SIMO SC-FDE系统的CFR盲估计

提出了一种基于线性预测的单输入多输出单载波频域均衡系统频域信道响应(也称为信道频率响应,CFR)盲估计算法.与传统的线性预测时域信道估计方法不同,提出的算法不需要计算新息以及新息和输出序列的互相关,而是直接从预测滤波器系数获得频域信道响应估计的闭式解.算法仅采用输出序列的二阶统计量,对信道阶次过估计具有鲁棒性,并且估计性能优于传统的线性预测时域信道估计方法.计算机仿真结果验证了理论分析的正确性.     

适用于含公共零点SIMO信道的盲均衡算法

在含公共零点单输入多输出(SIMO)模型的基础上,提出一种针对含公共零点的SIMO信道的直接无限冲击响应(IIR)盲均衡算法。该算法利用IIR预测均衡算法对输入信号进行初始均衡和对均衡结果进行相偏纠正,通过最小均方误差准则提高算法在高斯白噪声环境中的适应性,克服IIR预测算法中的相位偏转问题与IIR预测算法对信噪比敏感的缺点。仿真实验结果表明,该算法对IIR信道及含公共零点信道都具有较好的均衡效果。     

Bi-iterative least squares algorithms for blind channel identification and equalization with second-order statistics

We present an adaptive algorithm for blind identification and equalization of single-input multiple-output (SIMO) FIR channels with second-order statistics. We first reformulate the blind channel identification problem into a low-rank matrix approximation solution based on the QR decomposition of the received data matrix. Then, a fast recursive algorithm is developed based on the bi-iterative least squares (Bi-LS) subspace tracking method. The new algorithm requires only a computational complexity of O(md2)...     

基于改进盲均衡算法的水声通信可靠性研究

关于水声信道通信优化问题,水声通信中复杂信道存在码间干扰极易影响通信质量,需要进行均衡提高通信数据的可靠性,传统的自适应均衡方法是通过发送训练序列对信道特性捕获并更新参数完成均衡,然而对于快速时变的信道,不能快速得到信道特性无法及时对通信均衡,造成均衡性能不强、水声通信数据可靠性不高。为此,提出改进的盲均衡算法,盲均衡算法不需发送训练序列,只需知道发送序列的统计特性就可对发送信息恢复,可保证均衡的及时性,在盲均衡的基础上采用小生境技术对其改进,优化盲均衡搜索到的最优解,从而提高均衡的性能。实验表明,小生境技术的改进盲均衡算法能够快速捕获信息特性并及时准确地对信号进行均衡,具有较高的均衡性能,保证了通信数据的可靠性,为水声信道优化提供了参考。     

Two efficient adaptively varying modulus blind equalizers: AVMA and DM/AVMA

Among the blind channel equalization schemes, constant modulus algorithm (CMA), due to its robustness and easy implementation, is an excellent choice to correct the distortions caused by transmission channels. In two-dimensional communication systems, phase recovery is a subject of grave concern which cannot be handled by conventional CMA due to its phase blind nature. In this paper, an adaptively varying modulus algorithm (AVMA) is presented that accomplishes blind equalization and phase correction simultaneously in case of light distorted channels. In order to equalize the channel with higher distortion levels, DM/AVMA, a hybrid of MCMA and AVMA is also presented. Both of these equalizers outperform the conventional CMA and some other existing schemes under certain environments. Analysis and simulation results indicate that the AVMA blind equalizer has faster convergence rate and better steady state performance than those of the conventional CMA & MCMA equalizers in light distorted channels, while, the DM/AVMA equalizer outperforms all the above-mentioned equalizers significantly in channels with higher distortion levels.     

支持向量机初始化的常数模盲均衡算法仿真

为了提高常数模盲均衡算法的收敛速度并避免算法收敛至局部极小,提出了一种支持向量机初始化的常数模盲均衡算法.新算法采用一小段初始数据,利用支持向量机,将盲均衡问题转化为全局最优的支持向量回归问题,对盲均衡器的初始权向量进行设定,而后切换至计算量较小的常数模算法.采用浅海水声信道对新算法进行了计算机仿真,结果表明:支持向量机初始化阶段收敛速度快;切换至常数模算法后性能稳定.该算法适合应用于快衰落水声信道中通信数据的实时恢复.     

CMA adaptive equalization in subspace pre-whitened blind receivers

In blind channel equalization, the use of whitening transformation (WT) preceding the constant modulus algorithm (CMA), referred to as the pre-whitened CMA, may accelerate the rate of convergence of using the CMA alone. In this work, we develop a novel subspace pre-whitened CMA (SP-CMA) using the subspace WT. By allowing the channel matrix to be non-square, this approach for the subspace WT offers greater flexibility in the design of the WT than the conventional per-whitened CMA does. The similarities and differences between the SP-CMA and the conventional CMA were first examined and analyzed in terms of their convergence behaviors from a theoretical viewpoint. The convergence behavior of the SP-CMA was analyzed under the whiteness assumption in which the input of the blind equalizer has been whitened beforehand by the subspace WT. The upper bound of the improvement in convergence of the SP-CMA over its CMA counterpart was developed. This analysis can be readily extended to other existing pre-whitened CMA schemes. Computer simulations using two highly dispersive multipath channels confirmed the superior convergence of the SP-CMA.     

一种新的CMA神经网络均衡器

在数字通信中,接收信号通常会受到码间干扰的影响。采用盲均衡技术可以消除码间干扰,常模算法（CMA）是应用较广泛的盲均衡算法。因基于常模算法的盲均衡器存在收敛速度慢,剩余误差大的缺点,提出了一种新的基于神经网络的CMA盲均衡器。通过很少的训练序列使网络收敛,再转入盲均衡算法。实验仿真表明,无论是在线性信道还是非线性信道,该均衡器的剩余误差都比普通CMA均衡器较小,收敛速度也较快。     

Hybrid simplex genetic algorithm for blind equalization using RBF networks

The purpose of this paper is to derive a hybrid simplex genetic algorithm for nonlinear channel blind equalization using RBF networks. Most of the algorithms for blind equalization are focused on linear channel models because of their simplicity. However, most practical channels are better approximated by nonlinear models. In order to find an effective method for nonlinear channel blind equalization, here, the equalizer based on RBF networks which is constructed from channel output states instead of the channel parameters is considered. Using the Bayesian likelihood cost function defined as the accumulation of the natural logarithm of the Bayesian decision variable, the problem becomes to maximize the Bayesian likelihood cost function with the dataset which composes the RBF equalizer’s center. For this high dimensional complex optimal problem, the proposed hybrid simplex genetic algorithm solves it by incorporating the simplex operator with GA, and obtains a good convergence characteristic and satisfied equalization result.     

一种新的基于统计测度的变步长CMA盲均衡算法

在常模量算法的基础上，提出了一种适合于常模信号的基于统计测度的变步长盲均衡算法。考虑到信道畸变和噪声的同时作用将使得有些观测信号值受到更大的影响，因此可以认为这些值时均衡器系数的调整从统计测度上讲是不太有利，故采用较小的步长，使得该值在整个均衡器系数的调整中贡献较小。本文对此进行了理论解释。仿真结果表明，该算法具有较快的收敛速度，且收敛后的超量均方误差（EMSE）与CMA算法基本相同。     

Using recurrent neural networks for adaptive communication channelequalization

Nonlinear adaptive filters based on a variety of neural network models have been used successfully for system identification and noise-cancellation in a wide class of applications. An important problem in data communications is that of channel equalization, i.e., the removal of interferences introduced by linear or nonlinear message corrupting mechanisms, so that the originally transmitted symbols can be recovered correctly at the receiver. In this paper we introduce an adaptive recurrent neural network (RNN) based equalizer whose small size and high performance makes it suitable for high-speed channel equalization. We propose RNN based structures for both trained adaptation and blind equalization, and we evaluate their performance via extensive simulations for a variety of signal modulations and communication channel models. It is shown that the RNN equalizers have comparable performance with traditional linear filter based equalizers when the channel interferences are relatively mild, and that they outperform them by several orders of magnitude when either the channel's transfer function has spectral nulls or severe nonlinear distortion is present. In addition, the small-size RNN equalizers, being essentially generalized IIR filters, are shown to outperform multilayer perceptron equalizers of larger computational complexity in linear and nonlinear channel equalization cases.     

A blind equalization algorithm for biological signals transmission

Direct transmission of biological signals such as electrocardiogram (ECG) and electroencephalogram (EEG) through mobile network provides practically unlimited movement of the patients and unlimited coverage area. However, transmission of such signals over a bandlimited channel or through a multipath propagation is subject to inter symbol interference (ISI), whereby adjacent symbols on the output of the channel smear and overlap each other causing degradation of error performance. Mitigation of such kind of distortion can be achieved through equalization filter. Recently an adaptive blind channel equalization using sinusoidally-distributed dithered signed-error constant modulus algorithm (DSE-CMA) has been proposed. In this paper we investigate the performance and the feasibility of this scheme for wireless ECG and EEG transmission. Also, this paper discusses the importance of adaptive blind equalizer for biological signals transmission over existing wireless networks such as Global System for Mobile Communications (GSM) and the Enhanced Data rates for GSM Evolution (EDGE). The geometrical-based hyperbolically distributed scatterers (GBHDS) channel model for macrocell environments was simulated with angular spreads (AS) taken from measurement data. Simulation results show that the low complexity of implementation and the fast convergence rate are the major advantages of deploying this scheme for telemedicine applications. It is also shown that the equalizer output signal is highly correlated with the original transmitted signal in time and joint time-frequency domains.     

基于正交小波变换分数间隔的神经网络盲均衡算法

针对分数间隔盲均衡算法(T/4-FSE-CMA)收敛速度慢、稳态误差大的缺点,提出了一种基于正交小波变换分数间隔的神经网络盲均衡算法(T/4-FSE-WT-FNN).该算法采用四路子信道模型,以神经网络作为均衡器,同时,对均衡器的输入信号做正交小波变换并进行归一化,与基于正交小波变换的前馈神经网络盲均衡算法(WT-FN...     

基于二阶循环统计量的盲均衡算法

提出一种新的基于循环二阶统计量的盲均衡算法.通过对信道输出信号进行过采样,建立单输入多输出信道模型.由于过采样等效信道矩阵具有特殊结构,使得仅仅根据零延迟时的协方差矩阵所包含的信息就能实现信道估计,根据不同延迟下的协方差矩阵也可求得不同时延的均衡器矩阵,然后用最小均方误差MMSE准则来优化均衡器得到最佳延迟协方差矩阵.仿真结果表明了该算法的有效性.     

盲信号处理技术在双通道前臂肌电信号识别中的应用

根据肌电信号产生机理,本文对双通道前臂肌电信号建立单输入多输出FIR系统模型 ,由于模型输入未知且不可测,采用了盲信号处理方法对模型参数进行辨识．通过提取模型 冲激响应作为信号特征,能够对握拳、展拳、前臂内旋和前臂外旋四类前臂动作进行识别． 实验表明,该方法仅需建立较低阶数的模型即可达到较好的分类目的,性能要优于传统的AR 模型方法．     

Blind joint maximum likelihood channel estimation and data detection for SIMO systems

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single- input multiple-output (SIMO) systems.The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop.An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model,and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence.A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.     

基于过采样的IIR信道盲辨识与均衡算法

为了信道传输质量得到保证,提出了一种通信信道的盲辨识与均衡算法。算法将信道建模成IIR信道并在其输出端过采样,分别对信道传输函数的AR参数和MA参数进行辨识。在辨识MA参数时将信道分解成多个子信道,使得未知信道的信息被充分利用,提高了系统辨识的精度。同时该算法不需要信道信息和输入信号的统计特性,因而具有广泛的适用性。仿真试验表明该算法具有良好的性能。     

Distributed and recursive blind channel identification to sensor networks

In this paper, the distributed and recursive blind channel identification algorithms are proposed for single-input multi-output (SIMO) systems of sensor networks (both time-invariant and time-varying networks). At any time, each agent updates its estimate using the local observation and the information derived from its neighboring agents. The algorithms are based on the truncated stochastic approximation and their convergence is proved. A simulation example is presented and the computation results are shown to be consistent with theoretical analysis.     

Blind channel estimation and signal retrieving for MIMO relay systems

In this paper, we propose a blind channel estimation and signal retrieving algorithm for two-hop multiple-input multiple-output (MIMO) relay systems. This new algorithm integrates two blind source separation (BSS) methods to estimate the individual channel state information (CSI) of the source-relay and relay-destination links. In particular, a first-order Z-domain precoding technique is developed for the blind estimation of the relay-destination channel matrix, where the signals received at the relay node are pre-processed by a set of precoders before being transmitted to the destination node. With the estimated signals at the relay node, we propose an algorithm based on the constant modulus and signal mutual information properties to estimate the source-relay channel matrix. Compared with training-based MIMO relay channel estimation approaches, the proposed algorithm has a better bandwidth efficiency as no bandwidth is wasted for sending the training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm.     

一种基于统计特性变换的改进盲信道估计

首先从接收信号的过采样出发,总结了SIMO信道的堆栈系统模型.针对输入信号为平稳且有已知自相关函数时的信道估计与均衡问题,通过统计特性变换的方法导出了变换阵的闭式解,最后给出一种适用于平稳信源的改进盲信道估计与均衡算法,并对算法进行仿真,分析其辨识误差、收敛性能和均衡效果.结果表明算法在输入信号为平稳非独立同分布时仍能对信道进行辨识且具有较高的精度,算法的收敛速度快并能达到理想的均衡效果.