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.     

Adaptively Combined FIR and Functional Link Artificial Neural Network Equalizer for Nonlinear Communication Channel

This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.     

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．     

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     

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...     

Neural equalization applied to systems with bidimensional digital modulation

A new equalization model for digital communication systems is proposed, based on a multi-layer perceptron (MLP) artificial neural network with a backpropagation algorithm. Unlike earlier techniques, the proposed model, called the bidimensional neural equalizer, is composed of two independent MLP networks that operate in parallel for each dimension of the digital modulation scheme. A heuristic method to combine the errors of the two MLP networks is also proposed, with the aim of reducing the convergence time. Simulations performed for linear and nonlinear channels demonstrated that the new model could improve performance in terms of the bit error rate and the convergence time, compared to existing models.     

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.     

Robust nonlinear channel equalization using WNN trained by symbiotic organism search algorithm

In the present world of ‘Big Data,’ the communication channels are always remaining busy and overloaded to transfer quintillion bytes of information. To design an effective equalizer to prevent the inter-symbol interference in such scenario is a challenging task. In this paper, we develop equalizers based on a nonlinear neural structure (wavelet neural network (WNN)) and train it's weighted by a recently developed meta-heuristic (symbiotic organisms search algorithm). The performance of the proposed equalizer is compared with WNN trained by cat swarm optimization (CSO) and clonal selection algorithm (CLONAL), particle swarm optimization (PSO) and least mean square algorithm (LMS). The performance is also compared with other equalizers with structure based on functional link artificial neural network (trigonometric FLANN), radial basis function network (RBF) and finite impulse response filter (FIR). The superior performance is demonstrated on equalization of two non-linear three taps channels and a linear twenty-three taps telephonic channel. It is observed that the performance of the gradient algorithm based equalizers fails in the presence of burst error. The robustness in the performance of the proposed equalizers to handle the burst error conditions is also demonstrated.     

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。     

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。     

Communication channel equalization using complex-valued minimalradial basis function neural networks

A complex radial basis function neural network is proposed for equalization of quadrature amplitude modulation (QAM) signals in communication channels. The network utilizes a sequential learning algorithm referred to as complex minimal resource allocation network (CMRAN) and is an extension of the MRAN algorithm originally developed for online learning in real-valued radial basis function (RBF) networks. CMRAN has the ability to grow and prune the (complex) RBF network's hidden neurons to ensure a parsimonious network structure. The performance of the CMRAN equalizer for nonlinear channel equalization problems has been evaluated by comparing it with the functional link artificial neural network (FLANN) equalizer of J.C. Patra et al. (1999) and the Gaussian stochastic gradient (SG) RBF equalizer of I. Cha and S. Kassam (1995). The results clearly show that CMRANs performance is superior in terms of symbol error rates and network complexity.     

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。     

Improved functional link artificial neural network via convex combination for nonlinear active noise control

A method relying on the convex combination of two normalized filtered-s least mean square algorithms (CNFSLMS) is presented for nonlinear active noise control (ANC) systems with a linear secondary path (LSP) and nonlinear secondary path (NSP) in this paper. The proposed CNFSLMS algorithm-based functional link artificial neural network (FLANN) filter, aiming to overcome the compromise between convergence speed and steady state mean square error of the NFSLMS algorithm, offers both fast convergence rate and low steady state error. Furthermore, by replacing the sigmoid function with the modified Versorial function, the modified CNFSLMS (MCNFSLMS) algorithm with low computational complexity is also presented. Experimental results illustrate that the combination scheme can behave as well as the best component and even better. Moreover, the MCNFSLMS algorithm requires less computational complexity than the CNFSLMS while keeping the same filtering performance.     

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。     

基于深度神经网络的高速信道自适应均衡器

高速串行接口是提高高性能互连网络带宽的关键技术,而信道均衡器则是提高信号完整性的核心部件.利用现代数字信号处理(DSP)结构,提出了基于深度神经网络(DNN)的高速信道均衡研究方法,此方法在面向未来50 GB以上的高速信道时,克服了传统判决反馈均衡器(DFE)的判决速度受限于反馈回路的固有缺陷问题.仿真结果表明,在采用...     

Decision feedback recurrent neural equalization with fast convergence rate

Real-time recurrent learning (RTRL), commonly employed for training a fully connected recurrent neural network (RNN), has a drawback of slow convergence rate. In the light of this deficiency, a decision feedback recurrent neural equalizer (DFRNE) using the RTRL requires long training sequences to achieve good performance. In this paper, extended Kalman filter (EKF) algorithms based on the RTRL for the DFRNE are presented in state-space formulation of the system, in particular for complex-valued signal processing. The main features of global EKF and decoupled EKF algorithms are fast convergence and good tracking performance. Through nonlinear channel equalization, performance of the DFRNE with the EKF algorithms is evaluated and compared with that of the DFRNE with the RTRL.     

A channel equalizer using reduced decision feedback Chebyshev functional link artificial neural networks

In this paper, a reduced decision feedback Chebyshev functional link artificial neural network (RDF-CFLANN) is proposed for the design of a nonlinear channel equalizer. An RDF-CFLANN structure uses functional expansion utilities to nonlinearly transform its input signals into the output space. In most MLP structures, one or more hidden layers are needed to nonlinearly map the input signals to the output signal space. Therefore, the complexity of the RDF-CFLANN structure is generally much lower than that of an MLP structure. In addition, the required amount of computing at the training mode can also be reduced. The comparisons of the mean squared error (MSE) and the average transmission bit error rate (BER) among RDF-CFLANN, DF-CFLANN and CFLANN are presented in this paper. Simulation results demonstrate that RDF-CFLANN presents the best performance among the three structures.     

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。     

一种克服脉位脉宽信号码间干扰的均衡算法

使用脉位脉宽调制的室内红外无线通信系统,信号光经过红外信道会产生严重的码间干扰,因此需要在接收端使用均衡器进行处理。常用的迫零反馈均衡算法性能不够理想,针对脉位脉宽调制信号提出了一种MMSE分组判决反馈均衡算法。以最小均方误差原理为基础,同时简化了均衡器中判决器的结构,并使用信号抽取技术设计反馈系数,能够一次反馈多个符号以提高补偿效果。仿真实验表明,与经典的迫零均衡算法相比,算法在误比特率为时抗噪声性能提高了近3dB,并且能够适应更加复杂的信道条件,具有广阔的应用前景。