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11th IEEE Singapore International Conference on Communication Systems

作者： Srar, Jalal Abdulsayed Chung, Kah-Seng Curtin Univ Technol Dept Elect & Comp Engn Perth WA Australia

ISBN: (纸本)9781424424238

This paper examines the performance of an adaptive linear array employing the new Rlms algorithm, which consists of a recursive least square (RLS) section followed by a least mean square (lms) section. The performance measures used are output and input signal-to-interference plus noise ratios (SINR), side lobe level (SLL), and Delta SINRo, as a function of the direction of arrival of the interfering signal. Computer simulation results show that the performance of Rlms is superior to either the RLS or lms based on these measures, particularly when operating with low input SINR.

关键词： RLS algorithm lms algorithm Rlms algorithm adaptive antenna array beam forming

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FEATURE lms algorithmS

FEATURE LMS ALGORITHMS

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Paulo S. R. Diniz Hamed Yazdanpanah Markus V. S. Lima Universidade Federal do Rio de Janeiro DEE-DEL/Poli & PEE/COPPE/UFRJ P.O. Box 68504 Rio de Janeiro RJ 21941-972 Brazil

ISBN: (纸本)9781538646595

In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations among these coefficients so that some suitable tools are required to reveal the potential sparsity. This work proposes a set of lms-type algorithms, collectively called Feature lms (F-lms) algorithms, setting forth a hidden feature of the unknown parameters, which ultimately would improve convergence speed and steady-state mean-squared error. The key idea is to apply linear transformations, by means of the so-called feature matrices, to reveal the sparsity hidden in the coefficient vector, followed by a sparsity-promoting penalty function to exploit such sparsity. Some F-lms algorithms for lowpass and highpass systems are also introduced by using simple feature matrices that require only trivial operations. Simulation results demonstrate that the proposed F-lms algorithms bring about several performance improvements whenever the hidden sparsity of the parameters is exposed.

关键词： adaptive filtering lms algorithm feature matrix lowpass system highpass system Least mean square algorithm adaptive filters Levamisole learning management system algorithms linear transformations Unknown parameter model parameters

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On the convergence of lms filters under periodic signals

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DIGITAL SIGNAL PROCESSING 2013年第3期23卷 808-816页

作者： Parra, Ignacio E. Hernandez, Wilmar Fernandez, Eduardo Univ Politecn Madrid ETSI Aeronaut E-28040 Madrid Spain Univ Politecn Madrid IDR UPM E-28040 Madrid Spain Univ Politecn Madrid EUIT Telecomunicac E-28040 Madrid Spain

The response of the Least Mean Square (lms) algorithm to deterministic periodic inputs is considered. Under these conditions, initial values of the tap-weight vector can be identified that lead to periodic responses of lms filters. The stability of these periodic responses determines the long-term convergence of the filter. This analysis presents some advantages over the classical studies based on the correlation matrix, because it leads to more accurate results and a better understanding of the filter operation. It is also shown that such an operation does not change essentially for more realistic inputs, as when the desired response is perturbed with a zero-mean random signal. Finally, to validate the obtained results, some simulations and experiments have been conducted for an adaptive noise canceller. (C) 2012 Elsevier Inc. All rights reserved.

关键词： lms algorithm Periodic inputs Stability analysis

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On the design of lms-based channel estimators using the Doppler spread parameter

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DIGITAL SIGNAL PROCESSING 2013年第1期23卷 281-288页

作者： dos Santos, C. C. Galdino, J. F. Pinto, E. L. Inst Mil Engn Dept Elect Engn Rio De Janeiro RJ Brazil

An analysis of the optimization of the lms (Least Mean Square) algorithm for the estimation of time-varying and frequency-selective communication channels is here presented. In contrast to previous works on this subject, in which the step-size optimization is rooted on the assumption of specific channel models, this analysis is much more generic in respect to the modelling of both the Doppler and delay spreadings. Besides, it addresses not only the steady-state performance of channel estimators, but also their transient behaviors. Several useful approximate expressions for designing lms-based channel estimators are herein derived and validated by comparisons with simulation and with a previous work on this kind of analysis. These expressions are remarkably suitable for practical application, since they depend on a few parameters of the communication system. With regard to the channel variability, the knowledge of the Doppler spread parameter is shown to be enough to optimize the performance of a lms channel estimator using the analysis here presented. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Channel estimation lms algorithm Doppler spread Step-size parameter WSS-US channels

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Lossless compression of digital audio using cascaded RLS-lms prediction

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 2003年第6期11卷 532-537页

作者： Yu, RS Ko, CC ASTAR Inst Infocomm Res Singapore 110613 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

This paper proposes a cascaded RLS-lms predictor for lossless audio coding. In this proposed predictor, a high-order lms predictor is employed to model the ample tonal And harmonic components of the audio signal for optimal prediction gain performance. To solve the slow convergence problem of the lms algorithm with colored inputs, a low-order RLS predictor is cascaded prior to the lms predictor to remove the spectral tilt of the audio signal. This cascaded RLS-lms structure effectively mitigates the slow convergence problem of the lms algorithm and provides superior prediction gain performance compared with the conventional lms predictor, resulting in a better overall compression performance.

关键词： adaptive filter linear prediction lms algorithm lossless audio coding RLS algorithm

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A New Variable-Step-Size lms Adaptive Filtering algorithm

A New Variable-Step-Size LMS Adaptive Filtering Algorithm

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9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP'08)

作者： Ningning Tong Hongbing Li Jun Jiang Nannan Liu The Missile institute of Air Force Engineering University The Guilin Air Force Institute Xi'an China713800

A new variable-step-size lms algorithm is proposed,and it performance is *** results indicate that the performance is superior to that of existing VSS algorithm and Nlms *** proposed algorithm is then applied to adaptive noise jamming cancellation system;the computer simulation shows superior performance over the Nlms algorithm and MVSS algorithm.

关键词： lms algorithm convergence rate Variable-step-size Adaptive filter

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Learning curves for lms and regular Gaussian processes

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2002年第2期47卷 284-289页

作者： Hriljac, P Embry Riddle Univ Coll Engn Prescott AZ 86301 USA

This note uses methods due to Guo, Ljung, and Wang to obtain explicit bounds on the error of the lms algorithm used in a linear prediction of a signal using previous values of that signal. The signal is assumed to be a mean-zero Gaussian regular stationary random process. The bounds are then used to construct learning curves for the lms algorithm in situations where the statistics of the process are only partially known.

关键词： lms algorithm prediction tracking performance

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Frequency-response-shaped lms adaptive filter

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DIGITAL SIGNAL PROCESSING 2006年第6期16卷 855-869页

作者： Kukrer, Osman Hocanin, Aykut Eastern Mediterranean Univ Dept Elect & Elect Engn Mersin 10 Turkey

A new lms algorithm is introduced for improved performance when a sinusoidal input signal is corrupted by Correlated noise. The algorithm is based on shaping the frequency response of the transversal filter. This shaping is performed on-line by the inclusion of an additional term similar to the leakage factor in the adaptation equation of leaky lms. This new term, which involves the multiplication of the filter coefficient vector by a matrix, is calculated in an efficient manner using the FFT. The proposed adaptive filter is shown analytically to converge in the mean and mean-square sense. The filter is also analyzed in the steady state in order to show the frequency-response-shaping capability. Simulation results illustrate that the performance of the frequency-response-shaped lms (FRS-lms) algorithm is very effective even for highly correlated noise. (C) 2006 Elsevier Inc. All rights reserved.

关键词： adaptive filter lms algorithm frequency response shaping

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Reweighted lp constraint lms-based adaptive sparse channel estimation for cooperative communication system

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IET COMMUNICATIONS 2020年第9期14卷 1384-1391页

作者： Zhang, Aihua Liu, Pengcheng Ning, Bing Zhou, Qiyu Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China Cardiff Metropolitan Univ Cardiff Sch Technol Cardiff CF5 2YB Wales Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

The issue of sparsity adaptive channel reconstruction in time-varying cooperative communication networks through the amplify-and-forward transmission scheme is studied. A new sparsity adaptive system identification method is proposed, namely reweighted lp norm (0lms) algorithm. The main idea of the algorithm is to add a lp norm penalty of sparsity into the cost function of the lms algorithm. By doing so, the weight factor becomes a balance parameter of the associated lp norm adaptive sparse system identification. Subsequently, the steady state of the coefficient misalignment vector is derived theoretically, with a performance upper bounds provided which serve as a sufficient condition for the lms channel estimation of the precise reweighted lp norm. With the upper bounds, the authors prove that the lp (0algorithm has a better convergence speed and better steady-state behaviour than other lms algorithms.

关键词： wireless channels least mean squares methods computational complexity cooperative communication channel estimation iterative methods signal reconstruction adaptive filters amplify and forward communication lms algorithm norm adaptive sparse system identification coefficient misalignment vector performance upper bounds lms channel estimation norm problem sparse channel vectors p constraint lms-based adaptive sparse channel estimation communication system sparsity adaptive channel reconstruction time-varying cooperative communication networks sparsity adaptive system identification method cost function time-varying cooperative communication networks

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lms-BASED NOTCH FILTER FOR THE ESTIMATION OF SINUSOIDAL SIGNALS IN NOISE

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SIGNAL PROCESSING 1995年第2期46卷 223-231页

作者： XIAO, YG TADOKORO, Y Faculty of Engineering Toyohashi University of Technology Toyohashi-shi 441 Japan

In this paper, the problem of estimating the discrete Fourier coefficients of sinusoidal signals with known arbitrary frequencies in additive noise using a least mean square (lms) adaptive algorithm is treated in some detail. Frequency response analysis reveals that the lms algorithm proposed for a single-frequency case possesses the characteristics of an IIR notch filter. The estimator is analyzed for its convergence and mean square error (MSE) due to the additive noise and the mismatch between the signal and the designated frequencies. Difference equations governing the dynamic behavior of the estimator are also derived. The analyses of a single-frequency case are extended to a multifrequency case. Simulations are performed to confirm the theoretical results.

关键词： lms algorithm IIR NOTCH FILTER MEAN SQUARE ERROR SINUSOIDAL SIGNALS

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