Adaptive H∞ channel equalization for wirelesspersonal communications

In this paper, a new adaptive H_∞ filtering algorithm is developed to recursively update the tap-coefficient vector of a decision feedback equalizer (DFE) in order to adaptively equalize the time-variant dispersive fading channel of a high-rate indoor wireless personal communication system. Different from conventional L ₂ (such as the recursive least squares (RLS)) filtering algorithms which minimize the squared equalization error, the adaptive H _∞ filtering algorithm is a worst case optimization. It minimizes the effect of the worst disturbances (including input noise and modeling error) on the equalization error. Hence, the DFE with the adaptive H_∞ filtering algorithm is more robust to the disturbances than that with the RLS algorithm. Computer simulation demonstrates that better transmission performance can be achieved using the adaptive H_∞ algorithm when the received signal-to-noise ratio (SNR) is larger than 20 dB     

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。     

基于遗传算法的RLS自适应算法研究

在通信系统中采用信道均衡技术是改善信道特性行之有效的方法,但研究算法的同时,往往需要通过大量的仿真实验取平均值来选取最优的参数值,本文首先利用MATLAB仿真软件对线性调制下RLS自适应算法进行仿真分析,然后引入遗传算法的寻优特性及其优点,对RLS最佳遗忘因子λ的选取进行了寻优,得出最佳遗忘因子λ的取值,提供了参数选择的一条捷径,最后通过对比最优λ与参照λ,计算RLS算法均衡已知信号的均方误差值,证明了该方法的可行性。     

Diversity Combining and Equalization with TCM for Wireless Communications

This paper presents an equalization structure in which antennadiversity, adaptive decision feedback equalization (DFE), interleavingand trellis-coded modulation (TCM) can be effectively combined to combatboth ISI and cochannel interference in cellular mobile radioenvironments. The feedback filter of the DFE can use either tentative orfinal decision symbols of the TCM Viterbi decoding to cancel tail ISIwith the square root Kalman algorithm. A performance bound on theaverage pairwise error probability for TCM under perfect interleavingand equalization is obtained by analysis. Some simulation results whichillustrate the potential of the proposed system will also be given. Inparticular, a performance comparison between the proposed method anduncoded QPSK modulation will be undertaken.     

MAP selection-diversity DFE for indoor wireless data communications

Indoor high-speed wireless data networks encounter signal fading and delay-spread multipath propagation. Hence, the realization of low error rate transmission requires measures to combat the performance degradation due to both signal fading and intersymbol interference (ISI). Receiver diversity has been known to be an efficient way of coping with the former problem, while adaptive equalization could be used to mitigate the effects of the latter. Incorporation of receiver diversity with adaptive equalization is therefore desirable. We propose a novel selection-diversity approach with an adaptive decision-feedback equalizer (DFE). In this method, selection is done on a symbol-by-symbol basis such that the output of the branch with the lowest estimated a posteriori probability of error is used as the final decision. This final (and hence more reliable) decision is used to adapt the DFE for all diversity branches. It is shown in this paper that the proposed selection rule is optimal for selection-diversity in the maximum a posteriori probability (MAP) sense. A very simple selection metric can be derived from this selection rule and practical ways of computing the selection metric are also presented. Simulation results show that the proposed method is very efficient. It is capable of achieving almost the same performance as an optimal [least squares (LS)], but computationally intensive, combining diversity approach. Furthermore, at an average bit error rate (BER) of 10^-4, a gain of approximately 1.25 dB can be achieved over a previously proposed selection-diversity equalization approach     

MIMO DFE equalization for multitone DS/SS systems over multipathchannels

The combination of multitone modulation with direct sequence spectrum spreading (DS/SS) has been introduced in the past. The performance of a correlation receiver has been evaluated for a multipath channel and in the presence of an additional multiple access interference. We analyze the problem of decision feedback equalization (DFE) for such a system. In order to understand the potential of the system with equalization, we first study the steady-state behavior of the equalizer for a minimum mean square error (MMSE) criterion. The investigation is carried out for a receiver made of a bank of filters matched to both the symbol shape and the channel, and for a two path channel. Assuming transmission of binary phase shift keying (BPSK) symbols, an exact expression of the bit error probability is obtained in the form of an integral. Then adaptive least mean square (LMS) and recursive least square (RLS) structures are derived. The performance of the adaptive RLS algorithm is demonstrated by means of computer simulations     

宽带自适应MIMO-DFE空时接收机

设计了频率选择信道基于RLS算法的自适应判决反馈均衡MIMO-DFE空时接收机,由于这种接收机不需要信道识别,从而降低了接收机的复杂度。通过蒙特卡罗仿真评估了接收机在频率选择信道下的误符号率性能。仿真结果表明,在不加信道编码的情况下,该接收机在信噪比为 14dB时误符号率达到 10-3以下。     

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     

BEACON: an adaptive set-membership filtering technique with sparseupdates

This paper deals with adaptive solutions to the so-called set-membership filtering (SMF) problem. The SMF methodology involves designing filters by imposing a deterministic constraint on the output error sequence. A set-membership decision feedback equalizer (SM-DFE) for equalization of a communications channel is derived, and connections with the minimum mean square error (MMSE) DFE are established. Further, an adaptive solution to the general SMF problem via a novel optimal bounding ellipsoid (OBE) algorithm called BEACON is presented. This algorithm features sparse updating, wherein it uses about 5-10% of the data to update the parameter estimates without any loss in mean-squared error performance, in comparison with the conventional recursive least-squares (RLS) algorithm. It is shown that the BEACON algorithm can also be derived as a solution to a certain constrained least-squares problem. Simulation results are presented for various adaptive signal processing examples, including estimation of a real communication channel. Further, it is shown that the algorithm can accurately track fast time variations in a nonstationary environment. This improvement is a result of incorporating an explicit test to check if an update is needed at every time instant as well as an optimal data-dependent assignment to the updating weights whenever an update is required     

Antenna diversity combining and finite-tap decision feedbackequalization for high-speed data transmission

The next-generation wireless communication systems are expected to support high-speed data transmission. Associated with high transmission rates, however, is the problem of multipath intersymbol interference (ISI) due to frequency-selective fading. Decision feedback equalization (DFE) and antenna diversity combining are two practical techniques for combating multipath ISI. Through simulations we investigate the performance of diversity combining, together with DFE, under various numbers of antenna branches and equalization taps, in a quasistationary frequency-selective fading environment with additive white Gaussian noise (AWGN) and cochannel interference (CCI). We consider joint optimization combining and power selection diversity combining. We simulate the combiner, using quaternary phase shift keying (QPSK) modulation with up to four antenna branches. Our results show that using antenna diversity and DFE with joint optimization combining provides performance improvement with lower computational complexity, as compared to that of using either DFE or diversity combining alone for combating ISI     

ADAPTIVE EQUALIZATION WITH RLS-MLSE ASSISTED BY FEC BLOCK CODING FOR FADING MOBILE RADIO CHANNELS

In this paper, we propose an adaptive maximum-likelihood (ML) sequence estimator with RLS channel estimation, which is assisted by forward error control (FEC) coding. The reliable symbols reconstructed in the FEC decoder are used as the feedback signal to the RLS channel estimator. The scheme is compared with decision feedback equalization (DFE) with RLS algorithm, which is assisted by FEC coding. Computer simulations show that in frequency-selective fast fading mobile radio channels, the proposed scheme performs better at moderate Doppler frequencies. It is suitable for four-phase modulation data transmission at the rate of several 10 kb/s in 900 MHz band or in the 1800 MHz band.     

Adaptive step-size algorithms for blind interference suppression inDS/CDMA systems

This paper develops adaptive step-size blind LMS algorithms and adaptive forgetting factor blind RLS algorithms for code-aided suppression of multiple access interference (MAI) and narrowband interference (NBI) in DS/CDMA systems. These algorithms optimally adapt both the step size (forgetting factor) and the weight vector of the blind linear multiuser detector using the received measurements. Simulations are provided to compare the proposed algorithms with previously studied blind RLS and blind LMS algorithms. They show that the adaptive step-size blind LMS algorithm and adaptive forgetting factor blind RLS algorithm field significant improvements over the standard blind LMS algorithm and blind RLS algorithm in dynamic environments where the number of interferers are time-varying     

一种新的用于UWB RAKE接收机的自适应算法:快速横向滤波算法

本文首次提出将快速横向滤波(FTF)算法引入超宽带(UWB)通信系统的接收机结构中.通过引入遗忘因子对角矩阵,推导了带有遗忘因子的FTF滤波器的递推算法.FTF算法可以自适应地跟踪接收机输入信号的幅度衰减,做出实时地估计.仿真实验表明:FTF算法在运算量、收敛速度和误码率等性能上要优于常用的RLS算法,尤其FTF算法的收敛速度对数据的相关性不敏感,比RLS算法更具有吸引力.     

Behavior of the partial correlation coefficients of a least squareslattice filter in the presence of a nonstationary chirp input

This paper studies the performance of the a posteriori recursive least squares lattice filter in the presence of a nonstationary chirp signal. The forward and backward partial correlation (PARCOR) coefficients for a Wiener-Hopf optimal filter are shown to be complex conjugates for the general case of a nonstationary input with constant power. Such an optimal filter is compared to a minimum mean square error based least squares lattice adaptive filter. Expressions are found for the behavior of the first stage of the adaptive filter based on the least squares algorithm. For the general nth stage, the PARCOR coefficients of the previous stages are assumed to have attained Wiener-Hopf optimal steady state. The PARCOR coefficients of such a least squares adaptive filter are compared with the optimal coefficients for such a nonstationary input. The optimal lattice fitter is seen to track a chirp input without any error, and the tracking lag in such an adaptive filter is due to the least squares update procedure. The expression for the least squares based PARCOR coefficients are found to contain two terms: a decaying convergence term due to the weighted estimation procedure and a tracking component that asymptotically approaches the optimal coefficient value. The rate of convergence is seen to depend inversely on the forgetting factor. The tracking lag of the filter is derived as a function of the rate of nonstationarity and the forgetting factor. It is shown that for a given chirp rate there is a threshold adaptation constant below which the total tracking error is negligible. For forgetting factors above this threshold, the error increases nonlinearly. Further, this threshold forgetting factor decreases with increasing chirp rate. Simulations are presented to validate the analysis     

Error-rate evaluation of linear equalization and decision feedbackequalization with error propagation

We propose applying an approximate Fourier series to evaluate efficiently the bit-error-rate (BER) performance of finite-length linear equalization (LE) and decision feedback equalization (DFE). By extending the Fourier series, we enable BER calculations for quadrature phase-shift keying (QPSK) transmission on complex channels with in-phase and crosstalk intersymbol interference (ISI). The BER calculation is based on determining the residual ISI samples and background Gaussian noise variance at the equalizer output for static channels or for realizations of quasi-static fading channels. A simple bound on the series error magnitude in terms of the Fourier series parameters ensures the required accuracy and precision. Improved state transition probability estimates are derived and verified by simulation for an approximate Markov model of the DFE error propagation for the case in which residual ISI exists even when the previous decisions stored in the feedback filter (FBF) are correct. We demonstrate the ease and widespread applicability of our approach by producing results which elucidate a variety of equalization tradeoffs. Our analysis includes symbol-spaced and fractionally spaced minimum mean-square error (MMSE)-LE, zero-forcing (ZF)-LE, and MMSE-DFE (with and without error propagation) on static ISI channels and multipath channels with quasi-static Rayleigh fading; a comparison between suboptimum and optimum receiver filtering in conjunction with equalization; and an assessment of the accuracy of some widely used equalization BER approximations and bounds     

IEEE 802.16单载波调制系统的信道均衡技术

简要介绍了固定宽带无线接入标准IEEE 802.16以及一种用于信道均衡的自适应算法——指数加权RLS,对判决导引信道均衡技术的原理进行了具体描述。最后分别就两种自适应的均衡算法（LMS、RLS）,结合一种具体IEEE 802.16单载波调制系统推荐测试信道进行了仿真,得出RLS算法优于LMS的结论。     

Adaptive Bayesian decision feedback equalizer for dispersive mobileradio channels

The paper investigates adaptive equalization of time-dispersive mobile radio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE     

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.     

RLS算法及其改进形式在信号分离中的应用分析

本文着重研究了自适应滤波器的重要实现形式——递推最小二乘算法（RLS）的原理,分析了RLS算法在应用中的优点及存在问题。为解决RLS算法收敛速度和稳态误差的矛盾及系统在趋于平稳时跟踪效果差的问题,本文从实现可变遗忘因子和增加自扰动项两个方面介绍了RLS算法的几种改进方法。并将它们应用于复杂电磁环境、强干扰背景下的信号分离中去。通过仿真实验,对RLS算法及其两种改进方法在信号分离中的效果进行了比较,得出可变遗忘因子RLS算法在收敛速度和分离信号的准确性上都具有较好的性能。     

一种基于无偏协方差估计RLS的自适应阵列天线抗干扰算法

RLS是自适应阵列天线抗干扰的主要算法之一。为提高RLS算法对遗忘因子选择健壮性,避免因遗忘因子选择不当所造成的算法不收敛问题,本文针对自适应阵列天线的多路接收信号,基于其无偏协方差矩阵模型,推导设计出了一种新的RLS算法,相比于常规RLS,在该算法中遗忘因子可以更加精确地控制RLS迭代过程项,降低因遗忘因子设置不当而造成的算法不收敛风险。通过仿真验证了算法的有效性。