PSK信号均衡器抽头数量选取方法

选取无线信道数字均衡器抽头数量的方法,是均衡器设计的基本环节。介绍所采用的无线传输信道模型和均衡器的结构与抽头系数计算原理,提出了相移键控信号接收端均衡器抽头数量的2种选取方法：最大残余符号间干扰和信号判决差错率算法。给出了选取过程的流程图,并对信道举例进行计算,结果说明了这2种计算方法的有效性。     

基于信道缩短的多频带超宽带信道估计

针对多频带超宽带系统,提出了一种基于信道缩短的信道估计方法.利用循环前缀(CP)结构,在接收机前端设计信道缩短均衡器,解决了循环前缀长度小于信道最大多径延迟时难于估计信道参数的问题.根据均衡器输出序列估计出复合信道,通过反卷积解出原信道参数.计算仿真表明该算法具有良好性能.     

MB—OFDM UWB系统中基于信道缩短的信道估计

在MB-OFDM超宽带(UWB)系统中,针对循环前缀(CP)长度小于信道最大多径延迟时难于估计信道参数的问题,提出一种基于信道缩短的信道估计方法。首先采用发送信号插入梳状导频的方式,利用无约束最优准则,在接收机前端设计信道缩短均衡器,然后根据均衡器输出序列估计出复合信道,最后通过反卷积解出原信道参数。仿真结果表明,在信道最大多径延迟大于CP长度时,该算法具有更优的性能。     

基于零点消除的无线OFDM系统 信道缩短均衡器

针对无线信道的有限冲激响应及其全零点特性,本文设计了一种基于零点消除的无线正交频分复用(OFDM)系统信道缩短均衡器,该均衡器利用零点消除的方式有效地缩短信道时延扩展长度,并采用反馈滤波器的形式来实现无限冲激响应滤波器,该方案不仅降低了信道缩短均衡器算法的复杂度,而且也大大简化了无线OFDM系统的设计与实现.仿真结果验证了上述方案和算法的有效性.     

基于导频序列信道缩短的超宽带信道估计

针对多频带超宽带系统,提出一种基于导频序列信道缩短的信道估计方法,解决了循环前缀长度小于信道最大多径延迟时难于估计信道参数的问题。首先在发送信号中插入块状导频,利用最小均方误差准则（MMSE）,在接收机前端设计信道缩短均衡器,然后根据均衡器输出序列估计出复合信道,最后通过反卷积解出原信道参数。仿真实验表明：该算法具有良好性能。     

稀疏信道下基于稀疏贝叶斯学习的精简星座盲均衡算法

针对稀疏信道的盲均衡问题,在精简星座均衡算法框架下建立线性模型,利用稀疏信道下均衡器固有的稀疏特性,引入具有稀疏促进作用的先验分布对均衡器系数加以约束,使用稀疏贝叶斯学习方法迭代求解均衡器系数得到最大后验估计值。该文提出的均衡方法属于数据复用类均衡算法的范畴,能够适用于数据较短的应用场合。与随机梯度方法相比,算法性能受均衡器长度影响较小,收敛后误符号率性能更好,仿真实验验证了算法的有效性。     

基于JUMMSE准则的前向分数间隔判决反馈均衡器

研究了前向分数间隔判决反馈均衡器的原理与结构,并推导了基于联合无偏差MMSE(JUMMSE)准则设计的最优解。文中讨论了均衡器长度对误符号率(SER)性能的影响。仿真结果表明,在较高的信噪比、选择恰当的均衡器长度时,这种均衡器对于高频信道的均衡效果是显著的。     

基于Simulink的NLMS弥散信道均衡滤波器的参数优选方法

为了克服弥散信道的影响,改善通信质量,提高传输数据速率,近年来出现了多种解决方案,其中最有效的方法就是采用NLMS算法的自适应信道均衡技术.设计NLMS算法的自适应信道均衡器的关键就是寻找一组最佳的自适应滤波器的长度L和步长因子,使系统误码率最低.基于此,本文提出了一种能有效搜索均衡滤波器参数的方法,并构建了该方法的Simulink仿真平台.通过仿真,搜索寻找到了系统误码性能最佳条件下自适应滤波器的长度和步长,同时分析了自适应均衡器的性能,仿真结果证明了其有效性.该平台为最优自适应弥散信道均衡滤波器的设计提供了一个很好的平台,具有很高的实用价值.     

IR-UWB无线通信中MF-DFE-RLS的性能分析

文章通过研究脉冲超宽带(IR-UWB)接收端的均衡处理过程,提出了一种新的MF-DFE-RLS均衡方案,仿真结果表明MF-DFE-RLS均衡器具有误码率低、快速收敛的优点,在不同的信道下,通过调节均衡器长度能获得良好的性能。     

时域均衡在OFDM水声通信系统中的应用

为了对抗水声信道时延扩展大于循环前缀长度时引起的码间串扰问题,在基本的OFDM水声通信系统接收端进行DFT变换前加入相对较短的时域均衡器来限制信道冲激响应的长度。分析了基于MMSE准则的时域均衡算法的实现过程以及算法的复杂程度,对该算法在水声环境下的性能进行了仿真分析,并比较了影响系统性能的因素。结果表明通过加入时域均衡器能够有效对抗信道严重的时间离散性,改善OFDM水声通信系统在循环前缀不足时的系统性能。     

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     

A novel expression for the achievable MSE performance obtained by blind adaptive equalizers

In this paper, I propose for the noisy, real, and two independent quadrature carrier case, an approximated closed-form expression for the achievable minimum mean square error (MSE) performance obtained by blind equalizers where the error that is fed into the adaptive mechanism which updates the equalizer’s taps can be expressed as a polynomial function of the equalized output of order three like in Godard’s algorithm. The proposed closed-form expression for the achievable MSE is based on the step-size parameter, on the equalizer’s tap length, on the channel power, on the signal to noise ratio (SNR), on the nature of the chosen equalizer, and on the input signal statistics. Since the channel power is measurable or can be calculated if the channel coefficients are given, there is no need anymore to carry out any simulation with various step-size parameters, different values for the signal to noise ratio (SNR) and equalizer’s tap length for a given equalization method, and input signal statistics in order to find the MSE performance in the convergence state.     

What are the analytical conditions for which a blind equalizer will loose the convergence state?

In this paper, I propose for the noiseless, real and two independent quadrature carrier case some approximated conditions on the step-size parameter, on the equalizer’s tap length and on the channel power, related to the nature of the chosen equalizer and input signal statistics, for which a blind equalizer will not converge anymore. These conditions are valid for type of blind equalizers where the error that is fed into the adaptive mechanism that updates the equalizer’s taps can be expressed as a polynomial function of the equalized output of order three like in Godard’s algorithm. Since the channel power is measurable or can be calculated if the channel coefficients are given, there is no need anymore to carry out any simulation with various step-size parameters and equalizer’s tap length for a given equalization method and input signal statistics in order to find the maximum step-size parameter for which the equalizer still converges.     

一种水声通信中的多阵元Turbo均衡算法

Turbo均衡应用在水声通信中的问题主要在于水声信道时间扩展长,多接收阵元处理复杂度较高。该文研究了将时间反转与马尔可夫链蒙特卡罗(MCMC)均衡联合优化算法用于实现Turbo均衡。首先进行时间反转实现多接收阵元较长多径时延的压缩,再利用白化滤波器解决时间反转造成的噪声模型失配问题,最后利用复杂度较低的MCMC均衡器结合软迭代信道估计对时间反转合并后得到的信号进行均衡。结合真实实验信道条件对信道响应估计的误差建立模型,通过仿真比较得出, 该算法在相同条件下相对于多阵元直接自适应Turbo均衡算法复杂度降低67%,且有1.6 dB的误码率性能增益。通过对湖上试验数据进行处理,进一步验证了该算法的优势。     

Robust MSE equalizer design for MIMO communication systems in the presence of model uncertainties

This paper considers a robust mean-square-error (MSE) equalizer design problem for multiple-input multiple-output (MIMO) communication systems with imperfect channel and noise information at the receiver. When the channel state information (CSI) and the noise covariance are known exactly at the receiver, a minimum-mean-square-error (MMSE) equalizer can be employed to estimate the transmitted signal. However, in actual systems, it is necessary to take into account channel and noise estimation errors. We consider here a worst-case equalizer design problem where the goal is to find the equalizer minimizing the equalization MSE for the least favorable channel model within a neighborhood of the estimated model. The neighborhood is formed by placing a bound on the Kullback-Leibler (KL) divergence between the actual and estimated channel models. Lagrangian optimization is used to convert this min-max problem into a convex min-min problem over a convex domain, which is solved by interchanging the minimization order. The robust MSE equalizer and associated least favorable channel model can then be obtained by solving numerically a scalar convex minimization problem. Simulation results are presented to demonstrate the MSE and bit error rate (BER) performance of robust equalizers when applied to the least favorable channel model.     

Reconstruction of chaotic signals with application to channel equalization in chaos‐based communication systems

A number of schemes have been proposed for communication using chaos over the past years. Regardless of the exact modulation method used, the transmitted signal must go through a physical channel which undesirably introduces distortion to the signal and adds noise to it. The problem is particularly serious when coherent‐based demodulation is used because the necessary process of chaos synchronization is difficult to implement in practice. This paper addresses the channel distortion problem and proposes a technique for channel equalization in chaos‐based communication systems. The proposed equalization is realized by a modified recurrent neural network (RNN) incorporating a specific training (equalizing) algorithm. Computer simulations are used to demonstrate the performance of the proposed equalizer in chaos‐based communication systems. The Hénon map and Chua's circuit are used to generate chaotic signals. It is shown that the proposed RNN‐based equalizer outperforms conventional equalizers as well as those based on feedforward neural networks for noisy, distorted linear and non‐linear channels. Copyright © 2004 John Wiley & Sons, Ltd.     

Suppression of near- and far-end crosstalk by linear pre- andpost-filtering

Full-duplex data communication over a multi-input/multi-output linear time-invariant channel is considered. The minimum mean square error (MMSE) linear equalizer is derived in the presence of both near- and far-end crosstalk and independent additive noise. The MMSE equalizer is completely specified in terms of the channel and crosstalk transfer functions by using a generalization of previous work due to Salz (1985). Conditions are given under which the equalizer can completely eliminate both near- and far-end crosstalk and intersymbol interference. The MMSE transmitter filter, subject to a transmitted power constraint, is specified when the channel and crosstalk transfer functions are bandlimited to the Nyquist frequency. Also considered is the design of MMSE transmitter and receiver filters when the data signals are arbitrary wide-sense stationary continuous or discrete-time signals, corresponding to the situation where the crosstalk is not phase-synchronous with the desired signal     

Adaptive equalization for frequency-selective channels of unknown length

This paper studies adaptive equalization for time-dispersive communication channels whose impulse responses have unknown lengths. This problem is important, because an adaptive equalizer designed for an incorrect channel length is suboptimal; it often estimates an unnecessarily large number of parameters. Some solutions to this problem exist (e.g., attempting to estimate the "channel length" and then switching between different equalizers); however, these are suboptimal owing to the difficulty of correctly identifying the channel length and the risk associated with an incorrect estimation of this length. Indeed, to determine the channel length is effectively a model order selection problem, for which no optimal solution is known. We propose a novel systematic approach to the problem under study, which circumvents the estimation of the channel length. The key idea is to model the channel impulse response via a mixture Gaussian model, which has one component for each possible channel length. The parameters of the mixture model are estimated from a received pilot sequence. We derive the optimal receiver associated with this mixture model, along with some computationally efficient approximations of it. We also devise a receiver, consisting of a bank of soft-output Viterbi algorithms, which can deliver soft decisions. Via numerical simulations, we show that our new method can outperform conventional adaptive Viterbi equalizers that use a fixed or an estimated channel length.     

Improving bit-error-rate performance of the free-space optical communications system with channel estimation based on radiative transfer theory

In order to improve the performance of terrestrial free-space optical communication systems in adverse visibility conditions, we present a method for estimation of the atmospheric channel impulse response function which governs the optical intensity propagation. This method reduces run-time computational demands and system complexity in comparison to our previously proposed dual-wavelength channel estimation technique. We consider propagation of optical wavelengths in fog, where the droplet diameters are close to the wavelength and thus scattering and absorption effects are significant. A method for rapid calculation of a channel response function based on estimating the effective optical depth of the channel and curve-fitting is described. The channel response estimate can then be used to design a receiver-side equalizer (minimum meansquared error linear equalizer) to correct the signal distortion due to propagation through the dispersive channel. The channel estimates are based on parametric curve-fitting functions which have been developed using the modified-vector radiative transfer theory to model the channel response. The optimal fit parameters are found using particle-swarm optimization to minimize the simulated bit-error rate of the received signal.