基于RENYI熵的水声信道判决反馈盲均衡算法研究

在水下通信系统中,为了抑制由多径效应产生的严重码间干扰,必须进行信道均衡。针对传统的常数模判决反馈盲均衡(CMA-DFE)收敛速度较慢的问题,该文提出了一种基于RENYI熵的判决反馈盲均衡算法(RENYI-DFE)。该算法使用RENYI熵算法调节均衡器前向权向量,用CMA算法调节均衡器反馈权向量,与CMA-DFE相比,该算法在计算量增加很小的情况下,使得盲均衡算法的收敛速度显著增加。仿真结果证明了该算法的优越性。     

基于多小波双变换的非线性卫星信道盲均衡算法

针对非线性卫星信道Volterra盲均衡系统收敛缓慢、计算复杂高等不足,提出了基于多小波双变换的非线性卫星信道盲均衡算法.该算法用Wiener均衡器代替Volterra均衡器,减小了均衡器结构的复杂性;用平衡正交多小波对Wiener均衡器的输入信号进行变换,降低了输入信号的自相关性;在Wiener均衡器输出端增加一级判决反馈滤波器,同时对其输入信号作平衡多小波变换,又降低了判决反馈滤波器输出信号的自相关性.仿真结果验证了该算法的有效性.     

基于DD-LLMS算法的盲自适应判决反馈均衡器

本文把用于自适应线性滤波器的LLMS算法推广到盲判决反馈均衡器,并应用于短波信道的盲均衡。仿真结果表明这种DD—LLMS盲判决反馈均衡算法具有很好的稳定性、较快的收敛速度和较好的跟踪性能。     

应用粒子滤波器实现混沌通信系统的盲信道均衡

粒子滤波器(Particle filter,PF)是一种结合重要性权重抽样的序贯蒙特卡罗方法,能够应用到任意状态空间模型,并且能较好地估计经过非线性变化后的随机变量的统计特性.本文应用粒子滤波器和信号建模技术研究混沌通信系统的盲信道均衡问题,发展基于混沌的通信系统的盲均衡技术.仿真结果证实了,当Logistic映射作为混沌发生器和通信场景为固定参数与时变衰落信道时,该盲信道均衡器与基于扩展卡尔曼滤波算法的盲均衡器和基于无先导变换的自适应盲均衡器相比,有较好的均衡实现.此外,利用本文的盲均衡算法,实现了一种混沌调制通信系统的解调.     

A DECISION FEEDBACK EQUALIZER FOR NON-LINEAR CHANNELS

A Simple and useful decision feedback equalizer used for non-linear channels with severe linear distortion and mild non-linear distortion is proposed. It is a combination of a nonlinear channel equalizer based on connectionist model and a common decision feedback equalizer for linear channels. For a typical non-linear channel model it is shown that the equalization performances of the proposed equalizer are improved significantly.     

非线性信道判决反馈均衡器

本文针对严重线性失真、轻度非线性失真的信道提出了一种简单实用的非线性信道判决反馈均衡器。它是一个将关联模型非线性信道均衡器与线性信道判决反馈均衡器相结合的产物。对典型的非线性信道模型所做的模拟试验表明,该均衡器不仅简单实用、便于实时处理,而且均衡性能得到较大的改善。     

基于DD-LMS和MCMA的盲判决反馈均衡算法研究

文章对具有DFE结构的盲均衡算法作了研究,在一种修正常模算法(MCMA)代价函数中引入泄漏因子,并将常模算法(CMA)和直接判决-最小均方误差算法(DD-LMS)同时应用到盲判决反馈均衡器的抽头更新中,得到一种适用范围广?均衡特性好?变步长的DD-LLMS MCMBDFE算法。该算法在均衡的同时能自动补偿由信道引起的相位误差,收敛速度快,收敛后剩余误差小,同时还能克服当均衡器长期没有持续输入激励时,LMS算法产生的抽头系数漂移问题。仿真结果表明DD-LLMS MCMBDFE算法是一种有效的盲判决反馈均衡算法。     

l0-范数约束的稀疏多径信道分数间隔双模式盲均衡算法

针对深衰落稀疏多径信道下多进制相移键控（Multiple Phase Shift Keying,MPSK）信号的盲均衡问题,提出了一种l₀-范数约束的分数间隔稀疏自适应双模式盲均衡算法.该算法借鉴传统的分数间隔双模式盲均衡算法思想,结合稀疏自适应滤波理论,首先利用l₀-范数对均衡器抽头系数进行稀疏性约束,构造出一种l₀-范数约束的分数间隔双模式最小均方误差代价函数,然后依据梯度下降法推导出盲均衡器抽头系数更新公式,并对迭代步长进行归一化和比例系数化.理论分析和仿真实验表明,与基于门限稀疏化的盲均衡算法、基于分数阶范数的盲均衡算法及分数间隔双模式盲均衡算法相比,本文所提算法在保证较快收敛速度的前提下,能有效降低剩余符号间干扰.本文设计的盲均衡算法为水声通信系统中接收方恢复出发送信号,提供了一种快速有效的方法.     

时变信道的盲均衡

本文把求解接收信号相关矩阵和自适应盲均衡代价函数法结合起来，提出了一种适合时变信道的盲均衡新算法．计算机模拟表明：本文的方法提高了盲均衡的收敛速度，且均衡器在时变HF短波信道上发散时，能在200～300点之内把均衡器恢复到正常工作状态．     

Blind equalizer for constant-modulus signals based on Gaussian process regression

A new blind equalization method for constant modulus (CM) signals based on Gaussian process for regression (GPR) by incorporating a constant modulus algorithm (CMA)-like error function into the conventional GPR framework is proposed. The GPR framework formulates the posterior density function for weights using Bayes' rule under the assumption of Gaussian prior for weights. The proposed blind GPR equalizer is based on linear-in-weights regression model, which has a form of nonlinear minimum mean-square error solution. Simulation results in linear and nonlinear channels are presented in comparison with the state-of-the-art support vector machine (SVM) and relevance vector machine (RVM) based blind equalizers. The simulation results show that the proposed blind GPR equalizer without cumbersome cross-validation procedures shows the similar performances to the blind SVM and RVM equalizers in terms of intersymbol interference and bit error rate.     

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     

Application of Recurrent Wavelet Neural Networks to the Digital Communications Channel Blind Equalization

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Fast and low complexity blind equalization via subgradient projections

We propose a novel blind equalization method based on subgradient search over a convex cost surface. This is an alternative to the existing iterative blind equalization approaches such as the Constant Modulus Algorithm (CMA), which often suffer from the convergence problems caused by their nonconvex cost functions. The proposed method is an iterative algorithm called SubGradient based Blind Algorithm (SGBA) for both real and complex constellations, with a very simple update rule. It is based on the minimization of the l/sub /spl infin// norm of the equalizer output under a linear constraint on the equalizer coefficients using subgradient iterations. The algorithm has a nice convergence behavior attributed to the convex l/sub /spl infin// cost surface as well as the step size selection rules associated with the subgradient search. We illustrate the performance of the algorithm using examples with both complex and real constellations, where we show that the proposed algorithm's convergence is less sensitive to initial point selection, and a fast convergence behavior can be achieved with a judicious selection of step sizes. Furthermore, the amount of data required for the training of the equalizer is significantly lower than most of the existing schemes.     

一种不受信道阶数估计影响的SIMO直接盲均衡算法

针对单输入多输出（Single-Input-Multiple-Output, SIMO）模型提出一种完全不需要信道阶数估计的直接盲均衡算法。文章利用接收数据的截短协方差矩阵和信号子空间的关系设计一种零延迟均衡器,并通过信道矩阵和均衡器系数的合响应特性克服了算法相位偏转的问题,最后得到一种对信道阶数估计鲁棒并且没有相位偏转的盲均衡算法。该算法不同于一般子空间类算法,不需要直接对接收信号的协方差矩阵进行信号子空间和噪声子空间的分解,因此对信道阶数估计具有很强的鲁棒性。文章给出了算法的Batch实现过程,同时为更好适应一般时变信道环境和实现实时处理的要求,通过递归迭代得到算法的自适应实现过程。仿真实验表明该算法几乎不受信道阶数过估计或欠估计的影响,同时该算法具有良好的均方误差（Mean Square Error, MSE）和误符号率SER（Symbol Error Rate, SER）性能,并且具有很快的收敛速度。      

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。     

自适应递推最小二乘灰色均衡器用于时变信道

本文提出了适用于时变信道的自适应递推最小二乘灰色均衡的新思想，以收敛性比较。最小二乘灰色均衡器优于传统的最小二乘均衡器，而以时变信道上的误码率来看，其性能是盲均衡器盲均衡所不能比拟的，而且也优于传统的均衡器，理论分析及计算机模拟表明，自适应最小二乘灰色均衡器是时变信道上性能优良的均衡。     

Blind channel identification and equalization withmodulation-induced cyclostationarity

Recent results have pointed out the importance of inducing cyclostationarity at the transmitter for blind identification and equalization of communication channels. This paper addresses blind channel identification and equalization relying on the modulation-induced cyclostationarity, without introducing redundancy at the transmitter. It is shown that single-input single-output channels can be identified uniquely from output second-order cyclic statistics, irrespective of the location of channel zeros, color of additive stationary noise, or channel order overestimation errors, provided that the period of modulation-induced cyclostationarity is greater than half the channel length. Linear, closed-form, nonlinear correlation matching, and subspace-based approaches are developed for channel estimation and are tested using simulations. Necessary and sufficient blind channel identifiability conditions are presented. A Wiener cyclic equalizer is also proposed     

A fast linear programming algorithm for blind equalization

A fast implementation of a special non-MSE cost function for blind equalization is presented here. This baud-rate equalization algorithm is based on a convex cost function coupled with a simple linear constraint on the equalizer parameters. For a generic class of channels with persistently exciting quadrature amplitude modulation input signals, this new algorithm allows the convergence of equalizer parameters to a unique global minimum achieving intersymbol interference suppression and carrier phase recovery     

The soft-feedback equalizer for turbo equalization of highly dispersive channels

The complexity of a turbo equalizer based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm is manageable only for mildly dispersive channels having a small amount of memory. To enable turbo equalization of highly dispersive channels, we propose the soft-feedback equalizer(SFE). The SFE combines linear equalization and soft intersymbol-interference cancellation. Its coefficients are chosen to minimize the mean-squared error(MSE) between the equalizer output and the transmitted sequence, under a Gaussian approximation to the a priori information and the SFE output. The resulting complexity grows only linearly with the number of coefficients, as opposed to the quadratic complexity of previously reported minimum-MSE structures. We will see that an SFE-based turbo equalizer consistently outperforms another structure of similar complexity, and can outperform a BCJR-based scheme when complexity is taken into account.     

利用无约束函数的QAM信号自适应盲均衡方法

本文针对正交幅度调制(QAM)信号的盲均衡与载波相位恢复问题,首先在对现有的基于功率最大化盲均衡算法分析的基础上,依据向量范数间的关系,给出了一种QAM信号盲均衡与载波相位恢复的无约束代价函数;然后对代价函数的收敛性和解的唯一性进行严格论证,证明代价函数在取得最优解时能够同时实现盲均衡与载波相位恢复;之后将其转化为采用随机梯度法实现的自适应算法。相对CMA、MMA等其他自适应算法,新算法可获得更好的性能,通过对高阶QAM信号的均衡仿真,进一步验证了算法的有效性。