利用确定性信道盲估计改善DFE均衡器的收敛特性

针对一种全盲的判决反馈均衡器(DFE)进行研究,它对快速时变信道的均衡非常有效。利用基于二阶统计量(SOS)的子空间法来得到关于信道系数的确定性估计,然后利用这个估计值求出DFE抽头系数在MMSE意义上的最优解,以此进行DFE的初始化。相比传统的利用周期性训练序列初始化DFE的方法,文章提出的方法不仅节省了带宽资源,而且对于信道的突发性时变能够自适应地重新初始化,从而避免了DFE均衡器的病态收敛。     

一种多波形调制系统中的码片级判决反馈均衡器

多波形调制是指一个码元可以由多个波形来表示多个比特的调制方法,其中每一个波形由一组码片组成,所以可以看作是直接序列扩频调制的推广.由于这种调制方式比直扩具有更高的频谱效率,所以在高速无线通信系统中正受到越来越多的重视.本文针对这种调制系统,研究了一种码片级的判决反馈均衡器(DFE),导出了这种均衡器在最小均方误差(MMSE)准则下的最优权系数.为了降低其计算量,提出了一种低复杂度实现算法.仿真结果表明,这种均衡器的性能优于码片级线性均衡器,也优于只抑制码元间干扰(ISI)的码元级判决反馈均衡器.     

自适应均衡算法在信道均衡技术中的应用研究

文中描述了两种非线性均衡器分别为判决反馈均衡器(DFE)和最大似然序列估计(MLSE)均衡器.所用信道模型为加性白高斯噪声信道,在DFE和线性均衡器(LE)中都是使用递归最小二乘(RLS)算法和最小均方(LMS)算法对数据进行分块处理.MLSE均衡器中使用了维特比最佳译码算法.就误比特性能来做以比较,DFE远好于LE,MLSE均衡器又明显优于DFE,并且它能达到几乎最优的性能.     

稀疏多径信道的T/2间隔CFE均衡器研究

完全反馈均衡器(Complete Feedback Equalizer, CFE)是判决反馈均衡器(DFE)的改进。该文提出了一种T/2分数间隔稀疏CFE(T/2 Sparse CFE, T/2-SCFE)结构,以避免接收机对于符号定时误差的敏感性,并有效利用长时延扩展多径信道的稀疏性来降低均衡器的复杂度。理论分析与基于实测信道的计算机仿真表明, T/2-SCFE均衡器对符号定时误差保持了稳健性,总体性能优于符号间隔CFE及分数间隔DFE。     

移动OFDM系统中的ICI消除算法研究

在宽带OFDM系统中,高速移动引起的多普勒效应导致载波间干扰(InterCarrier Interference, ICI)。该文在充分利用ICI分布特性的基础上,使用部分信道信息推导出两种新的低复杂度MMSE和DFE均衡算法。分析及仿真表明,新算法能够灵活可控地实现接收机性能与复杂度的有效折衷。在计算开销近似相同的条件下,提出的DFE均衡方案有效消除了传统接收机差错性能的地板效应(error floor),从而获得了性能改善。     

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。     

应用于10 Gbit/s光通信及背板传输的自适应均衡器设计

描述了一种既可用于背板传输也可用于光纤通信的高速串行收发器前端均衡器的设计。为适应光信号在传播中的色散效应,使用前馈均衡器(FFE)加判决反馈均衡器(DFE)的组合,取代了背板通信中常用的连续时间线性均衡器(CTLE)和DFE的组合。设计使用3 pre-tap、3 post-tap和1个main tap的抽头组合方式,兼顾pre-cursor和post-cursor的信号失真,有效补偿范围为15 dB。补偿系数采用完全自适应算法调整,对FFE采用模拟MSE算法调整,DFE引擎采用1/16速率数字sign-sign最小均方差(LMS)算法实现。芯片使用UMC 28 nm工艺流片,输入信号频率为10 Gbit/s。     

基于噪声预测的单载波MIMO系统的频域均衡

 针对循环前缀采用特定序列(UW)的多输入多输出单载波频域均衡(MIMO-SCFDE)系统,提出了基于噪声预测的频域均衡器.在均衡器的输出端,根据数据估计噪声和UW估计噪声的相关特性,由UW噪声来预测和抵消数据估计中的噪声,从而改善了系统性能.根据迫零(ZF)和最小均方误差(MMSE)准则,分别推导了ZF和MMSE噪声预测均衡器.理论分析说明,与传统的MIMO频域均衡器相比,ZF噪声预测均衡器的输出端可以获得更低的噪声功率,MMSE噪声预测均衡器输出端的均方误差更小.实验仿真表明,噪声预测的频域均衡方法与传统方法相比可以获得明显的性能增益.     

一种用于水声相干通信系统的自适应均衡算法

多通道判决反馈均衡器（MC—DFE）是水声相干通信克服信道多径效应、消除码间干扰（ISI）的主要手段。为了减少多通道数据处理的复杂性、优化算法、提高算法精度,该文提出了自适应自最优预合并多通道判决反馈均衡算法。该算法将快速自优化LMS分集合并（FOLMSDC）算法、快速自优化LMS（FOLMS）算法和快速自优化LMS相位补偿（FOLMSPC）算法有机地结合在一起,使用统一的误差信号,按照最小化均方误差（MMSE）准则调节各部分的系数,从而实现均衡器性能的全局最优。仿真试验和湖上试验对该算法的性能进行了分析。实验结果表明,该文提出的算法可以进一步减少运算量,提高通信系统的接收性能,算法性能优于已有算法。     

数字微波自适应判决反馈均衡器

本文在分析判决反馈均衡器(DEF)和分数间隔均衡器(FSE)的基础上,提出了一种新型实用的判决反馈均衡器方案,设计出了实用的DFE硬件电路并给出了实验结果。     

Performance improvement in decision feedback equalisers by using`soft decision'

One method to combat intersymbol interference (ISI) in digital transmission is to use a decision feedback equaliser (DFE). It is well known that a major part of the errors made by a DFE is due to the fact that the equaliser makes instantaneous decisions without taking into account data received later. It is therefore obvious that the performance of a DFE could be improved by allowing some type of `soft decision'. A method to introduce `soft decisions' in a decision feedback equaliser is presented. The method gives a performance improvement in SNR of about 1.5 dB with almost no increase in complexity compared to an ordinary DFE     

Approach to blind decision feedback equalisation

Based on the fact that a decision feedback equaliser (DFE) can be divided into a linear equaliser and a prediction error filter with feedback, a new blind adaptation method for the DFE is proposed. The proposed method provides reliable convergence and the resulting symbol error rate is very close to that of the DFE using a training sequence     

Adaptive turbo reduced-rank equalization for MIMO channels

An adaptive iterative (turbo) decision-feedback equalizer (DFE) for channels with intersymbol interference (ISI) is presented. The filters are computed directly from the soft decisions and received data to minimize a least-squares (LS) cost function. Numerical results show that this method gives a substantial improvement in performance relative to a turbo DFE computed from an exact channel estimate, assuming perfect feedback. Adaptive reduced-rank estimation methods are also presented, based on the multistage Wiener filter (MSWF). The adaptive reduced-rank turbo DFE for single-input/single-output channels is extended to multiple-input/multiple-output (MIMO) channels with ISI and multiple receive antennas. Numerical results show that for MIMO channels with limited training, the reduced-rank turbo DFE can perform significantly better than the full-rank turbo DFE.     

On the Decision-Feedback Equalizer in Optically Amplified Direct-Detection Systems

This paper investigates the combined feed-forward and decision-feedback equalizer (DFE) in a lightwave system with optical amplifiers and a direct-detection receiver. Based on a nonlinear channel model, the paper provides a modification of the classical minimum mean square error theory of the DFE. Furthermore, an analytical method that is capable of accurate bit error rate (BER) evaluation is used to optimize the DFE for minimum BER. The paper evaluates the DFE performance for both optical ON-OFF keying and duobinary modulation formats in the presence of chromatic dispersion as well as the DFE performance for the mitigation of higher order polarization mode dispersion in first-order compensated systems. The paper shows that the DFE can compensate for the BER degradation due to narrow-band receiver-side optical filtering and can significantly improve the spectral efficiency of dense wavelength-division multiplexed systems.     

Parallel adaptive decision feedback equalizers

Several algorithms for parallel implementation of adaptive decision feedback equalizers (DFEs) are proposed. The first is a double-row DFE algorithm that outperforms previous approaches. Under the no-error-propagation assumption, the algorithm will perform exactly like a serially adapting DFE. The multiplication complexity of the double-row DFE algorithm is of the same order as that of the parallel DFE algorithm and the extended least-mean-square (LMS) method. The previous algorithms and the double-row DFE algorithm may become impractical to implement due to their large computational complexity, so three additional parallel implementations of the DFE, which lead to considerable hardware savings and avoid the coding loss of the former approaches, are presented. The different algorithms are compared on the basis of convergence analysis and simulation results     

基于粒子群优化的MIMO系统判决反馈均衡研究

为了更好地消除多输入多输出(MIMO)系统中的码间干扰(ISI),提出了一种基于粒子群优化(PSO)的判决反馈均衡(DFE)算法;将该算法与基于最小均方(LMS)算法的判决反馈均衡进行了比较.仿真结果表明,PSO DFE的误码率性能明显优于LMS DFE,且收敛速度快.     

Adaptive combined DFE/MLSE techniques for ISI channels

By embedding a decision-feedback equalizer (DFE) into the structure of a maximum-likelihood sequence estimator (MLSE), an adaptive combined DFE/MLSE scheme is proposed. In this combined DFE/MLSE, the embedded DFE has three functions: (i) prefiltering the received signals and truncating the equivalent channel response into the desired one, (ii) compensating for channel distortions, and (iii) providing the MLSE detector with predicted values of input signals. Since the embedded MLSE detector operates on the predicted signals the detected symbols at the output of the DFE/MLSE do not suffer any delay and can be directly fed back into the embedded DFE so that the error propagation, which usually takes place in a conventional DFE, can be greatly reduced. Analytical and simulation results indicate that the performance is significantly improved by the DFE/MLSE compared to the conventional DFE while its computation complexity is much less than that of the conventional MLSE receiver. The combined DFE/MLSE can use different adaptive structures (block-updating, sliding window updating or symbol-by-symbol updating) to meet different performance objectives. Moreover, the proposed DFE/MLSE provides a trade-off between performance and complexity with a parameter m representing the MLSE detection depth as well as the number of predicting steps of the embedded DFE. For some particular values of m, this scheme is capable of emulating the conventional DFE, MLSE-VA, adaptive LE-MLSE equalizer, adaptive DDFSE, and adaptive BDFE without detection delay     

Decision feedback equaliser based on fuzzy logic

A method for reducing the effect of error propagation in a decision feedback equaliser (DFE) by using fuzzy logic is presented. A simple membership function is proposed and adaptively determined from data. Simulation results are presented to show that the combination of the DFE and fuzzy logic is promising     

SCFNN-Based Decision Feedback Equalization Robust to Frequency Offset and Phase Noise

This paper proposes a self-constructing fuzzy neural network-based decision feedback equalizer (SCFNN DFE). An online learning algorithm containing the structure and parameter learning phases is employed in training the SCFNN DFE. Specifically, the feedforward input vector classification and a gradient-descent method are both used in this online learning algorithm. We show by simulations that the proposed SCFNN DFE offers improvement compared to the traditional DFE methods in the presence of frequency offset and phase noise.