In communication systems, data are often corrupted during transmission due to imperfect communication channels. Since channel characteristics may be time varying or unknown prior to data transmission, adaptive equaliz...
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In communication systems, data are often corrupted during transmission due to imperfect communication channels. Since channel characteristics may be time varying or unknown prior to data transmission, adaptive equalizers are typically incorporated into the receiver to reduce the ill effects of the imperfect channel. Conventional equalizers utilize a training signal to achieve the proper correction. Blind equalization schemes do not require the use of a training signal, but instead attempt restoration based upon some known property of the transmitted signal. The constant modulus algorithm (CMA) is a blind equalization technique that may be used to equalize certain communication signals (e.g. BPSK, QPSK, and FM). This paper investigates three new techniques for adaptive equalization based on the constantmodulus error criterion: (i) the fast quasi-Newton CMA, (ii) the conjugate gradient quasi-Newton CMA, (iii) the conjugate gradient CMA. These three methods are formulated analytically and evaluated experimentally on several typical communication channel models. (C) 1998 John Wiley & Sons, Ltd.
The authors address the problem of fractionally-spaced blind equalisation when there exist common zero(s) among subchannels. They propose a new cascaded fractionally-spaced and baud-spaced structure to tackle this dif...
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The authors address the problem of fractionally-spaced blind equalisation when there exist common zero(s) among subchannels. They propose a new cascaded fractionally-spaced and baud-spaced structure to tackle this difficulty. The recent constant modulus algorithm under soft constraint satisfaction (SCS-CMA) is used to demonstrate the feasibility of the technique. Simulations show that the performance of the proposed scheme is superior to those of both fractionally-spaced and baud-spaced CMAs, when channels have common zero(s).
Deterministic blind beamforming algorithms try to separate superpositions of soul ce signals impinging on a phased antenna array by using deterministic properties of the signals or the channels such as their constant ...
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Deterministic blind beamforming algorithms try to separate superpositions of soul ce signals impinging on a phased antenna array by using deterministic properties of the signals or the channels such as their constantmodulus or directions-of-arrival. Progress in this area has been abundant over the past ten years and has resulted in several powerful algorithms. Unlike optimal or adaptive methods, the algebraic methods discussed in this review act on a fixed block of data and give closed-form expressions for beamformers by focusing on algebraic structures. This typically leads to subspace estimation and generalized eigenvalue problems. After introducing a simple and widely used multipath channel model, the paper provides an anthology of properties that are available, as well as generic algorithms that exploit them.
The performance of the constant modulus algorithm (CMA) with the steepest descent method used in an adaptive array of monopole antennas mounted on a rectangular conducting plate was investigated. The mutual coupling (...
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The performance of the constant modulus algorithm (CMA) with the steepest descent method used in an adaptive array of monopole antennas mounted on a rectangular conducting plate was investigated. The mutual coupling (MC) effect among the array elements and the diffraction effect caused by the conducting plate were taken into account in the calculation by a hybrid method of moment method (MRI) and geometrical theory of diffraction (GTD). Simulations showed that the CMA adaptive array performs differently when the MC and the diffraction effects are taken into account. In some cases, the speed of convergence is slower with MC, and in other cases it is faster. Also, in multipath scenarios the array sometimes converges on a weaker delayed ray rather than the direct ray when MC is included. The capture property is explained by the fact that the CMA algorithm is sensitive to initial conditions and the initial array pattern is directional due to MC-not omnidirectional as in the ideal case. The performance of the array on a finite ground plane is different from that on an infinite ground plane due to diffraction effects.
A new approach to fractionally spaced blind equalisation of finite-duration impulse response (FIR) channels is proposed based on the method of least squares (LS) and the constant modulus algorithm (CMA). A nonlinear t...
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A new approach to fractionally spaced blind equalisation of finite-duration impulse response (FIR) channels is proposed based on the method of least squares (LS) and the constant modulus algorithm (CMA). A nonlinear transformation of the equaliser parameters is formulated and solved for, so as to avoid the potential problems associated with fractionally spaced CMA (FS-CMA) when the channel input is correlated. The new algorithm has a fast convergence rate in comparison with FS-CMA and the resulting equaliser setting is invariant to channel input correlation so long as all finite-length subsequences of the channel input sequence occur with nonzero probability. Although the algorithm is based on the assumption of constantmodulus channel inputs, its application to nonconstantmodulus constellations such as M-ary quadrature amplitude modulation (QAM) is also illustrated. The issue of channel noise enhancement is studied in connection with subchannel zeros, and a method for reducing inflated equaliser norms is proposed by way of reduced rank matrix approximation. A modified recursive least squares implementation of the algorithm is simulated to demonstrate its superior performance vis-a-vis FS-CMA. (C) 1997 Elsevier Science B.V.
In this letter, we propose a blind adaptation method for the decision feedback equalizer (DFE). In the proposed scheme, a DFE is divided into two parts: a front-end linear equalizer (LE), and a prediction error filter...
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In this letter, we propose a blind adaptation method for the decision feedback equalizer (DFE). In the proposed scheme, a DFE is divided into two parts: a front-end linear equalizer (LE), and a prediction error filter (PEF) followed by a feedback part. The coefficients of the filters in each part are updated using constant modulus algorithm and decision feedback prediction algorithm, respectively. The front-end LE removes intersymbol interference ISI, and the PEF with feedback part whitens the noise to reduce noise power enhanced by the LE. Pre-processing by the LE enables the DFE to equalize nonminimum phase channels. Simulation results show that the proposed scheme provides reliable convergence, and the resulting symbol error rate is much less than that of the conventional LE and very close to that of the DFE using a training sequence.
A general framework for convergence analysis of finite-dimensional blind adaptation algorithms of Bussgang type is presented. The approach allows discrete symbol sets and can be used for analysis of systems with both ...
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A general framework for convergence analysis of finite-dimensional blind adaptation algorithms of Bussgang type is presented. The approach allows discrete symbol sets and can be used for analysis of systems with both poles and zeros. The main tool of analysis is an associated differential equation whose stability properties are proved to be tied to the convergence properties of a general blind stochastic approximation algorithm. The recently highlighted ill-convergence problem of for example the constant modulus algorithm (CMA) is then addressed. The problem is partially solved using new blind adaptation algorithms which are not derived by criterion minimization. Instead, the averaged updating directions of the suggested stochastic gradient schemes are designed to guarantee global stability of the associated differential equations. (C) 1997 Elsevier Science B.V.
In this contribution, we address the comparison of Subspace (SS), Linear Prediction (LP) and constantmodulus (CM) identificaton/equalization algorithms in terms of robustness to loss of Fractionally-Spaced channel di...
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ISBN:
(纸本)0818679204
In this contribution, we address the comparison of Subspace (SS), Linear Prediction (LP) and constantmodulus (CM) identificaton/equalization algorithms in terms of robustness to loss of Fractionally-Spaced channel disparity. We show that SS procedure leads to an inconsistent channel estimation. Investigating a left-inverse channel estimation, we show that LP results in the estimation of the so-called minimum-phase multivariate channel factorization. We show that CM criterion still perform reasonable channel estimation, even if proper algorithm initialization is still a critical subject.
An exact block formulation of the constant modulus algorithm (CMA) is presented, in which a reduction of arithmetic complexity is achieved. Two types of fast algorithms are explained, either in time-domain or in frequ...
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An exact block formulation of the constant modulus algorithm (CMA) is presented, in which a reduction of arithmetic complexity is achieved. Two types of fast algorithms are explained, either in time-domain or in frequency-domain. The first one is of greater interest for small block lengths and the second one, using the FFT as an intermediate step, has greater advantage for large blocks. Due to the equivalence between the original CMA formulation and this one, the convergence properties of the CMA are maintained, which is not the case in the Treichler et al. implementation in frequency domain of this algorithm. Furthermore, this approach allows the use of very small block lengths (e.g. N = 2), the reduction of the arithmetic complexity increasing with the block size.
The constant modulus algorithm (CMA) updates its weight vector to minimize the modulus variation of the output signal. In this paper, the convergence behavior of the CMA used in interference cancellation application i...
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The constant modulus algorithm (CMA) updates its weight vector to minimize the modulus variation of the output signal. In this paper, the convergence behavior of the CMA used in interference cancellation application is studied. We first investigate the optimum weight vector that minimizes the performance index of the CMA which is defined as the mean-squared difference between the estimated and true moduli. We then analyze the convergence behavior of the squared output modulus and the performance index. Based on these analysis results, several convergence properties of the CMA are discussed.
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