线性滤波器与多项式级联结构的自适应均衡器*

针对严重的线性失真和适度的非线性失真的非线性信道,在Volterra非线性系统和非线性信道Wiener模型基础之上,研究了级联结构的自适应均衡器, 并推导出自适应算法,对其进行仿真。仿真表明,此均衡器结构不但结构简单,而且能够非常有效地消除码间和非线性干扰,改善信号的误码性能。     

线性滤波器与多项式级联结构的自适应均衡器

针对严重的线性失真和适度的非线性失真的非线性信道,在Volterra非线性系统和非线性信道Wiener模型基础之上,研究了级联结构的自适应均衡器, 并推导出自适应算法,对其进行仿真.仿真表明,此均衡器结构不但结构简单,而且能够非常有效地消除码间和非线性干扰,改善信号的误码性能.     

基于判决反馈结构的自适应均衡算法仿真研究

在数字通信中,接收信号通常会受到码间干扰的影响,尤其是在多径衰落无线信道环境中,这种现象更为严重。采用自适应均衡技术可以对信道响应进行补偿。由于在数字通信系统中,信道往往为非最小相位系统,此时线性均衡器性能不佳,因此该文对比研究了非线性结构的自适应波特间隔判决反馈均衡器和自适应分数间隔判决反馈均衡器,并对其性能进行了计算机仿真。仿真结果表明,对于非最小相位信道,自适应分数间隔判决反馈均衡器的性能优于波特间隔判决反馈均衡器。     

基于免疫算法的RBF网络在信道均衡中的应用

针对严重非线性失真信道,提出了一种基于免疫算法的径向基函数(RBF)网络自适应均衡器.这种均衡器引入了判决反馈均衡器的结构,并采用免疫算法确定RBF网络隐层(非线性层)的结构和参数.免疫 算法具有全局搜索能力,它通过引入多样性保持机制和免疫记忆机制提高了算法的优化效率,并在一定程度上克服了未成熟收敛现象.仿真结果表明,这种基于免疫算法的RBF网络均衡器性能优良,具有很强的抑制非线性失真的能力.􀁱     

Legendre神经网络非线性信道均衡

在分析Chebyshev正交多项式神经网络非线性滤波器的基础上，利用Legendre正交多项式快速逼近的优良特性以及判决反馈均衡器的结构特点，提出了两种新型结构的非线性均衡器，并利用NLMS算法，推导出自适应算法．仿真表明，无论通信信道是线性还是非线性，Legendre神经网络自适应均衡器与Chebyshev神经网络均衡器的各项性能均接近，而Legendre神经网络判决反馈自适应均衡器能够更有效地消除码间干扰和非线性干扰，误码性能也得到较好的改善．     

二阶Volterra变记忆长度LMP算法

针对实际应用中非线性系统记忆长度未知致使Volterra自适应滤波器可能无法达到最优性能的问题,提出一种二阶Volterra变记忆长度LMP算法。利用Volterra滤波器二阶权系数矩阵的对称性和对称矩阵可对角化分解性质,推导得到了一阶权系数与二阶权系数个数相同的信号矢量与权系数矢量内积的二阶Volterra滤波器输出信号表达式;提出了基于DCT的二阶Volterra自适应滤波器（CSVF）及其LMP算法（CSVLMP）;采用FIR抽头长度的自适应调整思想,提出了基于DCT的二阶Volterra变记忆长度LMP算法（CSVMLMP）。记忆长度未知的非线性系统辨识的仿真结果表明,在[α]稳定分布噪声背景下,该算法在收敛速度、稳态性能和计算复杂度之间达到了较好的折中。     

基于小波包变换的非线性信道均衡器

针对严重线性失真和轻度非线性失真的数字信道，为了提高基于最小均方误差算法的判决反馈均衡器的收敛速度，首先提出用一族正交小波包基函数来表示非线性信道判决反馈均衡器厦其输出，然后给出基于小渡包变换的非线性信道自适应均衡算法。该算法实现了小波包与非线性信道模型的结合，在计算量增加不多的前提下，利用小波包对小波空间的进一步划分以厦比小波变换更强的去相关能力来减小输入信号相关阵的条件数。对典型非线性信道模型的仿真结果表明，该算法可有效提高均衡器的收敛速度。     

基于再生核Hilbert空间的非线性信道均衡算法

在高速无线通信领域,为消除码间干扰（ISI）必须研究非线性信道均衡技术。基于再生核希尔伯特空间（RKHS）研究非线性信道的自适应均衡算法。首先基于非线性维纳模型提出均衡器的结构,基于RKHS引入核方法,与仿射投影算法（APA）相结合推导出核仿射投影算法（KAPA）,再通过引入松弛因子得到改进的KAPA算法。用蒙特卡罗法对提出的自适应算法进行仿真,从收敛性能、误码率（BER）、跟踪能力、计算复杂度等方面与其他算法做比较。在不增加计算复杂度的情况下,极大降低了误码率,非常适合时变非线性信道均衡的应用。     

非线性通信信道的神经FIR自适应幅值Laguerre均衡器

针对数字通信系统中线性和非线性信道干扰问题,在分析神经FIR滤波器、判决反馈结构和Laguerre滤波器的基础上,提出了神经FIR自适应幅值判决反馈均衡器和神经FIR自适应幅值Laguerre均衡器.其中神经FIR自适应幅值Laguerre均衡器结构简单,具有IIR和FIR滤波器特点,能够使用较少的阶数达到较好的均衡效果,且理论分析证明该均衡器是稳定的(0相似文献   

新型非线性信道判决反馈自适应均衡器*

针对信道的线性和非线性失真,在分析简化的非线性滤波器的基础上,利用判决反馈均衡器的结构特点对其进行扩展,提出了两种新型结构的判决反馈均衡器,并利用NLMS推导出自适应算法.仿真表明,此新型结构能够有效消除码间和非线性干扰,提高误码性能.     

Complex-bilinear recurrent neural network for equalization of adigital satellite channel

Equalization of satellite communication using complex-bilinear recurrent neural network (C-BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it can be used in modeling highly nonlinear systems with time-series characteristics more effectively than multilayer perceptron type neural networks (MLPNN). The BLRNN is first expanded to its complex value version (C-BLRNN) for dealing with the complex input values in the paper. C-BLRNN is then applied to equalization of a digital satellite communication channel for M-PSK and QAM, which has severe nonlinearity with memory due to traveling wave tube amplifier (TWTA). The proposed C-BLRNN equalizer for a channel model is compared with the currently used Volterra filter equalizer or decision feedback equalizer (DFE), and conventional complex-MLPNN equalizer. The results show that the proposed C-BLRNN equalizer gives very favorable results in both the MSE and BER criteria over Volterra filter equalizer, DFE, and complex-MLPNN equalizer.     

Pipelined functional link artificial recurrent neural network with the decision feedback structure for nonlinear channel equalization

This paper presents a computationally efficient nonlinear adaptive filter by a pipelined functional link artificial decision feedback recurrent neural network (PFLADFRNN) for the design of a nonlinear channel equalizer. It aims to reduce computational burden and improve nonlinear processing capabilities of the functional link artificial recurrent neural network (FLANN). The proposed equalizer consists of several simple small-scale functional link artificial decision feedback recurrent neural network (FLADFRNN) modules with less computational complexity. Since it is a module nesting architecture comprising a number of modules that are interconnected in a chained form, its performance can be further improved. Moreover, the equalizer with a decision feedback recurrent structure overcomes the unstableness thanks to its nature of infinite impulse response structure. Finally, the performance of the PFLADFRNN modules is evaluated by a modified real-time recurrent learning algorithm via extensive simulations for different linear and nonlinear channel models in digital communication systems. The comparisons of multilayer perceptron, FLANN and reduced decision feedback FLANN equalizers have clearly indicated the convergence rate, bit error rate, steady-state error and computational complexity, respectively, for nonlinear channel equalization.     

Nonlinear channel equalization for QAM signal constellation usingartificial neural networks

Application of artificial neural networks (ANN's) to adaptive channel equalization in a digital communication system with 4-QAM signal constellation is reported in this paper. A novel computationally efficient single layer functional link ANN (FLANN) is proposed for this purpose. This network has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomials. Because of input pattern enhancement, the FLANN is capable of forming arbitrarily nonlinear decision boundaries and can perform complex pattern classification tasks. Considering channel equalization as a nonlinear classification problem, the FLANN has been utilized for nonlinear channel equalization. The performance of the FLANN is compared with two other ANN structures [a multilayer perceptron (MLP) and a polynomial perceptron network (PPN)] along with a conventional linear LMS-based equalizer for different linear and nonlinear channel models. The effect of eigenvalue ratio (EVR) of input correlation matrix on the equalizer performance has been studied. The comparison of computational complexity involved for the three ANN structures is also provided.     

A hierarchical alternative updated adaptive Volterra filter with pipelined architecture

The pipelined adaptive Volterra filters (PAVFs) with a two-layer structure constitute a class of good low-complexity filters. They can efficiently reduce the computational complexity of the conventional adaptive Volterra filter. Their major drawbacks are low convergence rate and high steady-state error caused by the coupling effect between the two layers. In order to remove the coupling effect and improve the performance of PAVFs, we present a novel hierarchical pipelined adaptive Volterra filter (HPAVF)-based alternative update mechanism. The HPAVFs with hierarchical decoupled normalized least mean square (HDNLMS) algorithms are derived to adaptively update weights of its nonlinear and linear subsections. The computational complexity of HPAVF is also analyzed. Simulations of nonlinear system adaptive identification, nonlinear channel equalization, and speech prediction show that the proposed HPAVF with different independent weight vectors in nonlinear subsection has superior performance to conventional Volterra filters, diagonally truncated Volterra filters, and PAVFs in terms of initial convergence, steady-state error, and computational complexity.     

一种组合神经网络非线性判决反馈均衡器

1 引言数字通信系统的典型模型如图1所示,发送序列s(n)经信道传输后因发生失真及噪声v(n)的影响而成为畸变信号x(n),为此需用均衡器对其进行均衡以恢复发送序列。目前,自适应均衡已成为数字通信中一种非常重要的技术,自适应均衡器的构成也是多种多样,其中最简单的是线性横向均衡器(LTE)和判决反馈均衡器(DFE),它们都比较适用于线性信道。如果信道呈现非线性特性,两者的性能特别是LTE的均衡能力会大大下降,而利用径向基函数网络(RBFN)等构     

Equalization of nonlinear time-varying channels using type-2 fuzzyadaptive filters

Presents a kind of adaptive filter: type-2 fuzzy adaptive filter (FAF); one that is realized using an unnormalized type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS). We apply this filter to equalization of a nonlinear time-varying channel and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. A clustering method is used to adaptively design the parameters of the FAF. Two structures are used for the equalizer: transversal equalizer (TE) and decision feedback equalizer (DFE). A decision tree structure is used to implement the decision feedback equalizer, in which each leaf of the tree is a type-2 FAF. This DFE vastly reduces computational complexity as compared to a TE. Simulation results show that equalizers based on type-2 FAFs perform much better than nearest neighbor classifiers (NNC) or equalizers based on type-1 FAFs