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。     

比特交织编码调制系统中的均衡技术仿真研究

判决反馈迭代译码的比特交织编码调制(BICM-ID)技术是一种高性能、低复杂度的先进信道调制方案.分析了BICM-ID系统的结构和迭代译码方法,研究了最小均方(LMS)和最小二乘(RLS)自适应均衡算法以及线性(LE)和判决反馈(DFE)均衡器.设计了在ISI信道下BICM-ID使用DFE-RLS均衡器的系统,计算机仿真证明其能够自适应地快速估计、跟踪信道,对于码间干扰严重的信道有良好的均衡效果,经过迭代译码有良好的误码性能-分别在ISI干扰轻微和严重的W2.9和W3.5信道下,经过3次迭代译码,在信噪比分别为7.6 dB和12.8 dB时误码率可达10-4.     

基于T/4分数间隔的判决反馈盲均衡算法研究

在均衡具有深谱零点的稀疏水声信道时,针对波特间隔判决反馈盲均衡器权系数长、收敛速度慢、均方误差大的缺点,提出了基于T/4分数间隔的判决反馈盲均衡算法.该算法前馈滤波器采用4路子信道系统模型,用常数模误差函数对均衡器权系数进行调整,综合了分数间隔均衡器和判决反馈均衡器的优点,均衡器权长只要大于或等于信道长度就能完全均衡信道,该算法收敛速度比波特间隔判决反馈盲均衡算法(BSDFE)与T/4分数间隔盲均衡算法(T/4FSE)快、均方误差比BSDFE与T/4FSE小,且计算量不变,有利于信息的实时恢复.深谱零点稀疏水声信道盲均衡的仿真结果,进一步验证了该算法的性能.     

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．     

ATSC接收机中频域均衡算法的研究

本文主要研究一种结构简单性能可靠的频域均衡方法。通过对频域自适应滤波器算法以及该算法初始化问题的研究,给出了一种可以有效应用与ATSC标准接收机的频域均衡器结构,并且仿真分析了影响该均衡器性能的几个关键因素以及其与传统的判决反馈均衡器的性能差别,从仿真结果来看,自适应的频域均衡器可以在得到与判决反馈均衡器近似的性能,在计算量上具有很大优势,为将来的多标准数字电视接收机提供可行性参考。     

线性均衡和判决反馈均衡LMS算法仿真分析

介绍基于自适应最小均方线性均衡和判决反馈均衡算法的原理,并通过实验仿真比较两种算法在训练判决引导混合模式下的均衡性能,分析反馈滤波器长度对判决反馈均衡器性能的影响。结果表明:在训练阶段,最小均方线性均衡算法优于最小均方判决反馈均衡算法的性能;在判决阶段,良好信道条件下最小均方线性均衡具有比较理想的性能,当信道条件恶劣时,最小均方线性均衡算法性能变差,而最小均方判决反馈均衡算法随着反馈滤波器长度增加,均衡效果更优。     

一种高速接收机判决反馈均衡的验证方法

随着芯片速度的不断提高，接收机性能验证变得尤为重要，本文主要根据连续时间线性均衡器中交流增益和 直流增益的组合，结合误码测试分析结果，提出了一种误码分布式表验证判决反馈均衡性能的方法，可以直观地检查高速接收 机判决反馈均衡器对信号的补偿效果，为芯片性能提供依据。     

基于UKF的自适应DFE信道均衡

针对信道的线性和非线性失真,在分析简化的非线性滤波的基础上,利用判决反馈的结构特点对其进行扩展,提出了基于UKF滤波的判决反馈均衡器,仿真表明,UKF滤波算法能降低系统均方误差性能。     

FTN系统中两种频域迭代分组判决反馈均衡器仿真分析

超奈奎斯特(FTN)码元速率传输系统可以有效提高数据传输速率,但该系统在接收端引入了无限长的码间串扰(ISI),从而增加了接收复杂度。为此,分析两种能降低FTN系统计算复杂度的频域均衡器,即频域迭代分组判决反馈均衡器(IBDFE)和低复杂度迭代分组判决反馈均衡器(LC-IBDFE)。将IBDFE和LC-IBDFE分别扩展到加性高斯白噪声(AWGN)信道和频率选择性衰落信道中。仿真结果表明,这两种频域均衡器可以应用到衰落信道中,且在多径数目不大的情况下,两者的误码率和AWGN信道条件下十分接近。     

光记录通道中记录密度与均衡器的关系

文中建立了便于理论分析的光记录读出信号模型和离散通道模型。对均衡器对记录密度的影响进行了分析，证明了判决反馈均衡器比线笥均衡器提高记录密度约５０％。     

基于UW字的单载波判决反馈均衡器水声通信研究

将单载波块传输系统运用于水声通信中,为了克服信道中存在的深衰落,对基于一种UW字块结构的时频域混合判决反馈均衡和块迭代频域判决反馈均衡两种判决反馈均衡的方法进行了比较和分析。仿真结果及水池实验表明:基于该UW字结构的块迭代频域判决反馈均衡在水声通信中有良好的应用前景。     

奇对称误差函数变动量因子盲均衡算法

在分析奇对称误差函数判决反馈盲均衡算法（OFA-DFE,Odd symmetry error Function blind equalization Algorithm based Decision Feedback Equalizer）基础上,提出了基于奇对称误差函数变动量因子判决反馈动量盲均衡算法（VMFMOFA-DFE,Variable Momentum Factor Momentum OFA-DFE）。该算法采用具有奇对称性的误差函数来减少均衡器的均方误差,利用变因子的思想对动量项进行控制,并把变动量因子引入到判决反馈算法中,对判决反馈的前向权进行调整,以进一步提高算法的性能。水声信道的仿真结果表明,该算法具有较快的收敛速度和较小的均方误差。     

一种单载波宽带信号非线性均衡技术

针对单载波宽带信号均衡难以收敛的问题,研究了一种基于子带分解与重构的宽带非线性均衡技术。综合利用复数子带滤波器组与判决反馈均衡器理论,给出了两种具有模式切换功能的宽带非线性均衡结构,基于最小均方算法推导了它们的均衡权值迭代更新公式,分析比较了两种结构在不同均衡模式下的收敛特点。仿真证明基于子带技术的宽带非线性均衡能适用于群时延较严重的信道,且比传统全频带均衡具有更好的收敛效果和更低的计算复杂度。     

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     

Neural FIR adaptive Laguerre equalizer with a gradient adaptive amplitude for nonlinear channel in communication systems

To mitigate the linear and nonlinear distortions in communication systems, two novel nonlinear adaptive equalizers are proposed on the basis of the neural finite impulse response (FIR) filter, decision feedback architecture and the characteristic of the Laguerre filter. They are neural FIR adaptive decision feedback equalizer (SNNDFE) and neural FIR adaptive Laguerre equalizer (LSNN). Of these two equalizers, the latter is simple and with characteristics of both infinite impulse response (IIR) and FIR filte...     

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.     

An adaptive decision feedback equalizer based on the combination of the FIR and FLNN

To compensate the linear and nonlinear distortions and to track the characteristic of the time-varying channel in digital communication systems, a novel adaptive decision feedback equalizer (DFE) with the combination of finite impulse response (FIR) filter and functional link neural network (CFFLNNDFE) is introduced in this paper. This convex nonlinear combination results in improving the convergence speed while retaining the lower steady-state error at the cost of a small increasing computational burden. To further improve the performance of the nonlinear equalizer, we derive here a novel simplified modified normalized least mean square (SMNLMS) algorithm. Moreover, the convergence properties of the proposed algorithm are analyzed. Finally, computer simulation results which support analysis are provided to evaluate the performance of the proposed equalizer over the functional link neural network (FLNN), radial basis function (RBF) neural network and linear equalizer with decision feedback (LMSDFE) for time-invariant and time-variant nonlinear channel models in digital communication systems.