基于Hammerstein模型的非线性信道广义线性盲均衡算法

为了提高非线性信道盲均衡的性能、降低运算复杂度,本文以Hammerstein模型代替传统的Volterra级数模型来模拟非线性信道,利用非线性信道接收信号呈现非圆性的特点,构造了一种新的基于Wiener非线性模型的广义线性盲均衡器,并在常模准则的基础上提出了NCWL-CMA和NCWL-CMA Newton-like两种非线性信道广义线性盲均衡器抽头系数更新算法.理论分析和仿真实验结果表明,与传统盲均衡算法相比,新算法显著地降低了剩余码间干扰,提高了收敛速度.     

基于最小二乘支持向量机的自适应盲均衡器

提出了一种普适性较强的基于最小二乘支持向量机(LSSVM)的自适应盲均衡器(ABSVME)。该方法根据信号的特征恢复思想,将LSSVM均衡器的输出进行过采样,构造具有时间去相关特性的代价函数,结合Kumar快速算法和静态迭代学习算法在线跟踪信道。通过仿真实验,并与传统恒模盲均衡器和最大似然序列估计均衡器进行比较,结果证明该方法具有优良的非线性均衡能力。     

Blind equalization algorithm based on complex support vector regression

A new blind equalization algorithm for complex valued signals was proposed based on the framework of complex support vector regression(CSVR).In the proposed algorithm,the error function of multi-modulus algorithm (MMA) was substituted into CSVR to construct the cost function,and the regression relationship was established by widely linear estimation,and the equalizer coefficients were determined by the iterative re-weighted least square (IRWLS) method.Different from spliting the complex valued signals into real valued signals used in support vector regression,the Wirtinger’s calculus was used in complex support vector regression to analyze the complex signals directly in the complex regenerative kernel Hilbert space.Simulation experiments show that for QPSK modulated signals,compared with the blind equalization algorithm based on support vector regression,the equalization performance of the proposed algorithm is significantly improved in linear channel and nonlinear channel by choosing appropriate kernel function and iterative optimization method.     

Blind equalization for short burst wireless communications

In this paper, we propose a dual mode blind equalizer based on the constant modulus algorithm (CMA). The blind equalizer is devised for short burst transmission formats used in many current wireless TDMA systems as well as future wireless packet data systems. Blind equalization is useful for such short burst formats, since the overhead associated with training can be significant when only a small number of bits are transmitted at a time. The proposed equalizer overcomes the common problems associated with classic blind algorithms, i.e., slow convergence and ill-convergence, which are detrimental to applying blind equalization to short burst formats. Thus, it can eliminate the overhead associated with training sequences. Also, the blind equalizer is extended to a two branch diversity combining blind equalizer. A new initialization for fractionally spaced CMA equalizers is introduced. This greatly improves the symbol timing recovery performance of fractionally spaced CMA equalizers with or without diversity, when applied to short bursts. Through simulations with quasi-static or time-varying frequency selective wireless channels, the performance of the proposed equalizer is compared to selection diversity and conventional equalizers with training sequences. The results indicate that its performance is far superior to that of selection diversity alone and comparable to the performance of equalizers with short training sequences. Thus, training overhead can be removed with no performance degradation for fast time-varying channels, and with slight performance degradation for static channels     

Adaptive blind equalization for MIMO systems uadaptive blind equalization for MIMO systems under α-stable noise environmentnder α-stable noise environment

The presence of non-Gaussian impulsive noise in wireless system can degrade the performance of existing equalizers and signal detectors. In this paper, the problem of blind source separation and equalization for MIMO systems in heavy-tailed impulsive noise is studied. A generalized multiuser constant modulus cost function by employing the fractional lower-order statistic of the equalizer input signal is proposed. The associated adaptive blind equalization algorithm based on a stochastic gradient descent method is defined as fractional lower-order multi-user constant modulus algorithm (FLOS MU CMA). Computer simulations are presented to illustrate the performance of the new algorithm.     

Blind per tone equalization of multilevel signals using support vector machines for OFDM in wireless communication

We present an efficient support vector machine (SVM)-based blind per tone equalization for OFDM systems. Blind per tone equalization using constant modulus algorithm (CMA) and multi-modulus algorithm (MMA) are used as the comparison benchmark. The SVM-based cost function utilizes a CMA-like error function and the solution is obtained by means of an iterative re-weighted least squares algorithm (IRWLS). Moreover, like CMA, the error function allows to extend the method to multilevel modulations. In terms of bit error rate (BER), simulation experiments show that the blind per tone equalization using SVM performs better than blind per tone equalization using CMA and MMA.     

Efficient reinitialization of the prewhitened constant modulusalgorithm

We present a reinitialization scheme for blind equalizers adapted via the constant modulus algorithm (CMA) when an all-pole prefilter is included to whiten the received signal. The mechanism exploits the special structure of the minimum mean squared error (MMSE) equalizers and their relation with CMA equalizers. A heuristic rule for blind determination of the best equalization delay is also provided. Using these guidelines, the equalizer is capable of finding the optimal setting in an online and computationally efficient fashion. In particular, estimation and inversion of the channel output autocorrelation matrix is not needed, in contrast with previous approaches     

瑞利衰落电离层信道的自适应盲均衡

研究通过具有加性高斯白噪声的瑞利慢衰落电离层信道的恒模盲均衡技术。著名的恒模算法(CMA)是一种重要的盲信道均衡方法。但是，普通恒模算法存在收敛速度慢和相位旋转等不足。采用一种修正恒模算法(MCMA)，该算法使修正的误差函数最小并且自适应学习率由接收序列即时调整。两种算法用8—PSK信号进行了盲均衡比较，模拟结果显示修正恒模算法比普通恒模算法的收敛速度快，符号间干扰(ISI)小，另外该算法均衡器输出具有既无相位旋转也无延迟等优点。     

复值的MBER非线性均衡器用于QAM信号

本文提出一种复值的最低误码率非线性滤波器用于非线性信道中QAM信号的均衡.推导了针对QAM信号的最低误码率准则训练算法的目标函数,并用Voherra序列来实现复值的非线性均衡器.为使非线性均衡器能在线自适应训练并增加训练算法的数值稳定性,提出～种滑窗随机梯度算法.大量仿真表明,对于非线性信道中QAM信号的均衡,最低误码率非线性均衡器的性能优于最小均方误差准则.     

On the convergence of blind adaptive equalizers for constantmodulus signals

This paper studies the behavior of the error sequence of stop-anti-go variants of two adaptive blind equalizers, namely CMA2-2 and Sato's (1975) algorithm. It is shown that for transmitted signals with constant modulus γ, the equalizer output can be made to lie within the circle of radius γ√c infinitely often, for some value of c that is only slightly larger than one     

基于模拟退火粒子群优化的恒模算法

传统的固定步长恒模盲均衡算法在收敛速度和稳态剩余误差之间存在着矛盾,影响了恒模算法的均衡性能。为了解决这一缺点,提高恒模算法的性能,本文在介绍恒模盲均衡原理的基础上,提出一种变步长恒模算法,利用粒子群优化算法对均衡器的权向量进行优化,为避免优化过程进入局部极小值引入了模拟退火算法。通过在短波信道中的仿真,证明了该算法不仅加快了收敛速度,并且具有更高的收敛精度。     

基于粒子群优化的正交小波盲均衡算法

为克服常数模算法(CMA)收敛速度慢、稳态误差大的缺点,在分析正交小波常数模盲均衡算法(WT-CMA)基础上,该文提出了基于粒子群优化的正交小波常模盲均衡算法(PSO-WT- CMA)。该算法利用粒子群的信息共享机制和有效的全局搜索特点,寻找最优的均衡器权值,并用正交小波变换降低信号的自相关性。水声仿真结果表明:与常数模算法(CMA)、基于粒子群优化的常数模盲均衡算法(PSO-CMA)和基于正交小波变换的常数模盲均衡算法(WT-CMA)相比,该算法在提高收敛速度和减小码间干扰方面的性能有很大的改善。     

基于最大相关熵准则的恒模盲均衡算法

针对恒模算法(constant modulus algorithm, CMA)在脉冲噪声环境下性能退化的问题,本文基于最大相关熵准则(maximum correntropy criterion, MCC)对恒模算法中基于最小均方误差(mean square error, MSE)准则的代价函数进行修正,推导出适用于脉冲噪声环境的基于MCC准则的恒模盲均衡算法(MCC_CMA)。该算法利用通信信号的恒模特性,首先得到发送信号与均衡器输出信号模值的误差信号,再通过使模值误差信号的相关熵最大来获得其迭代误差调节项,避免了传统高阶统计量算法在脉冲噪声环境下性能退化的问题。对高斯噪声以及α-稳定分布和混合高斯分布两种脉冲噪声环境下的信道均衡问题的仿真实验表明,相对于经典的自适应恒模盲均衡算法,MCC_CMA算法不依赖噪声的先验知识就能获得较快的收敛速度、较低的剩余码间干扰和误码率,并且在不同脉冲强度的脉冲噪声环境下都能够得到较好的均衡结果,表明MCC_CMA算法具有很好的鲁棒性。      

基于支持向量机的非线性信道盲均衡算法研究

针对非线性信道盲均衡问题,考察了一种基于支持向量机的单输入单输出(SISO)盲均衡算法,该算法利用通信信号的常数模特性,将非线性盲均衡问题转化为非线性支持向量回归问题。在此基础上,本文利用分集技术,将该算法拓展至单输入多输出(SIMO)的情况。对两种算法进行的计算机仿真表明,基于支持向量机的SISO盲均衡算法能够有效地抑制信道中的非线性码间干扰;本文提出新算法由于更好地利用了信道的时空特性,具有剩余平均模误差小的优点。     

Timing phase offset recovery based on dispersion minimization

This paper presents a blind timing phase offset recovery scheme that attempts to optimize the baud spaced equalizer output mean square error (MSE) for a realistic equalizer length that is usually shorter than the ideal length. Among the existing blind timing recovery schemes, few are designed for equalizer output MSE optimization, and none are designed for the realistic case when the equalizer is short. The proposed algorithm (that is based on a cost function that minimizes the dispersion of the received signal) attempts to minimize the MSE of a one-tap equalizer output. It also exhibits good performance for relatively short equalizers. Conditions for the unimodality of the dispersion minimization cost are investigated, and a geometric relationship to the minimum MSE (MMSE) timing offset is shown qualitatively. The detailed MSE performance of the algorithm is investigated for the representing classes of channels by comparing existing blind timing offset estimation schemes.     

基于混合遗传算法及最低误码率准则的几何特征均衡器

针对高效调制通信系统中带内干扰抑制问题,提出一种基于最低误码率准则的非线性几何特征均衡器,并用径向基函数神经网络来实现.为优化非线性均衡器的参数训练,本文构造了一种新的遗传随机梯度混合算法.仿真表明:对于扩展的二元相移键控信号,在相对强的窄带干扰下,匹配滤波器及线性均衡器已失效,而基于最低误码率准则的几何特征均衡器仍能表现出良好的性能,也大大优于基于最小均方误差准则的非线性均衡器.     

基于改进高斯核函数的雷达高分辨距离像目标识别算法研究

针对支持向量机的核函数选择问题,在保形变换方法的基础上,分析了确定数据分布特征的重要性,给出了判断数据呈高斯分布的方法。利用支持向量机的高斯核函数,提出一种基于改进的高斯核函数雷达目标高分辨距离像的研究方法。该方法对SVM的高斯核函数进行改进,并进行核函数选择。通过改进的高斯核函数与多项式核函数进行比较,在Matlab环境下采用两种方法对高分辨距离像进行仿真,仿真方法验证并改进了高斯核函数的有效性。     

A Blind Equalizer Based on Unsupervised Gaussian Cluster Formation with an Adaptive Non—Linearity

In this paper we present a blind equalizer algorithm based on an unsupervised Gaussian cluster formation technique with an optimized gradient adaptive step-size to update the equalizer coefficients.The novelty of this work lies in the optimization of the nonlinearity of the cluster formation used to achieve an optimal soft decision.The proposed iterative procedure combined with the variable step-size gradient-based adaptation,significantly accelerates the convergence speed of the blind equalization.The advantages of the proposed equalization techniques are illustrated by simulation.Simulation results obtained are compared with the Sato and Godard blind equalizers.     

自适应均衡算法在信道均衡技术中的应用研究

文中描述了两种非线性均衡器分别为判决反馈均衡器(DFE)和最大似然序列估计(MLSE)均衡器.所用信道模型为加性白高斯噪声信道,在DFE和线性均衡器(LE)中都是使用递归最小二乘(RLS)算法和最小均方(LMS)算法对数据进行分块处理.MLSE均衡器中使用了维特比最佳译码算法.就误比特性能来做以比较,DFE远好于LE,MLSE均衡器又明显优于DFE,并且它能达到几乎最优的性能.     

基于序列检测的块判决辅助Turbo均衡器

该文针对高斯分布假设条件下逐符号检测均衡器的不足,提出了一种基于序列检测的块判决辅助Turbo均衡器(Turbo-BDAE)。新算法利用译码全信息估计发送序列,提高了序列估计的可靠性,引入软判决可以降低硬判决引起的错误扩散,同时结合序列检测为均衡器输出的后验概率(APP)估计提供了更可靠的信息。仿真表明,该均衡器在保持相近复杂度的同时,进一步提升了均衡系统性能,优于逐符号LMMSE均衡器和硬反馈BDFE。