一种新颖的基于差分结构的判决反馈均衡器

多用户型的检测系统，既要求低误码率（BER），又要求较低计算复杂度．本文以此为目的提出一种新颖的具有差分结构的多用户反馈判决反馈均衡器系统，它是由多级具有差分结构的误差反馈滤波器的判决反馈均衡器（DFE-EFF）构成．此算法选择较优的多用户干扰抵消的具有误差反馈滤波器的判决反馈均衡器（多用户干扰抵消．DFEEFF,即多用户反馈DFE-EFF）的方法；并采用差分结构来解决因性能提升所带来复杂度增加的问题．文中给出仿真结果，就多用户反馈DFE-EFF以及具有差分结构的多用户反馈DFE-EFF性能和计算效率做出比较分析。     

运用误差反馈的判决反馈均衡器的性能分析

判决反馈均衡器（Decision Feedback Equalizer，DFE）能补偿具有严重符号间干扰（Inter Symbol Interference，ISI）的信道，且不存在线性均衡器增强噪声的影响。而在其基础上改进的运用误差反馈的DFE，可利用误差反馈滤波器来减少传统DFE中存在的误差信号的相关性，同时其硬件实现的复杂度没有明显提高。理论分析和仿真表明，这种方法比传统的DFE更有效，特别是针对信道有严重符号间干扰的情况。     

一种T/4分数间隔预测判决反馈盲均衡算法

为了消除由深衰落信道引起的严重码间干扰,本文给出一种基于改进双模式多模算法的 T/4 分数间隔预测判决反馈均衡器.该均衡器采用改进的双模式多模算法独立优化前向、反馈滤波器,前向滤波器采用 T/4 分数间隔,反馈滤波器采用预测结构,仍为符号速率.在两种深衰落信道条件下仿真实验表明,本文给出的均衡器能够避免传统判决反馈均衡器的误收敛情况;同时其稳态均方误差小于基于常模算法的 T/4 分数间隔均衡器、基于常模算法的 T/4 分数间隔判决反馈均衡器,以及基于双模式多模算法的 T/4 分数间隔预测判决反馈均衡器,更适合处理 QAM 信号.     

基于非单点模糊RBF网络的判决反馈均衡器

提出了一种具有较强抗突发干扰能力的非单点模糊径向基函数（Radial Basis Function,RBF）网络判决反馈均衡器.该方法将具有前置滤波特性的非单点模糊化技术引入RBF网络,利用梯度下降法自适应调整参数.通过仿真实验,并与基于径向基函数网络的判决反馈均衡器（Radial Basis Function Network-Decision Feedback Equalizer,RBFN-DFE）和传统判决反馈均衡器（Decision Feedback Equalizer,DFE）进行比较,结果证明该方法抗突发干扰能力强,误码性能好.     

基于小波包变换和判决反馈RBF网络的组合均衡器

本文提出了一种基于小波包变换和判决反馈RBF网络的组合非线性均衡器的结构和算法.首先将信号进行小波包分解,再将分解后的信号分量送入带有判决反馈结构的RBF神经网络进行均衡.一方面,小波包具有很强的去相关能力,可以提高均衡器的收敛速度;另一方面,RBF神经网络具有较强的非线性模式分类能力,可降低均衡器的均方误差.在仿真实验中,针对无线通信数字信号传输过程中由于多径效应和信道衰落而产生的码间干扰(ISI)问题,比较了最小均方(LMS)算法和组合均衡器算法的均衡效果,结果表明,组合均衡算法具有更快的收敛速度,更低的误码率.     

联合均衡块迭代软判决反馈干扰抵消接收机

CDMA扩频通信系统在低扩频比时,路径间干扰(IPI, inter-path interference)变得非常严重。本文将块迭代干扰抵消同MMSE均衡器相结合,提出了一种适用于CDMA扩频通信系统低扩频比情况下的联合均衡块迭代软判决反馈干扰抵消(MMSE-BIIC)接收机结构。理论分析与计算机仿真表明,本文提出的MMSE-BIIC接收机同传统的Rake接收机、线性MMSE均衡器以及多级干扰抵消接收机相比在性能上有较大改善。     

基于FPGA的分数间隔预测判决反馈均衡器的设计与实现

针对高阶QAM信号,本文设计实现了一种基于FPGA的分数间隔预测判决反馈均衡器.前向滤波器采用分数间隔结构,反馈部分采用预测结构,仍保持符号速率.该均衡器充分结合了分数间隔均衡器与预测判决反馈均衡器的优势,能够消除由深衰落信道引起的严重码间干扰.同时,该均衡器采用简化后的双模式多模算法独立优化前向、反馈滤波器,简化算法...     

基于自适应步长LMS算法的判决反馈均衡器研究

本文研究了一种基于自适应步长LMS算法的自适应判决反馈均衡器算法，此算法通过对前向滤波器、后向滤波器和锁相环参数分别进行实时的、自适应步长的调整，在均衡时变多径的同时高精度地锁定接收信号相位，因此适用于相干通信系统。仿真结果表明在相同多径条件下，该方法比基于固定步长LMS算法的判决反馈均衡器输出误码率低1-7倍。     

用判决反馈自适应均衡器抑制多音干扰

从理论上分析了在QPSK系统中用判决反馈自适应均衡器抑制多音干扰的性能,给出了均衡器最佳系数与最小均方误差计算公式,指出QPSK系统中的均衡器能同时起到抑制多音干扰的作用。利用此性能可使设备简化。     

一种基于FPGA的判决反馈均衡器

论述了一种基于现场可编程门阵列( FPGA)的判决反馈均衡器的设计.首先介绍了判决反馈均衡器的原理,然后给出了Simulink仿真的电路和仿真结果,介绍了用分布式算法(DA)来实现滤波器的原理和各模块原理,最后给出Modelsim仿真结果.     

A blind decision feedback equalizer incorporating fixed lagsmoothing

A new type of blind decision feedback equalizer (DFE) incorporating fixed lag smoothing is developed in this paper. The structure is motivated by the fact that if we make full use of the dependence of the observed data on a given transmitted symbol, delayed decisions may produce better estimates of that symbol. To this end, we use a hidden Markov model (HMM) suboptimal formulation that offers a good tradeoff between computational complexity and bit error rate (BER) performance. The proposed equalizer also provides estimates of the channel coefficients and operates adaptively (so that it can adapt to a fading channel for instance) by means of an online version of the expectation-maximization (EM) algorithm. The resulting equalizer structure takes the form of a linear feedback system including a quantizer, and hence, it is easily implemented. In fact, because of its feedback structure, the proposed equalizer shows some similarities with the well-known DFE. A full theoretical analysis of the initial version of the algorithm is not available, but a characterization of a simplified version is provided. We demonstrate that compared to the zero-forcing DFE (ZF-DFE), the algorithm yields many improvements. A large range of simulations on finite impulse response (FIR) channels and on typical fading GSM channel models illustrate the potential of the proposed equalizer     

Adaptive decision feedback equalization: can you skip the trainingperiod?

This paper presents a novel unsupervised (blind) adaptive decision feedback equalizer (DFE). It can be thought of as the cascade of four devices, whose main components are a purely recursive filter (ℛ) and a transversal filter (𝒯). Its major feature is the ability to deal with severe quickly time-varying channels, unlike the conventional adaptive DFE. This result is obtained by allowing the new equalizer to modify, in a reversible way, both its structure and its adaptation according to some measure of performance such as the mean-square error (MSE). In the starting mode, ℛ comes first and whitens its own output by means of a prediction principle, while 𝒯 removes the remaining intersymbol interference (ISI) thanks to the Godard (1980) (or Shalvi-Weinstein (1990)) algorithm. In the tracking mode the equalizer becomes the classical DFE controlled by the decision-directed (DD) least-mean-square (LMS) algorithm. With the same computational complexity, the new unsupervised equalizer exhibits the same convergence speed, steady-state MSE, and bit-error rate (BER) as the trained conventional DFE, but it requires no training. It has been implemented on a digital signal processor (DSP) and tested on underwater communications signals-its performances are really convincing     

Fuzzy stochastic gradient decision feedback equalizer for VSBterrestrial HDTV broadcasting

We present a fuzzy stochastic gradient (FSG) decision feedback equalizer (DFE) for VSB terrestrial HDTV broadcasting. This equalizer employs a well-designed fuzzy Takagi-Sugeno (1985) model to automatically regulate the step size of the descent gradient vector, combining a fast convergence rate and a low excess mean square error (MSE). The only penalty paid is a slight increase in the computational complexity compared with the LMS algorithm. Simulation results show that this equalizer provides 3.5 dB signal-to-noise ratio (SNR) improvement at a BER of 3.0×10^-6 with respect to the conventional LMS DFE recommended by the Grand Alliance     

Iterative Equalization using Improved Block DFE for Synchronous CDMA Systems

Iterative equalization using optimal multiuser detector and optimal channel decoder in coded CDMA systems improves the bit error rate (BER) performance tremendously. However, given large number of users employed in the system over multipath channels causing significant multiple-access interference (MAI) and intersymbol interference (ISI), the optimal multiuser detector is thus prohibitively complex. Therefore, the sub-optimal detectors such as low-complexity linear and non-linear equalizers have to be considered. In this paper, a novel low-complexity block decision feedback equalizer (DFE) is proposed for the synchronous CDMA system. Based on the conventional block DFE, the new method is developed by computing the reliable extrinsic log-likelihood ratio (LLR) using two consecutive received samples rather than one received sample in the literature. At each iteration, the estimated symbols by the equalizer is then saved as a priori information for next iteration. Simulation results demonstrate that the proposed low-complexity block DFE algorithm offers good performance gain over the conventional block DFE.     

A new adaptive functional-link neural-network-based DFE forovercoming co-channel interference

A new approach for the decision feedback equalizer (DFE) based on the functional-link neural network is described. The structure is applied to the problem of adaptive equalization in the presence of intersymbol interference (ISI), additive white Gaussian noise, and co-channel interference (CCI). It is shown through simulation results for a severe amplitude distorted co-channel system that the decision feedback functional-link equalizer (DFFLE) provides significantly superior bit-error rate (BER) performance characteristics compared to the conventional DFE, the linear transversal equalizer (LTE), the nonlinear radial basis function (RBF) neural-network-based structures and the feed-forward functional-link equalizer (FFLE)-based structures. The DFFLE is also shown to have a significantly simpler computational requirement relative to the RBF and the FFLE     

Block sequential least squares decision feedback equalizationalgorithm with application to terrestrial HDTV transmission

Due to the very high symbol rate of terrestrial HDTV systems, up to now there exist no equalization solutions with sufficiently low hardware complexity and satisfactory performance for commercial applications. We present a block sequential least squares decision feedback equalization algorithm with application to over-the-air HDTV channels. The proposed adaptive algorithm is derived on the basis of minimization of the least squares criterion, thereby achieving faster convergent and tracking rate relative to the recommended LMS algorithm. Meanwhile, good numerical stability is guaranteed because it successfully eliminates time updates of the filtering coefficients, which is the main cause of instability of FTF-like algorithms. Also of great significance is its drastic reduction in computational complexity by means of block operation, taking advantage of the slowly time-varying nature of terrestrial broadcasting channels. Simulation results show that the equalizer achieves an almost 3.5 dB SNR improvement at a bit error rate of 3×10^-6 without significant increase in computational complexity, as compared to the conventional LMS decision feedback equalizer (DFE) when applied to the equalization of over-the-air HDTV channels     

Adaptive decision feedback equalization for digital satellitechannels using multilayer neural networks

This paper introduces an adaptive derision feedback equalization using the multilayer perceptron structure of an M-ary PSK signal through a TDMA satellite radio channel. The transmission is disturbed not only by intersymbol interference (ISI) and additive white Gaussian noise, but also by the nonlinearity of transmitter amplifiers. The conventional decision feedback equalizer (DFE) is not well-suited to detect the transmitted sequence, whereas the neural-based DFE is able to take into account the nonlinearities and therefore to detect the signal much better. Nevertheless, the applications of the traditional multilayer neural networks have been limited to real-valued signals. To overcome this difficulty, a neural-based DFE is proposed to deal with the complex PSK signal over the complex-valued nonlinear MPSK satellite channel without performing time-consuming complex-valued back-propagation training algorithms, while maintaining almost the same computational complexity as the original real-valued training algorithm. Moreover, a modified back-propagation algorithm with better convergence properties is derived on the basis of delta-bar-delta rule. Simulation results for the equalization of QPSK satellite channels show that the neural-based DFE provides a superior bit error rate performance relative to the conventional mean square DFE, especially in poor signal-to-noise ratio conditions     

Reliability feedback–aided low‐complexity detection in uplink massive MIMO systems

Massive multiple‐input multiple‐output (MIMO) plays a crucial role in realizing the demand for higher data rates and improved quality of service for 5G and beyond communication systems. Reliable detection of transmitted information bits from all the users is one of the challenging tasks for practical implementation of massive‐MIMO systems. The conventional linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) achieve near‐optimal bit error rate (BER) performance. However, ZF and MMSE require large dimensional matrix inversion which induces high computational complexity for symbol detection in such systems. This motivates for devising alternate low‐complexity near‐optimal detection algorithms for uplink massive‐MIMO systems. In this work, we propose an ordered sequential detection algorithm that exploits the concept of reliability feedback for achieving near‐optimal performance in uplink massive‐MIMO systems. In the proposed algorithm, symbol corresponding to each user is detected in an ordered sequence by canceling the interference from all the other users, followed by reliability feedback‐based decision. Incorporation of the sequence ordering and the reliability feedback‐based decision enhances the interference cancellation, which reduces the error propagation in sequential detection, and thus, improves the BER performance. Simulation results show that the proposed algorithm significantly outperforms recently reported massive‐MIMO detection techniques in terms of BER performance. In addition, the computational complexity of the proposed algorithm is substantially lower than that of the existing algorithms for the same BER. This indicates that the proposed algorithm exhibits a desirable trade‐off between the complexity and the performance for massive‐MIMO systems.     

滑动窗快速横向滤波的自适应判决反馈均衡器算法

本文提出了一种基于滑动窗广义多路快速横向滤波（ＳＷＦＴＦ）的自适应判决反馈均衡器（ＤＦＦ）算法，它具有快速跟踪性能，故可用于快速时变多径衰落的信道。文中推导了ＳＷＦＴＦ－ＤＦＥ算法。在数字移动通信信道模型上，利用计算机模拟，在均方误差和误码率特性方面与其它均衡器算法进行了比较。