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Blind adaptive identification and equalization using bias-compensated NLMS methods

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Science China(Information Sciences) 2022年第5期65卷 184-195页

作者： Zhen ZHANG Lijuan JIA Ran TAO Yue WANG School of Information and Electronics Beijing Institute of Technology

In this paper, two new blind adaptive identification and equalization algorithms based on second-order statistics are proposed. We consider a practical case where the noise statistics of each transmission channel is unknown. Resorting to the technique of antennas array, a single-input double-output channel can be obtained. We further convert the problem of blind identification into an errors-in-variables(EIV)parameter estimation problem, then we apply the normalized least-mean squares(NLMS) algorithms to tackle the problem. To improve the performance of the NLMS algorithms, we also develop a variable stepsize NLMS(VSS-NLMS) algorithm that ensures the stability of the algorithm and faster convergence speed at the beginning of the iterations process. Under various practical scenarios, noise affects transmission channels;it is necessary to estimate the variance and remove the bias. By modifying the cost function, we present a bias-compensated NLMS(BC-NLMS) algorithm and a bias-compensated NLMS algorithm with variable step-size(BC-VSS-NLMS) to eliminate the bias. The proposed algorithms estimate the variances of the noise online, and therefore, the noise-induced bias can be removed. The estimate of the channel characteristics is available for equalization. Simulation results are presented to demonstrate the performance of the proposed algorithms.

关键词： blind adaptive identification equalization normalized least mean squares algorithm bias compensation errors-in-variables

Distributed incremental bias-compensated RLS estimation over multi-agent networks

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Science China(Information Sciences) 2017年第3期60卷 153-167页

作者： Jian LOU Lijuan JIA Ran TAO Yue WANG School of Information and Electronics Beijing Institute of Technology

In this paper, we study the problem of distributed bias-compensated recursive least-squares(BCRLS) estimation over multi-agent networks, where the agents collaborate to estimate a common parameter of interest. We consider the situation where both input and output of each agent are corrupted by unknown additive noise. Under this condition, traditional recursive least-squares(RLS) estimator is biased, and the bias is induced by the input noise variance. When the input noise variance is available, the effect of the noise-induced bias can be removed at the expense of an increase in estimation variance. Fortunately, it has been illustrated that distributed collaboration between agents can effectively reduce the variance and can improve the stability of the estimator. Therefore, a distributed incremental BC-RLS algorithm and its simplified version are proposed in this paper. The proposed algorithms can collaboratively obtain the estimates of the unknown input noise variance and remove the effect of the noise-induced bias. Then consistent estimation of the unknown parameter can be achieved in an incremental fashion. Simulation results show that the incremental BC-RLS solutions outperform existing solutions in some enlightening ways.

关键词： distributed parameter estimation bias compensation incremental schemes recursive least-squares multi-agent networks

Forward/backward prediction solution for adaptive noisy FIR filtering

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Science in China(Series F) 2009年第6期52卷 1007-1014页

作者： JIA LiJuan TAO Ran WANG Yue WADA Kiyoshi Department of Electronic Engineering Beijing Institute of Technology Beijing 100081 China Department of Electrical and Electronic System Engineering Kyushu University Fukuoka Japan

An important and hard problem in signal processing is the estimation of parameters in the presence of observation noise.In this paper, adaptive finite impulse response （FIR） filtering with noisy input-output data is considered and two developed bias compensation least squares （BCLS） methods are proposed.By introducing two auxiliary estimators, the forward output predictor and the backward output predictor are constructed respectively.By exploiting the statistical properties of the cross-correlation function between the least squares （LS） error and the forward/backward prediction error, the estimate of the input noise variance is obtained; the effect of the bias can thereafter be removed.Simulation results are presented to illustrate the good performances of the proposed algorithms.

关键词： adaptive FIR filtering recursive least squares algorithm bias compensation forward prediction backward prediction

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Adaptive Filtering of EIV-FIR System by Solving Eigenvalue Problems

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Adaptive Filtering of EIV-FIR System by Solving Eigenvalue P...