自适应切换双模盲均衡算法

常数模算法在无线通信系统的盲均衡中得到广泛的使用,为了进一步降低稳态误差通常选择将其与判决引导最小均方误差算法相结合,传统的双模切换盲均衡算法通过人为设定门限值实现两种算法的硬切换,其切换时机选择的合理性无法保证,不能充分凸显双模切换的长处。该文利用凸组合结构借助遵循一定规则迭代变化的联合参数将两种算法进行结合,实现两种算法模式的切换,自适应地选择切换时机,并通过对算法的修正和混合参数归一化的改进使在克服恢复信号相位偏转的同时加快了收敛速率、降低了稳态误差;另外,对稳态性能进行推导分析得到了理论的稳态模型。仿真结果证明稳态性能与模型推导结果保持一致,参数归一化改进所得效果较为明显,与同类其它双模切换算法相比具有更优的性能。     

一种并行的软判决引导常数模盲均衡算法

介绍了一种常数模与软判决引导结合的盲均衡算法CMA SDD。该算法克服了CMA收敛速度慢、稳态误差大的缺点,同时可以纠正相位偏转。仿真表明,在高信噪比(SNR)条件下,两种算法的稳态均方误差相近,而CMA SDD算法的收敛速度更快;在低信噪比条件下,CMA SDD算法可以获得更快的收敛速度和更低的稳态均方误差。     

基于CMA算法的双模式盲均衡算法

常数模算法（ＣＭＡ）的收敛速度非常缓慢。为了加快收敛过程，一旦误码率降低到足够低，该算法必须切换到ＤＤ算法。为了克服这些缺点，本文利用ＱＡＭ信号分布在几个已知半径的圆上的特点，提出了两类多模盲均衡算法。在此基础上又提出一种双模式均衡方案：多模算法模式和常数模算法模式。研究表明，两类多模算法及双模式均衡方案收敛快，收敛性能也令人满意     

十字形QAM信号的盲均衡技术研究

提出两种适用于十字形正交幅度调制QAM(Quadrature amplitude modulation)信号的盲均衡算法:改进的常十字模算法(MCXA)和基于判决引导的改进的常十字模算法(DD-MCXA).MCXA在CXA基础上重构了代价函数,在其代价函数中增加了星座图匹配误差(CME)函数,CXA部分提供了全局收敛,CME部分用来减少剩余误差和加快收敛速度.DD-MCXA在保持CXA部分提供良好的最初收敛的优点的同时,自动削弱了CXA部分在局部收敛区域的误差影响,从而进一步提高了均衡性能.通过对户外无线微波信道模型的仿真实验,验证了这两种算法良好的均衡性能.     

基于组合代价函数的双模式盲均衡新算法

以常数模和判决引导准则设计的双模式盲均衡算法可显著提高均衡性能,目前已有双模式盲均衡算法均需设置切换参数且切换参数选择和设定缺乏理论依据.为解决双模式盲均衡算法中切换参数难以确定的问题,提出来一种组合代价函数的双模式盲均衡新算法.利用常数模和判决引导准则通过加权设计代价函数,在盲均衡器更新过程中自适应调节权值实现算法由常数模算法向判决引导算法的切换,避免了在双模式算法中设计切换参数,提高了算法的泛化性能.为克服常数模算法相位盲的缺点,在虚实分开改进的常数模算法基础上优化组合代价函数以及盲均衡器更新算法的设计,进一步提高了算法收敛性能.仿真结果证明,组合代价函数双模式盲均衡新算法可充分发挥常数模算法和判决引导算法的优点,具有更快的收敛速度和更小的稳态剩余误差.     

基于MMMA的双模式变步长盲均衡算法

恒模算法(CMA)是一种重要的盲均衡技术,虽然能达到盲均衡效果,但是收敛后有较大的剩余误差和相位模糊。在VS-MCMA+DD-LMS算法的基础上进行了改进,提出变步长修正多模算法(VS-MMMA)和DD-LMS相结合,以及变步长解相关修正多模算法(VS-UMMMA)和DD-LMS相结合两种新算法,新算法在收敛速度和均衡误差方面均有明显的改进。最后,对该算法进行了仿真和性能分析。     

适用于圆形高阶QAM信号的盲均衡算法

针对现有盲均衡算法大多仅适用于方形或十字形QAM信号的问题,本文提出了一种适用于圆形高阶QAM信号的混合盲均衡算法。该算法首先对区域多模盲均衡算法进行改进,提出了新的区域划分方法并将其与改进的多模盲均衡算法相结合,构造了新的误差模型使之与圆形星座匹配程度大大提高;然后将其与判决引导算法和数据重用技术有机结合,构成低稳态误差的快速混合盲均衡算法。与其他同类型盲均衡算法相比,本文算法对于圆形星座高阶QAM信号尤为有效,不仅降低了稳态误差,且大幅度减少了收敛所需符号数。仿真结果验证了该算法的有效性。      

高速突发传输中的均衡算法

通过对几种常用均衡算法的比较,确定高速突发传输中使用收敛速度较快、运算量较小的VLMS均衡算法。为降低高速突发信号中的训练序列开销,提高传输效率,提出了一种新的混合结构均衡方案。新方案充分利用数据辅助均衡与盲均衡的各自特点,将均衡过程分成两个阶段,数据辅助均衡阶段为盲均衡阶段提供一个足够精确的初始值,引导盲均衡实现快速收敛。仿真结果表明,该混合结构均衡方案能利用短训练序列实现收敛,而且能达到较好的性能。     

双模式变步长解相关盲均衡算法研究

恒模算法(CMA)是一种重要的盲均衡技术,虽然能达到盲均衡效果,但是收敛后会有较大的剩余误差和相位模糊。在文献[3]中提出的VS-MCMA+DD-LMS算法的基础上做了进一步改进,提出变步长修正多模算法(VS-MMMA)和DD-LMS相结合,以及变步长解相关修正多模算法(VS-UMMMA)和DD-LMS相结合2种新算法,进行了仿真和性能分析,证明新算法在收敛速度和均衡误差方面均有明显的改进。     

Blind Equalization and Carrier Recovery Using a "Stop-and-Go" Decision-Directed Algorithm

We show that the standard decision-directed estimatedgradient adaptation algorithm for joint MSE equalization and carrier recovery, normally utilized in the open-eye condition, can be turned into an algorithm providing effective blind convergence in the MSE sense, usable in the closed-eye startup phase with no need of a known training sequence. This is obtained by means of a simple flag telling both the equalizer and the synchronizer whether the current output error with respect to the decided symbol is sufficiently reliable to be used. If not, adaptation is stopped for the current iteration. In the paper, this "stopand-go" decision-directed algorithm is presented for both linear and decision-feedback MSE complex equalizers with joint blind carrier recovery. Simulation results demonstrate the effectiveness of the proposed technique.     

Sign algorithms for blind equalization and their convergence analysis

In blind equalization a communication channel is adaptively equalized without resorting to the usual training sequence. In this paper we have introduced two new algorithms for blind equalization, which hard limit the equalizer input or the error at the output of the equalizer. These new algorithms are simple to implement and reduce the number of multiplications by approximately one-half. We show by way of simulations that the performance of the algorithm resulting from hardlimiting the error is comparable with the performance of the corresponding algorithm in which the error is not hardlimited. We formulate the new sign-error algorithm as a stochastic minimization of an error functional and demonstrate that the case of zero intersymbol interference corresponds to local minima of this error functional. We also present convergence analysis to predict the output mean square error in both these sign algorithms. Since the algorithms are highly nonlinear we incorporate several simplifying approximations and provide heuristic justifications for the validity of these approximations when the algorithms are operated in a typical practical environment. Computer simulations demonstrate the accuracy of the predicted convergence behavior.This research was supported in part by the Office of Naval Research under Contract N00014-89-J-1538.     

Normalized sliding window constant modulus and decision-directedalgorithms: a link between blind equalization and classical adaptivefiltering

By minimizing a deterministic criterion of the constant modulus (CM) type or of the decision-directed (DD) type, we derive normalized stochastic gradient algorithms for blind linear equalization (BE) of QAM systems. These algorithms allow us to formulate CM and DD separation principles, which help obtain a whole family of CM or DD BE algorithms from classical adaptive filtering algorithms. We focus on the algorithms obtained by using the affine projection adaptive filtering algorithm (APA). Their increased convergence speed and ability to escape from local minima of their cost function make these algorithms very promising for BE applications     

基于T/4分数间隔采样双模式盲均衡算法的研究

本文针对恒模算法(CMA)收敛速度较慢、收敛后均方误差较大的缺点,提出一种新的双模式盲均衡算法。先采用T/4分数间隔采样的盲均衡算法,由于采用了过采样技术,避免了因欠采样引起的频谱混叠,然后在算法收敛后切换到判决引导(DD-LMS)算法,减少误码率。计算机仿真表明,本文提出的新算法有较快的收敛速度、较小的稳态误差和较低的误码率。     

基于数据重用的集员滤波拟仿射投影盲均衡算法

传统常模盲均衡算法应用广泛但是其收敛速率很慢.为了满足在短突发数据条件下的信道盲均衡,提出一种基于数据重用的集员滤波拟仿射投影盲均衡算法.该算法将仿射投影思想结合到常模算法中,利用多数据向量同时提取信道信息,再附加改进的集员滤波算法有效减小了运算量,并结合数据重用思想重新设计所匹配的数据重用方式.仿真结果证明所提算法具有较快的收敛速率,与同类算法相比能提前700个迭代点收敛,且在信噪比为10 dB以上的信道环境中也有较好的效果,能够在短突发数据信道均衡中有效发挥作用.     

一种适用于MPSK调制的混合型盲均衡算法

提出了一种特别适用于ＭＰＳＫ信号的混合型盲均衡算法，理论分析和计算机模拟表明，这种混合型盲均衡算法的收敛性能与Ｇｏｄａｒｄ类盲均衡算法的收敛性能相同，而误码性能优于Ｇｏｄａｒｄ类盲均衡算法，并接近系统ＬＭＳ均衡算法的性能，是一种具有实用价值的盲均衡算法。     

并行多信道判决引导CDL盲均衡算法

针对传统算法抽头系数较长和难以处理深衰落信道的问题,提出适用于通用数据链信号的并行多信道判决引导盲均衡算法。新算法借助判决反馈结构中前馈、反馈滤波器的独立优化特性,将加权后的判决值代入下一次的迭代过程,有利于消除拖尾效应;综合运用采样率高的分数间隔结构与稳态误差小的判决引导算法,对接收信号采用并行处理的方式,大大缩短了抽头系数长度。实验结果表明,针对CDL信号,新算法能够有效提升收敛速度、降低误码率。      

Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems

Conventional equalization and carrier recovery algorithms for minimizing mean-square error in digital communication systems generally require an initial training period during which a known data sequence is transmitted and properly synchronized at the receiver. This paper solves the general problem of adaptive channel equalization without resorting to a known training sequence or to conditions of limited distortion. The criterion for equalizer adaptation is the minimization of a new class of nonconvex cost functions which are shown to characterize intersymbol interference independently of carrier phase and of the data symbol constellation used in the transmission system. Equalizer convergence does not require carrier recovery, so that carrier phase tracking can be carried out at the equalizer output in a decision-directed mode. The convergence properties of the self-recovering algorithms are analyzed mathematically and confirmed by computer simulation.     

Performance analysis of error propagation effects in the DFE for ATSC DTV receivers

The paper analyzes the error propagation phenomenon in the decision feedback equalizer (DFE) for the receivers of Advanced Television Systems Committee (ATSC) digital television (DTV) and presents the performance upper-limits of the DFE by comparing various error propagation cases and the no-error propagation case. As one approach to the performance limit, we consider a blind DFE, adopting a trellis decoder with a trace-back depth of 1 as a decision device. Through simulation, we show how much the DFE performance in ATSC DTV receivers is affected by error propagation. We found that while blind equalization is preferable to decision-directed (DD) equalization at signal-to-noise ratio (SNR) values less than 18 dB, DD equalization is superior to blind equalization at SNR values greater than 18 dB. In addition, symbol error rate curves quantitatively show that the performance difference in the DFE caused by error propagation becomes clearer at the trellis decoder following the DFE. The analysis results presented are very informative for developing equalization algorithms for ATSC DTV receivers.     

用于MQAM调制的双模式Sign-CMA盲均衡算法

在Sign-CMA算法基础上，提出了一种用于MQAM数字通信系统的双模式盲均衡算法。该算法克服了CMA算法稳态误差大的缺点，提高了Sign-CMA算法的收敛速度，并能纠正信道存在的相位旋转，实现载波恢复。     

Blind Equalization Based on Data Reuse and Reliability Labeling

In the absence of a training sequence, blind equalization algorithm usually takes thousands or even tens of thousands symbols to converge, which is especially evident when the channel is severely distorted. When the short data is required to achieve equalization, the current blind equalization algorithms will be difficult to converge. This letter presents a short data reuse blind equalization algorithm based on reliability labeling. If the output symbol corresponding to the same input is labeled twice with reliability during the process of the current and last data reuse, the algorithm will switches to the decision-directed model automatically. At the same time the letter gives a simple way for model switching by a look-up table. Simulations show that the proposed algorithm performs better compared with Modified Constant Modulus Algorithm with long data.