基于复数径向基函数神经网络的自适应均衡器

本文对基于复数径向基函数网络（ＣＲＢＦＮ）的自适应均衡器作了研究；提出了两种新的复数径向基函数网络结构的均衡器；正交ＣＲＢＦＮ（ＯＣＲＢＦＮ）均衡器与非对称ＣＲＢＦＮ（ＡＣＲＢＦＮ）均衡器；并在此了提出了这两种自适应均衡器的训练算法，模拟结果表明对于线性信道或非线性信道定这两种基于ＣＲＢＦＮ均衡器的收敛性能比文献「２，５」中的元首衡器优越，具有一定的实用价值。     

一种新的神经网络均衡器:结构、算法与性能

本文根据克服数字通信中码间干扰（ISI）的最佳均衡解一般表达式，提出了一种新的自适应神经网络均衡器结构，然后导出了基于该结构的一种自适应算法和相应的学习规则，最后对提出的自适应神经网络均衡器性能进行了计算机模拟，模拟结果与分析表明：本文提出的神经网络均衡器用于实现最佳信道均衡非常有效，比传统线性均衡器和Gibson等人[1]提出的多层感知均衡器（MLPE）性能更优越，更具实用性．     

基于小波神经网络的自适应均衡器的研究

提出了基于小波神经网络的通信信道自适应均衡器 ,给出了这种均衡器的结构和训练算法。理论分析和计算机仿真均表明 ,与线性均衡器相比 ,基于小波神经网络的均衡器具有更快的收敛速度 ,是一种前景广阔的均衡器。     

自适应复信道均衡的一种新的神经网络方法

近年来,神经网络已经广泛地应用到许多信号处理问题中.自适应信道均衡是数字通信系统中的一个重要问题.在本文中我们提出一种基于复数赫布类型算法的自适应信道均衡器.计算机模拟表明,无论在线性还是非线性信道中,所提出的均衡器均表现出良好的性能,这为自适应复信道均衡提供了一种新的方法.     

一种脉冲噪声下的自适应均衡算法

由单层神经自适应滤波器和Chebyshev正交多项式滤波器级联构成一个均衡器,基于此均衡器结构,提出一种基于分数低阶统计量的自适应均衡算法(称为NC-NLMP算法).仿真结果表明,对线性信道和非线性信道,NC-NLMP算法的均衡性能均优于传统的FIR横向滤波器的自适应均衡算法和神经网络均衡算法;对于非线性信道,NC-NLMP算法的均衡性能远优于后两者;NC-NLMP算法对高斯噪声和稳定分布脉冲噪声都具有很好的均衡性能,对背景噪声韧性强.     

激光通信系统的波特间隔均衡器设计

当前应用的均衡器，主要采用单一的盲自适应算法实现信道均衡处理，容易出现误差扩散的现象，导致均衡器误码率较高。因此，针对激光通信系统，设计一种新的波特间隔均衡器。根据激光通信系统的接收端接收信号模型、干扰模型，定义激光通信系统信道模型。基于信道均衡要求，设计由前滤波器、反馈滤波器两个主要部分组成的多阶滤波器，作为波特间隔均衡器的基础结构。通过设置MSE切换门限联动LMS算法和CMA算法，形成自适应混合均衡算法，确保均衡器具有优越的信道均衡处理性能。最后，在FPGA上实现波特间隔均衡器。实验结果显示：应用所设计均衡器后，激光通信系统的最小误码率降低至10^-4,达到了提升激光通信质量的目的。     

MIMO通信系统中一种新的自适应均衡方法

该文提出了时变频率选择性衰落环境下多输入多输出(MIMO)通信系统中一种新的基于递归最小二乘(RLS)的自适应干扰对消的判决反馈均衡方法(RLS-IC-DFE)。该方法将每个数据子流上的自适应判决反馈均衡器等效为一个加长的新的均衡器,并把已解出的子流数据作为新的输入参数,应用RLS算法自适应调整新的均衡器参数。仿真结果表明,该文提出的自适应干扰对消的判决反馈均衡器可以自适应跟踪信道变化,同时比已有的判决反馈均衡器(RLS-DFE)能提供更好的误符号率(SER)性能。     

基于小波包变换和判决反馈RBF网络的组合均衡器

本文提出了一种基于小波包变换和判决反馈RBF网络的组合非线性均衡器的结构和算法.首先将信号进行小波包分解,再将分解后的信号分量送入带有判决反馈结构的RBF神经网络进行均衡.一方面,小波包具有很强的去相关能力,可以提高均衡器的收敛速度;另一方面,RBF神经网络具有较强的非线性模式分类能力,可降低均衡器的均方误差.在仿真实验中,针对无线通信数字信号传输过程中由于多径效应和信道衰落而产生的码间干扰(ISI)问题,比较了最小均方(LMS)算法和组合均衡器算法的均衡效果,结果表明,组合均衡算法具有更快的收敛速度,更低的误码率.     

径向基函数网络的ABS投影学习算法

Ｂｒｏｏｍｈｅａｄ（１９８８），Ｃｈｅｎ（１９９１）等人提出的ＲＢＦ网络的学习算法都是基于传统的ＬＭＳ算法，因此具有一定的局限性。本文提出了一种新的ＲＢＦ网络的学习算法－ＡＢＳ投影学习算法，它是一种直接的学习算法。计算机模拟的结果表明，它具有学习效率高，识别率高和适用范围广的优点。     

结构优化的RBF神经网络学习算法

文章提出了一种自动“删减”隐层神经元的ＲＢＦ神经网络学习算法。模拟结果表明，该算法训练的ＲＢＦ网络不仅结构得以优化，同时性能良好，可能成功地应用于模式分类和时间序列预测问题中。     

Length- and cost-dependent local minima of unconstrained blindchannel equalizers

Baud-rate linear blind equalizers may converge to undesirable stable equilibria due to different mechanisms. One such mechanism is the use of linear FIR filters as equalizers. It is shown that this type of local minima exist for all unconstrained blind equalizers whose cost functions satisfy two general conditions. The local minima generated by this mechanism are thus called length-dependent local minima. Another mechanism is generated by the cost function adopted by the blind algorithm itself. This type of local minima are called cost-dependent local minima. It is shown that several well-designed algorithms do not have cost-dependent local minimum, whereas other algorithms, such as the decision-directed equalizer and the stop-and-go algorithm (SGA), do. Unlike many existing convergence analysis, the convergence of the Godard (1980) algorithms (GAs) and standard cumulant algorithms (SCAs) under Gaussian noise is also presented. Computer simulations are used to verify the analytical results     

Convergence analysis of finite length blind adaptive equalizers

The paper presents some new analytical results on the convergence of two finite length blind adaptive channel equalizers, namely, the Godard equalizer and the Shalvi-Weinstein equalizer. First, a one-to-one correspondence in local minima is shown to exist between the Godard and Shalvi-Weinstein equalizers, hence establishing the equivalent relationship between the two algorithms. Convergence behaviors of finite length Godard and Shalvi-Weinstein equalizers are analyzed, and the potential stable equilibrium points are identified. The existence of undesirable stable equilibria for the finite length Shalvi-Weinstein equalizer is demonstrated through a simple example. It is proven that the points of convergence for both finite length equalizers depend on an initial kurtosis condition. It is also proven that when the length of equalizer is long enough and the initial equalizer setting satisfies the kurtosis condition, the equalizer will converge to a stable equilibrium near a desired global minimum. When the kurtosis condition is not satisfied, generally the equalizer will take longer to converge to a desired equilibrium given sufficiently many parameters and adequate initialization. The convergence analysis of the equalizers in PAM communication systems can be easily extended to the equalizers in QAM communication systems     

最低误码率非线性均衡器的快速自适应学习算法

针对最低误码率非线性均衡器的参数在线自适应学习问题,本文提出基于拟牛顿方法的快速自适应学习算法。采用Parzen窗函数方法估计误码率,通过设定切换条件,使参数学习在滑窗随机梯度法与滑窗拟牛顿法之间切换。这既增加了新算法的数值稳定性,又可提高收敛速度。通过对拟牛顿方法进行修改,还使新算法既可以在线自适应学习,也可用于高维参数的快速学习。仿真采用最低误码率非线性均衡器对通信系统进行干扰抑制和信道均衡,结果表明了新算法的高效性。     

A dynamic regularized radial basis function network for nonlinear,nonstationary time series prediction

In this paper, constructive approximation theorems are given which show that under certain conditions, the standard Nadaraya-Watson (1964) regression estimate (NWRE) can be considered a specially regularized form of radial basis function networks (RBFNs). From this and another related result, we deduce that regularized RBFNs are m.s., consistent, like the NWRE for the one-step-ahead prediction of Markovian nonstationary, nonlinear autoregressive time series generated by an i.i.d. noise processes. Additionally, choosing the regularization parameter to be asymptotically optimal gives regularized RBFNs the advantage of asymptotically realizing minimum m.s. prediction error. Two update algorithms (one with augmented networks/infinite memory and the other with fixed-size networks/finite memory) are then proposed to deal with nonstationarity induced by time-varying regression functions. For the latter algorithm, tests on several phonetically balanced male and female speech samples show an average 2.2-dB improvement in the predicted signal/noise (error) ratio over corresponding adaptive linear predictors using the exponentially-weighted RLS algorithm. Further RLS filtering of the predictions from an ensemble of three such RBFNs combined with the usual autoregressive inputs increases the improvement to 4.2 dB, on average, over the linear predictors     

Eigenanalysis-based blind methods for identification, equalization,and inversion of linear time-invariant channels

Some blind algorithms are presented for estimation of equalizers for linear time invariant (LTI) channels from the eigenvectors of certain rank-one matrices constructed from the second-order statistics of the oversampled received signal. It is shown that the channel can also be identified from the same matrices. It can be shown that in multipath dominated environments, equalizers with symbol spread of only one (referred to as inverters) can be used when sufficient diversity is available. Because of the manner in which structure in the channel distortion is exploited, the proposed identification and equalization algorithms are also applicable to this case. For the same reason, the proposed algorithms do not require estimation of the channel memory (only an upper bound is required). Equalizers of desired delay are estimated directly independent of others     

Equalizers for transmultiplexers in orthogonal multiple carrier data transmission

Orthogonal multiple carrier data transmission systems are efficiently realized using modified dft transmultiplexer filter banks. In data transmission applications, a non-ideal transmission channel causes distortions such as intersymbol interference and crosstalk between the subrate bands of the transmultiplexer. Hence, in order to equalize these distortions, subband equalizers, which affect the intersymbol interference and crosstalk behavior, are considered for implementation. The special structure of modified dft transmultiplexers requires a discussion concerning the various possibilities of placing the subband equalizers at the receiver. Wiener solutions and lms adaptive algorithms for various new subband equalizer structures are derived and compared by means of simulation results.     

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.     

Performance bounds for multistep prediction-based blindequalization

Blind equalization attempts to remove the interference caused by a communication channel without using any known training sequences. Blind equalizers may be implemented with linear prediction-error filters (PEFs). For many practical channel types, a suitable delay at the output of the equalizer allows for achieving a small estimation error. The delay cannot be controlled with one-step predictors. Consequently, multistep PEF-based algorithms have been suggested as a solution to the problem. The derivation of the existing algorithms is based on the assumption of a noiseless channel, which results in zero-forcing equalization. We consider the effects of additive noise at the output of the multistep PEF. Analytical error bounds for two PEF-based blind equalizers in the presence of noise are derived. The obtained results are verified with simulations. The effect of energy concentration in the channel impulse response on the error bound is also addressed     

Direct blind equalizers of multiple FIR channels: a deterministicapproach

Blind equalization of single-input multi-output channels has practical value for inverse problems encountered in communications, sonar, and seismic data processing. Relying on diversity (sufficient number of multiple outputs), we bypass the channel estimation step and derive direct blind FIR equalizers of co-prime FIR channels. There are no constraints on the inaccessible input, apart from a minimum persistence of excitation condition; the input can be deterministic or random with unknown color or distribution. At moderate SNR (>20 dB), the resulting algorithms remain operational even with very short data records (<100 samples), which makes them valuable for equalization of rapidly fading multipath channels. Complexity, persistence of excitation order, and mean-square error performance tradeoffs are delineated for equalizers of single-shift (semi-blind), pair, or, multiple shifts estimated separately or simultaneously. Optimum and suboptimum combinations of the equalizers' outputs are also studied. Simulations illustrate the proposed algorithms and compare them with dual deterministic channel identification algorithms     

Deterministic approaches for blind equalization of time-varyingchannels with antenna arrays

In this paper, we study the blind equalization problem of time-varying (TV) systems where the channel variations are too rapid to be tracked with conventional adaptive equalizers. We show that using a finite Fourier basis expansion, a TV antenna array system can be cast into a time-invariant multi-input, multi-output (MIMO) framework. The multiple inputs are related through the bases, thereby allowing blind equalization to be accomplished without the use of higher order statistics. Two deterministic blind equalization approaches are presented: one determines the channels first and then the equalizers, whereas the other estimates the equalizers directly. Related issues such as order determination are addressed briefly. The proposed algorithms are illustrated using simulations