非线性信道判决反馈均衡器

本文针对严重线性失真、轻度非线性失真的信道提出了一种简单实用的非线性信道判决反馈均衡器，它是一个将关联模型非线性信道均衡器与线性信道判决反馈均衡器相结合的产物，对典型的非线性信道模型反做的模拟试验表明，该均衡器不仅简单实用，便于实时处理，而且均衡性能得到的较大的改善。     

缓慢衰落信道自适应均衡

本文研究用于连续数据传输的缓慢衰落信道的均衡。比较了线性和判决反馈自适应均衡技术。用Markov过程模型分析了判决反馈均衡器中的误差传播影响。对固定的和衰落的信道以及二元和四元PSK传输计算了错误概率的增大。结果表明,误差传播影响是小的,而且在实用的错误概率范围内,判决反馈均衡器优于线性均衡器。估算了实际判决反馈均衡器的参数,并完成了性能计算。结果表明,隐分集增益是显著的,且发现符号间干扰代价小于1分贝。由于符号间干扰的代阶是小的,所以对这种应用就不必推荐更复杂的非线性处理装置,例如Viterbi算法。时间抖动对均衡器的影响包含在平均错误概率的计算中。     

基于多小波双变换的非线性卫星信道盲均衡算法

针对非线性卫星信道Volterra盲均衡系统收敛缓慢、计算复杂高等不足,提出了基于多小波双变换的非线性卫星信道盲均衡算法.该算法用Wiener均衡器代替Volterra均衡器,减小了均衡器结构的复杂性;用平衡正交多小波对Wiener均衡器的输入信号进行变换,降低了输入信号的自相关性;在Wiener均衡器输出端增加一级判决反馈滤波器,同时对其输入信号作平衡多小波变换,又降低了判决反馈滤波器输出信号的自相关性.仿真结果验证了该算法的有效性.     

无方根广义多道Givens格型算法及其在自适应非线性均衡中的应用

对于自适应非线性均衡问题，当非线性信道的传输函数可由二阶Ｖｏｌｔｅｒｒａ级数表征时，可以作为广义多道滤波问题来处理。本文提出了一种无方根广义多道Ｇｉｖｅｎｓ格型算法来实现该非线性信道均衡器，研究了该均衡器消除由于信道非线性衰落引起的码间干扰的性能。实验结果表明：该自适应非线性均衡器具有快速收敛性能，对非线性信道的均衡效果优于ＬＳ格型判决反馈均衡器。     

基于稀疏多径信道的均衡技术分析

为了解决宽带通信稀疏多径信道下均衡器结构复杂和收敛速度慢的问题,介绍了稀疏多径信道和基于伪随机序列的多径搜索。针对稀疏多径信道的特点,对判决反馈均衡器进行改进,提出了一种基于正交匹配追踪信道估计的完全判决反馈均衡器。利用正交匹配追踪估计结果在前馈均衡器前消除已判决码元对当前判决码元的码间串扰,降低了均衡器的复杂度,提高了收敛速度。通过仿真分析表明完全判决反馈均衡器能取得良好的性能。     

无人机数据链判决反馈均衡器的FPGA实现

从无人机数据链的需求出发,使用现场可编程门阵列(FPGA)实现判决反馈均衡器(DFE),以消除无人机数据链中的码间干扰。文中利用System Generator对判决反馈均衡器进行建模,将模型转换为硬件,并通过硬件协调仿真在Xilinx virtex5 XC5VSX50T芯片上验证。仿真结果表明,在不同信道条件下,判决反馈均衡器能很好地克服码间干扰,适用于无人机信道。本文为无人机高速数据链均衡器的实现打下基础。     

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.     

两种前馈神经网络均衡器在HDSL信道中的性能研究

目前在HDSL中普遍使用自适应判决反馈均衡技术来消除ISI和噪声,但其在线路衰耗较大、线路非线性失真较重或持续强噪声等线路不良情况时,无法正确工作.因此,本文尝试使用两种前向型神经网络模型构建均衡器,替代传统的自适应判决反馈均衡器.在HDSL信道条件下,以传统的DFE性能作为基准,主要采取多层感知机(MLP)、径向基函数(RBF)两种前向型神经网络模型加以仿真,比较分析仿真结果.     

两种前馈神经网络均衡器在HDSL信道中的性能研究

目前在HDSL中普遍使用自适应判决反馈均衡技术来消除ISI和噪声,但其在线路衰耗较大、线路非线性失真较重或持续强噪声等线路不良情况时,无法正确工作.因此,本文尝试使用两种前向型神经网络模型构建均衡器,替代传统的自适应判决反馈均衡器.在HDSL信道条件下,以传统的DFE性能作为基准,主要采取多层感知机(MLP)、径向基函数(RBF)两种前向型神经网络模型加以仿真,比较分析仿真结果.     

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.     

A DECISION FEEDBACK EQUALIZER FOR NON-LINEAR CHANNELS

A Simple and useful decision feedback equalizer used for non-linear channels with severe linear distortion and mild non-linear distortion is proposed. It is a combination of a nonlinear channel equalizer based on connectionist model and a common decision feedback equalizer for linear channels. For a typical non-linear channel model it is shown that the equalization performances of the proposed equalizer are improved significantly.     

Adaptive Equalization for SQPSK and SQPR Over Frequency Selective Microwave LOS Channels

This paper compares the effectiveness of linear and decision feedback equalizers in counteracting the effect of frequency-selective distortion in microwave relay links. A quadratic frequency-selective model of the channel is employed and particular attention is given to channels with a null in the passband. Both staggered QPSK and QPR modems are studied. Performance is studied for error probability and mean squared error. The general conclusion is reached that a decision feedback equalizer is required for robust performance.     

A Hammerstein-type equalizer for concatenated fiber-wireless uplink

In optical fiber-based wireless access schemes, the radio signal is transmitted through fiber without frequency conversion radio-over-fiber (ROF). Although the fiber has adequate bandwidth, nonlinear distortion due to electrical to optical (E/O) conversion is a concern. In the uplink, the dynamic multipath wireless channel is followed by this static memoryless ROF link; this forms a Wiener system. In this paper, we propose a Hammerstein type decision feedback equalizer (HDFE) for the fiber-wireless uplink to combat the nonlinear distortion and the wireless channel dispersion. The proposed equalizer is less complex because it handles static and dynamic distortions separately. The nonlinear distortion is compensated first, reducing the power of cross modulation products significantly. Analytical results show that the lower bound of the mean squared error depends on the optical and wireless channel noise. The bit error rate (BER) performance of the HDFE for the nonlinear channel approaches the performance of a decision feedback equalizer (DFE) in a linear channel when the nonlinearity is adequately compensated.     

A decision feedback recurrent neural equalizer as an infiniteimpulse response filter

An adaptive decision feedback recurrent neural equalizer (DFRNE), which models a kind of an IIR structure, is proposed. Its performance is compared with the traditional linear and nonlinear equalizers with FIR structures for various communication channels. The small size and high performance of the DFRNE makes it suitable for high-speed channel equalization     

Adaptive multichannel decision feedback equalization for volterra type nonlinear communication channels

A model-based approach for the decision feedback equalization of Volterra type nonlinear communication channels is proposed such that the linear model-based decision feedback equalization can be considered as a special case of the proposed approach. In designing the decision feedback equalizer, the nonlinear decision feedback equalization problem is visualized as a linear, multichannel equalization problem. A complete modified Gram–Schmidt orthogonalization of the input vector is achieved by using modified sequential processing multichannel lattice stages. The elements of the multichannel desired signal vector are then estimated from the new orthogonal set by using only scalar operations. The probability of error performance of the proposed equalizer is improved by the estimation of the elements of the desired signal vector through a sigmoid activation function so that a polynomial perceptron equalizer is realized. The comparative computational complexity calculations and performance results of the proposed decision feedback equalizer are also provided.     

A new adaptive equalizer with channel estimator for mobile radiocommunication

For unknown mobile radio channels with severe intersymbol interference (ISI), a maximum likelihood sequence estimator, such as a decision feedback equalizer (DFE) having both feedforward and feedback filters, needs to handle both precursors and postcursors. Consequently, such an equalizer is too complex to be practical. This paper presents a new reduced-state, soft decision feedback Viterbi equalizer (RSSDFVE) with a channel estimator and predictor. The RSSDFVE uses maximum likelihood sequence estimation (MLSE) to handle the precursors and truncates the overall postcursors with the soft decision of the MLSE to reduce the implementation complexity. A multiray fading channel model with a Doppler frequency shift is used in the simulation. For fast convergence, a channel estimator with fast start-up is proposed. The channel estimator obtains the sampled channel impulse response (CIR) from the training sequence and updates the RSSDFVE during the bursts in order to track changes of the fading channel. Simulation results show the RSSDFVE has nearly the same performance as the MLSE for time-invariant multipath fading channels and better performance than the DFE for time-variant multipath fading channels with less implementation complexity than the MLSE. The fast start-up (FS) channel estimator gives faster convergence than a Kalman channel estimator. The proposed RSSDFVE retains the MLSE structure to obtain good performance and only uses soft decisions to subtract the postcursor interference. It provides the best tradeoff between complexity and performance of any Viterbi equalizers     

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     

短波高速QAM信号解调算法的研究

文中针对短波高速QAM信号,提出并实现了一种全数字的解调方案。采用了最大平均功率定时同步算法和最大似然载波相位估计算法。利用同步导频使信号的眼图张开,进而对数据段进行信道均衡与相位补偿。采用判决反馈模式的非线性均衡器,更好地补偿了信道畸变。这种前向式解调结构减小了算法的复杂性,易于DSP实现。Matlab仿真结果表明该方案有效可行。     

非线性信道自适应均衡的一种方法

本文通过对非线性信道模型的分析,构造了一种新的、关联模型的自适应均衡器。分析表明,与一般的基于Volterra级数的非线性扩展关联模型相比,新结构的权数大为减少。模拟实验证实,当信道噪声较小时,新结构以较高的精度收敛于最佳解。而且无论对最小相位或非最小相位信道,该均衡器均表现出良好的收敛特性和误码性能。     

An adaptive RAM-DFE for storage channels

A modification of the decision feedback equalizer (DFE), RAM-DFE, is presented and analyzed for use in channels with trailing nonlinear intersymbol interference, especially binary saturation-recording channels. In the RAM-DFE, a look-up table, which can be easily implemented with random access memory, (RAM), replaces the transversal filter feedback section of the DFE. The feedforward section of the equalizer remains linear. A general nonlinear Markov (or finite-state machine) model is used to model the nonlinear intersymbol interference (ISI) channel. With this Markov model, a method is introduced for computing the minimum-mean-squared-error settings of the feedforward filter coefficients and the feedback filter and look-up table contents for the linear DFE and the RAM-DFE, respectively. RAM-DFE with these settings can be significantly better than the linear DFE for channels with trailing nonlinear ISI. Globally convergent gradient-type algorithms for updating the feedforward section coefficients and the contents of the feedback table are introduced and analyzed. Results based on data taken from disk storage units are discussed