LMS算法的基础上,提出了一种新的变步长算法,该算法用误差的平均值来控制步长的变化,进一步的解决了收敛速度和稳态误差的矛盾。讲述了新算法的具体改进方式,并将该算法和变步长G-SVSLMS算法以及固定步长算法分别应用到系统辨识中,通过MATLAB进行仿真,结果证实文中提出的算法在明显提高收敛速度的同时,并拥有好的稳态误差。     

用SystemView仿真LMS自适应算法

最小均方误差(LMS)算法是自适应信号处理中最常用的算法.本文在给出LMS算法的原理的基础上,设计了一种单一频率的自适应陷波器的仿真方案.采用SystemView通信系统仿真工具,仿真了该自适应陷波器工作过程,给出了各点工作波形,并通过实验给出了不同参数条件下的陷波器收敛性能.实验结果表明,在合适的参数条件下,LMS算法可以兼顾收敛速度和稳态误差两方面的性能,实现性能良好的陷波器.同时,由于采用迭代算法,LMS算法更适合DSP或FPGA的数字实现.     

基于载波域自适应迭代滤波器的无源雷达多径杂波抑制方法

在无源雷达系统中,监测通道信号中存在零频和非零频多径杂波,影响目标的检测。时域自适应迭代滤波器(如LMS, NLMS, RLS等)常被用于无源雷达杂波抑制,但这些方法只适用于零频多径杂波。该文针对零频和非零频多径杂波的问题,结合数字广播电视信号的正交频分复用波形特征,提出一种基于载波域自适应迭代滤波器的杂波抑制算法。该算法利用同一载频下含有相同多普勒频移的多径杂波的相关性原理,进行杂波抑制。仿真和实测数据处理结果证明了算法的有效性。     

一种应用于限制零极点位置复数陷波器的迭代算法

基于最小均方误差准则(MMSE),本文推导出了一种修改限制零极点位置的一阶复数陷波器权值迭代算法,并进而提出了一种新的可应用于矩阵型高阶陷波器的自适应权值修改算法,该高阶陷波器由一阶陷波器作为陷波单元构成。该迭代算法直接修改陷波器权值的指数,因而在算法迭代过程中能够将高阶复数陷波器的极点始终限制在单位圆内,从而保证了陷波器的稳定工作。仿真结果表明,采用该算法的一阶和高阶复数陷波器工作稳定,对输入陷波器的宽带信号损伤小,且能快速跟踪和有效抑制其中的强单/多频信号。     

在脉冲噪声环境中用于快衰信道估计的改进型算法

该文分析了在存在噪声干扰的情况下,进行估计快衰信道的方法。在无线通信系统中,快衰信道可以采用AR(Auto-Regressive)模型进行预测,而LS (Least Square)算法和自适应Kalman滤波器可以分别对AR模型的参数和信道的冲激响应进行估计,但是这两种算法对噪声干扰非常敏感。该文提出改进型的RLM算法和Kalman 滤波器,并在存在噪声的情况下,使用它们并行对AR参数和信道的冲激响应进行联合估计。仿真结果显示:相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估的参数的收敛速度。     

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。     

Low-complexity data reusing methods in adaptive filtering

Most adaptive filtering algorithms couple performance with complexity. Over the last 15 years, a class of algorithms, termed "affine projection" algorithms, have given system designers the capability to tradeoff performance with complexity. By changing parameters and the size/scale of data used to update the coefficients of an adaptive filter but without fundamentally changing the algorithm structure, a system designer can radically change the performance of the adaptive algorithm. This paper discusses low-complexity data reusing algorithms that are closely related to affine projection algorithms. This paper presents various low-complexity and highly flexible schemes for improving convergence rates of adaptive algorithms that utilize data reusing strategies. All of these schemes are unified by a row projection framework in existence for more than 65 years. This framework leads to the classification of all data reusing and affine projection methods for adaptive filtering into two categories: the Kaczmarz and Cimmino methods. Simulation and convergence analysis results are presented for these methods under a number of conditions. They are compared in terms of convergence rate performance and computational complexity.     

Performance analysis of the smoothed least mean square (SLMS) algorithm

Stochastic gradient algorithms were thoroughly investigated. Results reported in the literature as well as experiments we have carried out indicate that their performance can be improved by the addition of a gradient smoothing element. This motivated us to present and study a version of the algorithm, with constant adaptation coefficients, which we named Smoothed Least Mean Square (SLMS). The well-known LMS algorithm turns out to be a special case of the SLMS. We develop equations governing the behavior of first- and second-order statistics of this algorithm and conditions for its convergence. Our study indicates that improved steady-state performance can be achieved by the additional smoothing process.     

一种改进的可变步长LMS算法及性能分析

为解决自适应算法的收敛速度和稳态误差两者间的矛盾,对归一化的最小均方(NLMS)算法、变步长算法及可变步长NVSS算法进行了研究,并结合变步长的思想,提出了一种新的可变步长算法。新的算法中引入合适的遗忘因子与修正参数来建立与步长因子间的函数关系,加快了算法收敛速度的同时,也能在非平稳的环境中有好的跟踪能力。最后把不同的算法应用到系统辨识系统中,通过MATLAB进行实验仿真,结果证实了新提出的算法有快的收敛速度和跟踪时变系统的能力。