针对判决反馈均衡器的部分IIR提前结构及其应用

给出了一种可以用于高速数字接收的特殊的判决反馈均衡器结构.为减少FIR内多径传播影响到ⅡR内的多径响应,而将部分ⅡR提前于FIR,以得到更快的系数收敛速度.在此基础上的数据仿真,比较了提前结构同普通结构的性能差异,验证了该结构可以使均衡器在严重畸变的信道条件下得到更快的收敛速度.最后,介绍了提前结构在最新的高清晰度电视8VSB接收机中的应用.     

用Laguerre滤波器实现多径衰落信道自适应均衡

提出了一种衰落信道自适应均衡的新方法。该方法基于Laguerre滤波器结构,采用最小二乘估计估算滤波器极点,通过RLS算法实现自适应过程。仿真结果表明,由于Laguerre滤波器同时具有FIR和ⅡR结构的特点,在信噪比低、信道多径条件复杂的情况下,可以获得比通常的线性自适应均衡器和决策反馈均衡器更好的抗符号间干扰的效果;同时,Laguerre滤波器结构的稳定性有效地减少了差错传播的发生。     

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.     

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。     

判决反馈均衡器结构及LMS算法研究

数字通信系统中多径衰落的无线信道环境通常会使接收信号受到严重的码间干扰。自适应均衡可以校正信道产生的畸变。文中介绍了基于LMS算法的自适应均衡器的原理和结构,在给定的信道模型下利用MAT- LAB工具对其收敛速度和精度进行仿真,结果表明在无线通信中,判决反馈均衡器比线性均衡器有更好的效果。     

基于小波神经网络的自适应均衡器的研究

提出了基于小波神经网络的通信信道自适应均衡器 ,给出了这种均衡器的结构和训练算法。理论分析和计算机仿真均表明 ,与线性均衡器相比 ,基于小波神经网络的均衡器具有更快的收敛速度 ,是一种前景广阔的均衡器。     

一种基于子带分解技术的宽带盲均衡器结构和算法

高速宽带无线通信中,多径传输信道可能导致几百个符号间的相互干扰,这使得接收端的线性盲均衡器的收敛速度极其缓慢。基于子带分解技术,该文提出了一种适合于高速宽带无线传输的盲均衡器结构及算法。该结构将子带分解技术和全频带的子卷积方法有机结合在一起,明显地加快了高速宽带传输条件下线性盲均衡器的收敛速度;同时通过对接收数据进行降低速率的并行处理,该结构还能减小运算复杂度,有利于工程的实时实现。仿真实验结果验证了文中提出的结构和算法的有效性。     

Bussgang盲均衡器

文中描述了基带均衡通信系统的信号模型,研究了基于此模型的三个典型的Bussgang均衡器：恒模算法（CMA）,Sato和直接判决（DD）均衡器。给出了与算法相关的性能仿真结果。仿真结果展示了CMA均衡器可以提供更好的BER性能及比Sato和DD均衡器更快的收敛速度。     

预滤波式均衡器的原理和实现

提出了一种新的均衡器——预滤波式均衡器。这种均衡器由预滤波器和白适应均衡器组成。它利用高效的相关估计算法，计算出所有多径的时延和幅度，然后使用多径拆分算法，设置预滤波器的参数，使得接收信号经过预滤波之后，畸变得到了改善，进而可由后面的自适应均衡器将残余的码间干扰完全消除。结果表明，在性能上它比传统均衡器有显著提高，可以补偿幅度为0dB的副径。     

一种基于信道缩短的深衰落稀疏均衡改进算法

针对较低信噪比下的深衰落稀疏多径信道,提出了一种基于信道缩短的自适应稀疏均衡改进算法。该算法采用前置分数间隔信道缩短均衡器与后置自适应稀疏均衡器级联的均衡器结构,其中,首先利用短训练序列设计基于最小均方误差准则的前置均衡器,前置均衡器与稀疏多径信道级联后得到能量集中于较短时间区域且分布稀疏的等效信道,使得原始信道的深衰落畸变得到部分有效补偿;然后采用能实现稀疏信号重构的随机梯度追踪算法调整后置自适应均衡器的抽头系数,后置均衡器用于消除等效信道的剩余符号间干扰。仿真结果表明,与传统的单级分数间隔自适应均衡器相比,该算法具有收敛速度快和运算复杂度低的优点。     

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     

Efficient decision feedback equalization for sparse wireless channels

A new efficient decision feedback equalizer (DFE) appropriate for channels with long and sparse impulse response (IR) is proposed. Such channels are encountered in many high-speed wireless communications applications. It is shown that, in cases of sparse channels, the feedforward and feedback (FB) filters of the DFE have a particular structure, which can be exploited to derive efficient implementations of the DFE, provided that the time delays of the channel IR multipath components are known. This latter task is accomplished by a novel technique, which estimates the time delays based on the form of the channel input-output cross-correlation sequence in the frequency domain. A distinct feature of the resulting DFE is that the involved FB filter consists of a reduced number of active taps. As a result, it exhibits considerable computational savings, faster convergence, and improved tracking capabilities as compared with the conventional DFE. Note that faster convergence implies that a shorter training sequence is required. Moreover, the new algorithm has a simple form and its steady-state performance is almost identical to that of the conventional DFE.     

Reduced-complexity equalization techniques for broadband wirelesschannels

This paper presents reduced-complexity equalization techniques for broadband wireless communications, both outdoors (fixed or mobile wireless asynchronous transfer mode (ATM) networks) and indoors [high-speed local-area networks (LANs)]. The two basic equalization techniques investigated are decision-feedback equalization (FE) and delayed decision-feedback sequence estimation (DDFSE). We consider the use of these techniques in highly dispersive channels, where the impulse response can last up to 100 symbol periods. The challenge is in minimizing the complexity as well as providing fast equalizer start-up for transmissions of short packets. We propose two techniques which, taken together, provide an answer to this challenge. One is an open-loop timing recovery approach (for both DFE and DDFSE) which can be executed prior to equalization; the other is a modified DFE structure for precanceling postcursors without requiring training of the feedback filter. Simulation results are presented to demonstrate the feasibility of the proposed techniques for both indoor and outdoor multipath channel models. The proposed open-loop timing recovery technique plays a crucial role in maximizing the performance of DFE and DDFSE with short feedforward spans (the feedforward section of DDFSE is a Viterbi sequence estimator). A feedforward span of only five is quite sufficient for channels with symbol rate-delay spread products approaching 100. The modified DFE structure speeds up the training process for these channels by 10-20 times, compared to the conventional structure without postcursor precancellation. The proposed techniques offer the possibility of practical equalization for broadband wireless systems     

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     

Bidirectional iterative ISI canceller for high-rate DSSS/CCK communications

This paper proposes a new RAKE receiver incorporated with a bidirectional iterative intersymbol interference (ISI) canceller in order to reinforce multipath robustness of high-rate direct-sequence spread-spectrum complementary code keying (DSSS/CCK) systems. The proposed RAKE receiver first combines multipath signal components through a channel matched filter (CMF) and removes postcursor-ISI by employing a codeword decision feedback equalizer (DFE). Then, a CCK codeword detector tentatively determines the current CCK codeword symbol and reuses it to subtract precursor-ISI from the previous symbol. Therefore, the ultimate symbol decision is made using the delayed signal with both postcursor-ISI and precursor-ISI cancelled. The detection performance can be more improved through an iterative refinement processing between the postcursor and the precursor components. Simulation results exhibit a significantly improved error rate performance of the proposed receiver compared with that of the legacy RAKE receiver employing only a postcursor DFE. The additional cost for realization of the proposed receiver is one symbol decision delay and reuse complexity of the DFE and the codeword detector.     

On the equivalence of the simultaneous and separate MMSEoptimizations of a DFE FFF and FBF

There is great interest in the use of decision feedback equalization (DFE) to mitigate the effects of intersymbol interference (ISI) on wireless multipath fading channels. The coefficients of a DFE feedforward filter (FFF) and feedback filter (FBF) are usually adjusted based on the minimum mean square error (MMSE) criterion. The equalizer coefficients can be calculated by recursive adaptation or by direct computation based on a channel estimate. The equivalence of the simultaneous and separate MMSE optimization of the FFF and FBF of a finite-length DFE is established     

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     

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     

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.