Direct multiple-channel blind equalization

In this paper, the problem of blind channel identification and equalization is reviewed, and some recent results are presented.     

两种改进的盲均衡算法

文献[1]提出了一种适用于PAM、QAM通信系统的盲均衡算法--MMA,该算法虽然克服了CMA收敛后剩余误差大和stop-and-go等算法有待定参数的缺点,但是它的收敛速度比较慢,改进算法1对MMA进行了改进,加快了算法收敛的速度.改进算法2是一种联合信道盲均衡和相位恢复算法--MCMA[2]的改进算法,改进后的算法比MCMA具有更小的剩余误差,同时在迭代过程中能够提供更准确的相位信息,来补偿信道引起的相位误差,从而更加有利于判决器进行准确的判决.两种改进算法都大大减少了使系统误符号率降为0所需的迭代次数.计算机仿真结果表明,这两种算法具有良好的均衡特性.     

DVB-C中盲均衡算法的收敛性

介绍了应用于DVB—C标准的QAM接收机中均衡器的盲均衡算法Stop—Go，RCA和Godard算法，分析了各种算法的收敛性，提出了避免系数收敛到局部收敛点的方法。     

An enhancement to blind equalization algorithms

Blind equalization performance can be improved by controlling the equalizer adaption step-size according to the region in which the received signal lies in the constellation plane. A simple technique based on this principle is proposed as a modification to blind equalization algorithms, and its effectiveness is demonstrated by simulation. As an example, the algorithm of Picchi and Prati (1987) is considered since it is simpler and converges faster. The proposed modification is applied to the zero-forcing (ZF) algorithm     

Dual-mode type algorithms for blind equalization

Adaptive channel equalization accomplished without resorting to a training sequence is known as blind equalization. The Godard algorithm and the generalized Sato algorithm are two widely referenced algorithms for blind equalization of a QAM system. These algorithms exhibit very slow convergence rates when compared to algorithms employed in conventional data-aided equalization schemes. In order to speed up the convergence process, these algorithms may be switched over to a decision-directed equalization scheme once the error level is reasonably low. The authors present a scheme which is capable of operating in two modes: blind equalization mode and a mode similar to the decision-directed equalization mode. In this proposed scheme, the dominant mode of operation changes from the blind equalization mode at higher error levels to the mode similar to the decision-directed equalization mode at lower error levels. Manual switch-over to the decision-directed mode from the blind equalization mode, or vice-versa, is not necessary since transitions between the two modes take place smoothly and automatically     

Single-channel blind equalization for GSM cellular systems

This paper focuses on the study of blind equalization global system for mobile communications (GSM) systems using a single antenna. In order to utilize the well-known linear system model in conventional studies of blind equalization, an equivalent baseband quadrature amplitude modulation (QAM) approximation is used for the nonlinear GMSK signal in GSM systems. Since the GMSK signal in GSM has very little excess bandwidth to warrant oversampling, a derotation scheme is developed to create two subchannels for each received GMSK signal sampled at the baud rate. Linear approximation of the GMSK signal makes the traditional QAM blind equalization system model applicable for GSM. Derotation induces channel diversity without an additional antenna and reduces the number of necessary radio frequency (RF) receivers (sensors) without increasing hardware or computational costs. Several second-order statistical and higher order statistical methods of blind equalization are adopted for GSM signals     

Two-stage interference-resistant adaptive periodically time-varyingCMA blind equalization

We consider the problem of blindly equalizing a digital communication signal distorted by a linear time-invariant channel and contaminated by severe co-channel or adjacent-channel digital interference under the assumption that the latter exhibits a different symbol rate from the desired signal. The proposed equalizer is composed of two stages that are both periodically time-varying (PTV) in order to better match the periodical statistics of the received signal. The first stage employs linear PTV filtering to mitigate interference, thus allowing the second stage, based on the constant modulus algorithm (CMA), to reliably recover the transmitted information symbols. Computer simulations confirm the effectiveness of the new approach, and comparisons with existing blind methods show that a significant performance gain can be attained     

一种适用于FPGA的实现的盲均衡算法

本文提出一种适用于PAM和QAM调制信号的主副抽头分别调整变步长的盲均衡算法,并使用Altera公司的FPGA器件实现,理论分析和计算机模拟表明此盲均衡算法的收敛性能及误码性能均优于CMA算法,是一种很实用的均衡算法。     

Constrained optimization methods for direct blind equalization

Constrained optimization techniques are studied for direct design of linear multichannel equalizers. Novel blind algorithms are derived by minimizing the equalizer's output variance subject to appropriate constraints. The constraints are chosen to guarantee no desired signal cancellation, and their parameters are jointly optimized to maximize the signal component at the output. The resulting blind algorithm was observed to have near optimal performance at high signal-to-noise ratio, i.e., close to the performance of the trained minimum mean-square-error receiver. Also, the proposed method is not sensitive to the color of the transmitted sequence. Analytical expressions are derived to quantify the algorithm's performance     

Least squares approach to blind channel equalization

We present a least squares (LS) algorithm for blind channel equalization based on a reformulation of the Godard algorithm. A transformation for the equalizer parameters is considered to convert the nonlinear LS problem inherent in the Godard algorithm to a linear LS problem. Unlike the Godard (1980) algorithm, the proposed LS approach does not suffer from ill-convergence to closed-eye local minima. Methods for extracting the equalizer parameters from their transformed version are developed. Offline and recursive implementations of the LS algorithm are presented. The algorithm requires only a small number of channel output observations to estimate the equalizer parameters and is therefore fast vis-a-vis the Godard algorithm. The channel input correlation does not impose any restriction on the application of the algorithm, as long as a weak sufficient-excitation condition is satisfied. Simulation examples are presented to demonstrate the LS approach and to compare it with the Godard algorithm     

Combined multiuser signal classification and blind equalization

A multiuser automatic modulation classifier (MAMC) is an important signal processing component of a multiantenna cognitive radio (CR) receiver that helps the radio in identifying modulation format employed by multiple users in a frequency band simultaneously. In a typical wireless communication, transmitted signals are subjected to multipath fading and interference from other users. Multipath fading not only affects symbol detection performance but also affects the performance of the automatic modulation classifier. A multi input multi output (MIMO) blind equalizer is another important component of a multiantenna CR receiver that improves symbol detection performance by reducing inter symbol interference and inter user interference. In a CR scenario it is preferable to consider the performance of the AMC also while adapting the parameters of the blind equalizer. In this paper we propose MIMO blind equalizers that improves the performance of both multiuser symbol detection and cumulants based MAMC. Computer simulations are provided to illustrate this concept and the proposed algorithm.     

Unbiased blind adaptive channel identification and equalization

The blind adaptive equalization and identification of communication channels is a problem of important current theoretical and practical concerns. Previously proposed solutions for this problem exploit the diversity induced by sensor arrays or time oversampling, leading to the so-called second-order algebraic/statistical techniques. The prediction error method is one of them, perhaps the most appealing in practice, due to its inherent robustness to ill-defined channel lengths as well as for its simple adaptive implementation. Unfortunately, the performance of prediction error methods is known to be severely limited in noisy environments, which calls for the development of noise (bias) removal techniques. We present a low-cost algorithm that solves this problem and allows the adaptive estimation of unbiased linear predictors in additive noise with arbitrary autocorrelation. This algorithm does not require the knowledge of the noise variance and relies on a new constrained prediction cost function. The technique can be applied in other noisy prediction problems. Global convergence is established analytically. The performance of the denoising technique is evaluated over GSM test channels     

Channel estimation standard and adaptive blind equalization

Presents a novel concept of channel estimation standard (CES) and applies a new CES error criterion to the process of an adaptive blind equalization. It is shown that the establishment of the CES contributes to the development of a practical communication scheme for approaching to the capacity of a high SNR band-limited channel without using a preamble signal training     

Fractionally spaced blind equalization using polyperiodic linearfiltering

Performance of fractionally spaced equalizers using polyperiodic linear filtering is depicted. The fractionally sampled version of an arbitrary stable channel's outputs with stationary random inputs exhibit cyclostationarity with cycle frequencies being integer multiples of the reciprocal of the oversampling factor. By optimally polyperiodic linear filtering the oversampled outputs, we can fully utilize the cyclostationary property. With some traditional Bussgang techniques, such as stop-and-go decision-directed method, to adaptively update the filter coefficients, the convergence rate of the fractionally spaced equalizers proposed is improved, while the complexity retains in the same order as original Bussgang ones. Some computer simulation results are shown to demonstrate the improvement we can achieve     

Properties of some blind equalization criteria in noisy multiuserenvironments

Blind equalization in noisy multiuser channels has met with increasing attention with the advent of multiaccess digital communication systems. We examine blind equalizer performance in cases where perfect equalization proves unattainable due to noise and interference from concurrent users. In particular, we obtain a characterization of stationary points and extrema for a family of blind criteria in "undermodeled" cases, which assimilates the influence of differing source statistics and background noise correlation properties; relations to mean-square equalization measures are then obtained as a byproduct. By re-examining a gradient search procedure, we obtain domains of attraction of each extremum in a special "sufficient order" setting. We also derive a global step-size bound for undermodeled cases, which ensures convergence of a gradient search procedure to an extremum of a blind cost function. We likewise confirm that the super-exponential algorithm results from an optimal choice of this step-size parameter     

几种盲均衡算法的比较及仿真研究

以新兴的盲均衡技术为理论基础,一些盲均衡算法相继提出。本文以高阶的QAM信号作为输入信号,针对常模算法、多模算法、加权多模算法存在的缺陷,最终引入一种性能优越的加入动量项的加权多模算法。通过计算机的仿真实验首次对这些算法进行依次比较,所得实验结果表明加入动量项的加权多模盲均衡算法在信道均衡上的性能明显优于前面几种算法,它具有更快的收敛速度和更小的稳态误差,因此具有实用价值。     

Convergence analysis of blind equalization algorithms using constellation-matching

Two modified blind equalization algorithms are analyzed for performance. These algorithms add a constellationmatched error term to the cost functions of the generalized Sato and multimodulus algorithms. The dynamic convergence behavior and steady-state performance of these algorithms, and of a related version of the constant modulus algorithm, are characterized. The analysis establishes the improved performance of the proposed algorithms.     

Adaptive blind equalization using second- and higher orderstatistics

This paper presents two classes of adaptive blind algorithms based on second- and higher order statistics. The first class contains fast recursive algorithms whose cost functions involve second and third- or fourth-order cumulants. These algorithms are stochastic gradient-based but have structures similar to the fast transversal filters (FTF) algorithms. The second class is composed of two stages: the first stage uses a gradient adaptive lattice (GAL) while the second stage employs a higher order-cumulant (HOC) based least mean squares (LMS) filter. The computational loads for these algorithms are all linearly proportional to the number of taps used. Furthermore, the second class, as various numerical examples indicate, yields very fast convergence rates and low steady state mean square errors (MSE) and intersymbol interference (ISI). MSE convergence analyses for the proposed algorithms are also provided and compared with simulation results     

A fast linear programming algorithm for blind equalization

A fast implementation of a special non-MSE cost function for blind equalization is presented here. This baud-rate equalization algorithm is based on a convex cost function coupled with a simple linear constraint on the equalizer parameters. For a generic class of channels with persistently exciting quadrature amplitude modulation input signals, this new algorithm allows the convergence of equalizer parameters to a unique global minimum achieving intersymbol interference suppression and carrier phase recovery     

A soft decision-directed LMS algorithm for blind equalization

An adaptation algorithm for equalizers operating on very distorted channels is presented. The algorithm is based on the idea of adjusting the equalizer tap gains to maximize the likelihood that the equalizer outputs would be generated by a mixture of two Gaussians with known means. The decision-directed least-mean-square algorithm is shown to be an approximation to maximizing the likelihood that the equalizer outputs come from such an independently and identically distributed source. The algorithm is developed in the context of a binary pulse-amplitude-modulation channel, and simulations demonstrate that the algorithm converges in channels for which the decision-directed LMS algorithms does not converge