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     

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     

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。     

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.     

基于最大比特速率准则的FMT频域均衡算法

针对严格采样滤波多音调制(Filtered Multi-Tone modulation,FMT)系统符号间干扰严重的问题,通过推导严格采样FMT系统的矩阵表示式,提出了一种新的频域均衡算法,该算法以最大化比特速率为目标来确定均衡器的系数。仿真结果表明,在ATTC(Advanced Television Technology Center)E型信道条件下,新算法的可达比特率(Achievable Bit Rate,ABR)性能比最小均方误差准则的线性均衡和判决反馈均衡分别提高了40%和70%,误码率性能在BER为10-4则分别改善了3 dB和5 dB;而在WLAN信道条件下,新算法的ABR性能分别提高了45%和75%,误码率性能在BER为10-4则分别改善了6 dB和10 dB。     

Pole-zero decision feedback equalization with a rapidly convergingadaptive IIR algorithm

A decision feedback equalizer (DFE) containing a feedback filter with both poles and zeros is proposed for high-speed digital communications over the subscriber loop. The feedback filter is composed of a relatively short FIR filter that cancels the initial part of the channel impulse response, which may contain rapid variations due to bridge taps, and a pole-zero, or IIR, filter that cancels the smoothly decaying tail of the impulse response. Modifications of an adaptive IIR algorithm, based on the Steiglitz-McBride (1965) identification scheme, are proposed to adapt the feedback filter. A measured subscriber loop impulse response is used to compare the performance of the adaptive pole-zero DFE, assuming a two-pole feedback filter, with a conventional DFE having the same number of coefficients. Results show that the pole-zero DFE offers a significant improvement in mean squared error relative to the conventional DFE. The speed convergence of the adaptive pole-zero DFE is comparable to that of the conventional DFE using the standard least mean square (LMS) adaptive algorithm     

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。     

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.     

Adaptive Lattice Decision-Feedback Equalizers--Their Performance and Application to Time-Variant Multipath Channels

This paper presents two types of adaptive lattice decisionfeedback equalizers (DFE), the least squares (LS) lattice DFE and the gradient lattice DFE. Their performance has been investigated on both time-invariant and time-variant channels through computer simulations and compared to other kinds of equalizers. An analysis of the self-noise and tracking characteristics of the LS DFE and the DFE employing the Widrow-Hoff least mean square adaptive algorithm (LMS DFE) are also given. The analysis and simulation results show that the LS lattice DFE has the faster initial convergence rate, while the gradient lattice DFE is computationally more efficient. The main advantages of the lattice DFE's are their numerical stability, their computational efficiency, the flexibility to change their length, and their excellent capabilities for tracking rapidly time-variant channels.     

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.     

An edge-based dual adaptive decision feedback equalizer for Gbps serial links

This paper presents an adaptive edge-DFE for 2PAM Gbps serial links. The optimal tap coefficients of the DFE are obtained by minimizing the jitter of received data. Reference voltage for generating DFE error signal is also obtained iteratively using an edge-DFE like algorithm. Issues critical to the proposed adaptive edge-DFE are examined in detail. The effectiveness of the proposed adaptive edge-DFE has been validated using a 5 Gbps serial link designed in a 65 nm 1.2 V CMOS technology. The effect of PVT (process, voltage, and temperature) variations on the performance of the proposed DFE has also been investigated. Simulation results demonstrate that the DFE is capable of opening completely closed data eyes when the DFE is absent. Equalized data have 55 % vertical-opening and 86.5 % horizontal eye-opening with 25 ns adaption time.     

BAD: bidirectional arbitrated decision-feedback equalization

The bidirectional arbitrated decision-feedback equalizer (BAD), which has bit-error rate performance between a decision-feedback equalizer (DFE) and maximum a posteriori (MAP) detection, is presented. The computational complexity of the BAD algorithm is linear in the channel length, which is the same as that of the DFE, and significantly lower than the exponential complexity of the MAP detector. While the relative performance of BAD to those of the DFE and the MAP detector depends on the specific channel model, for an error probability of 10/sup -2/, the performance of BAD is typically 1-2 dB better than that of the DFE, and within 1 dB of the performance of MAP detection.     

An LMS-based decision feedback equalizer for IS-136 receivers

In digital mobile communication systems, intersymbol interference is one of the main causes of degrading system performance. Decision feedback equalization (DFE) is the commonly used remedy for this problem. Since the channel is fast-varying, an adaptive algorithm possessing a fast convergence property is then required. The least mean square (LMS) algorithm is well known for its simplicity and robustness; however, its convergence is slow. As a consequence, the LMS algorithm is rarely considered in this application. In this paper, we consider an LMS-based DFE for the North American IS-136 system. We propose an extended multiple-training LMS algorithm accelerating the convergence process. The convergence properties of the multiple-training LMS algorithm are also analyzed. We prove that the multiple-training LMS algorithm can converge regardless of its initial value and derive closed-form expressions for the weight error vector power. We further take advantage of the IS-136 downlink slot format and divide a slot into two subslots. Bidirectional processing is then applied to each individual subslot. The proposed LMS-based DFE has a low computational complexity and is suitable for real-world implementation. Simulations with a 900-MHz carrier show that our algorithm can meet the 3% bit error rate requirement for mobile speeds up to 100 km/hr     

An efficient block adaptive decision feedback equalizer implemented in the frequency domain

In this paper, a new block adaptive decision feedback equalizer (DFE) implemented in the frequency domain is derived. The new algorithm is suitable for applications requiring long adaptive equalizers, as is the case in several high-speed wireless communication systems. The inherent "causality" problem appearing in the block adaptive formulation of the DFE equations is overcome by using tentative decisions in place of the unknown ones within each block. These tentative decisions are subsequently improved by using an efficient iterative procedure, which finally converges to the optimum decisions in a few iterations. This procedure is properly initialized by applying a minimization criterion that utilizes all the available information. The whole algorithm, including the iterative procedure, is implemented in the frequency domain and exhibits a considerable reduction in computational complexity, as compared with the conventional DFE, offering, at the same time, a noticeable increase in convergence speed. Additionally, the level of the steady-state MSE, which is achieved by the new algorithm, is practically insensitive to the block length.     

RLS Algorithm with Variable Fogetting Factor for Decision Feedback Equalizer over Time-Variant Fading Channels

In a high-rate indoor wireless personal communication system, the delay spread due to multipath propagation results in intersymbol interference (ISI) which can significantly increase the transmission bit error rate (BER). Decision feedback equalizer (DFE) is an efficient approach to combating the ISI. Recursive least squares (RLS) algorithm with a constant forgetting factor is often used to update the tap-coefficient vector of the DFE for ISI-free transmission. However, using a constant forgetting factor may not yield the optimal performance in a nonstationary environment. In this paper, an adaptive algorithm is developed to obtain a time-varying forgetting factor. The forgetting factor is used with the RLS algorithm in a DFE for calculating the tap-coefficient vector in order to minimize the squared equalization error due to input noise and due to channel dynamics. The algorithm is derived based on the argument that, for optimal filtering, the equalization errors should be uncorrelated. The adaptive forgetting factor can be obtained based on on-line equalization error measurements. Computer simulation results demonstrate that better transmission performance can be achieved by using the RLS algorithm with the adaptive forgetting factor than that with a constant forgetting factor previously proposed for optimal steady-state performance or a variable forgetting factor for a near deterministic system.     

面向云计算环境的动态公平性度量方法

针对云计算环境资源动态性特点,给出了一种资源分配算法公平性动态度量模型——DFE (dynamic fairness evaluation)。模型引入了公平、效率和时间参数,通过将资源分配结果进行量化处理,定量地描述不同分配算法的公平性。首先考虑了云环境下的2种典型动态因素：1）节点资源需求动态变化;2）虚拟节点动态变化,并分别对这2种场景建立子模型,通过这2种模型导出了公平性动态度量模型DFE。分析了2种子模型以及DFE,通过DFE对几种典型资源分配算法进行度量,验证了在资源需求以及虚拟节点动态变化的情况下,DFE能够有效度量分配算法公平性,为云计算平台资源分配算法的选择提供了重要的参考。     

Reduced complexity decision feedback equalization for digitalsubscriber loops

Decision feedback equalizers (DFEs) are widely used in modern local network digital transmission systems to remove the intersymbol interference caused by slowly decaying pulse tails. A gradient descent algorithm for adapting a coefficient to model the slowly decaying portion of the tail is described. An equalization strategy is described that exploits prior knowledge of the nature of the subscriber loop channel, together with the new adaptation algorithm, to give reduced complexity DFE structures. The use of this algorithm in FIR and IIR equalizer structures is described. The use of this algorithm in FIR and IIR equalizers is quantitatively compared to a conventional DFE in terms of performance and implementation complexity. An analysis is presented describing the operation of the adaptation algorithm in the presence of noise. Simulation results illustrate the training of the algorithm and its stability in the presence of near-end crosstalk noise     

Sector antenna and DFE modems for high speed indoor radiocommunications

This paper examines the outage probabilities of BPSK and BPSK/DFE radio modems with omnidirectional and six-sector antennas. The effects of room size, data rate, transmitted power and sector antenna patterns on the performance of the systems are analyzed. A 2-D ray tracing algorithm is used to model the radio propagation in a typical indoor environment used for wireless LAN applications. The parameters of the radio propagation model are adjusted to fit the results of simulations to the results of empirical measurements with omnidirectional antenna. It is shown that in a line-of-sight (LOS) environment, a six-sector antenna is more effective than a DFE with an omnidirectional antenna at eliminating the effects of multipath. For an obstructed-line-of-sight (OLOS) environment where at least one wall exists between the transmitter and the receiver, the DFE is more effective than the six-sector antenna. It is shown that a BPSK/DFE modem with a six-sector antenna can support data rates close to 20 Mbps     

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     

Iterative equalization with adaptive soft feedback

In this letter, a novel equalization algorithm applying soft-decision feedback and designed for binary transmission is introduced. In contrast to conventional decision-feedback equalization (DFE), iterations are necessary, because a simple matched filter serves as feedforward filter, which collects signal energy, but creates noncausal intersymbol interference. The rule for generating soft decisions is adapted continuously to the current state of the algorithm. In most cases, standard DFE methods are clearly outperformed. For a class of certain channel impulse responses, performance of maximum-likelihood sequence estimation is attained, in principle. The high performance of the scheme is explained using results from neural network theory