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IEEE COMMUNICATIONS LETTERS 2000年第3期4卷 92-94页

作者： Liang, YC Chin, FPS Ctr Wireless Commun Singapore 117674 Singapore

Pilot Symbol-assisted adaptive algorithms provide coherent detection for communication systems when the filtering coefficients. such as beamforming weights or equalizer coefficients, are converged. This property can be exploited to speed up the convergence of adaptive algorithms used. In this letter, two new adaptive algorithms, coherent least mean square (C-lms) and coherent normalized lms (CN-lms), are proposed by constraining the desired signal component of the filtering output to he always coherent in phase with the reference signal at each iteration. For same adaption step size, these new algorithms provide faster convergence, while yield same steady-state excess mean-square error as compared with their standard lms counterparts.

关键词： coherent detection constraint lms algorithms

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Block-sparsity-aware lms algorithm for network echo cancellation

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ELECTRONICS LETTERS 2018年第15期54卷 951-952页

作者： Wei, Ye Zhang, Yonggang Wang, Chengcheng Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Network echo path impulse response is single-block-sparse in nature. In order to obtain a single-block-sparse estimate of the unknown echo path, a new least mean squares (lms) algorithm is proposed by introducing the penalty of single block sparsity, which is the difference between the mixed l2,1 norm and l2 norm of the uniformly partitioned filter tap-weight vector, into the original mean-squareerror cost function. This is motivated by the fact that the difference between the mixed l2,1 norm and l2 norm of a vector is minimised only when there is at most one non-zero block in the vector. Numerical simulation results show that the proposed algorithm can effectively estimate and track the unknown echo path, outperforming existing block-sparsity-induced lms algorithms.

关键词： adaptive filters echo least mean squares methods acoustic signal processing transient response echo suppression block-sparsity-aware lms algorithm network echo cancellation path impulse response single-block-sparse estimate unknown echo path mean squares algorithm single block sparsity mixed $l_{2\comma 1} norm uniformly partitioned filter tap-weight vector original mean-square-error cost function nonzero block existing block-sparsity lms algorithms

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Tracking Performance and Optimal Adaptation Step-Sizes of Diffusion-lms Networks

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2018年第1期5卷 67-78页

作者： Abdolee, Reza Vakilian, Vida Champagne, Benoit Qualcomm Inc San Diego CA 92121 USA Calif State Univ Dept Comp & Elect Engn & Comp Sci Bakersfield CA 93311 USA McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0E9 Canada

In this paper, we investigate the effect of adaptation step sizes on the tracking performance of diffusion least-mean squares (Dlms) algorithms in networks under nonstationary signal conditions. We assume that the network parameter vector being estimated varies over time according to a first-order random walk model. To find the optimal adaptation step sizes over the network, we formulate a constrained nonlinear optimization problem and solve it through a log-barrier Newton algorithm in an iterative manner. Our studies reveal that the optimal step size of each node in the network not only depends on the statistics of the random walk and the energy profile of the node itself, but also on the energy and statistical profile of its neighboring nodes. The results show that the optimal step sizes can substantially improve the performance of Dlms algorithms in tracking time-varying parameters over networks. We also find that the Dlms algorithms have faster tracking ability and superior steady-state mean-square deviation (MSD) performance than the Dlms in noncorporative mode since the diffusion mode of cooperation, and each node at each iteration can take a larger step toward the network optimal parameters.

关键词： Diffusion adaptation distributed estimation lms algorithms sensor networks time-varying parameters

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A QR-based least mean squares algorithm for adaptive parameter estimation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING 1998年第3期45卷 321-329页

作者： Liu, ZS Li, J Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

The optimum nonlinearly modified least-mean-square (ONM-lms) algorithm has been shown to perform better than both the lms and the normalized lms algorithms. This paper proposes a QR-lms adaptive parameter estimation algorithm that can perform significantly better than ONM-lms. The performances of QR-lms, including its numerical stability, error propagation property, and tracking ability, are analyzed. These properties are also verified with numerical examples.

关键词： adaptive parameter estimation convergence analysis error propagation properties lms algorithms QR-decomposition

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An analysis of real-Fourier domain-based adaptive algorithms implemented with the Hartley transform using cosine-sine symmetries

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2005年第2期53卷 622-629页

作者： Raghavan, V Prabhu, KMM Sommen, PCW Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands Univ Wisconsin Dept Elect & Comp Engn Madison WI 53706 USA

The least mean squared (lms) algorithm and its variants have been the most often used algorithms in adaptive signal processing. However the lms algorithm suffers from a high computational complexity, especially with large filter lengths. The Fourier transform-based block normalized lms (FBNlms) reduces the computation count by using the discrete Fourier transform (DFT) and exploiting the fast algorithms for implementing the DFT. Even though the savings achieved with the FBNlms over the direct-lms implementation are significant, the computational requirements of FBNlms are still very high, rendering many real-time applications, like audio and video estimation, infeasible. The Hartley transform-based BNlms (HBNlms) is found to have a computational complexity much less than, and a memory requirement almost of the same order as, that of the FBNlms. This paper is based on the cosine and sine symmetric implementation of the discrete Hartley transform (DHT), which is the key in-reducing the computational complexity of the FBNlms by 33% asymptotically. (with respect to multiplications). The parallel implementation of the discrete cosine transform (DCT) in turn can lead to more efficient implementations of the HBNlms.

关键词： adaptive algorithms DCT DST FBNlms FFT FHT frequency domain algorithms lms algorithms

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An adaptive penalized maximum likelihood algorithm

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SIGNAL PROCESSING 2008年第9期88卷 2323-2334页

作者： Deng, Guang Ng, Wai-Yin La Trobe Univ Dept Elect Engn Bundoora Vic 3086 Australia Chinese Univ Hong Kong Dept Informat Engn Shatin Hong Kong Peoples R China

The lms algorithm is one of the most popular learning algorithms for identifying an unknown system. Many variants of the algorithm have been developed based on different problem formulations and principles. In this paper, we use the penalized maximum likelihood (PML) as a principled and unified approach for developing lms-type algorithms. We study a general solution to the problem and develop algorithms to address the problems of robustness to impulsive noise and exploiting the sparseness of the system. We perform a statistical analysis of a special case of the proposed algorithm and propose a data-driven method to update the penalty parameter. We also reveal an invariant property of the algorithm. Connections with algorithms based on stochastic gradient descent are also studied. We demonstrate the competitive performance of the proposed algorithms by numerical examples and comparison with recently published algorithms. (c) 2008 Elsevier B.V. All rights reserved.

关键词： lms algorithms learning algorithms penalized maximum likelihood

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Side-Lobe Interference Suppression for DFT Filter Banks based on Improved GSC

Side-Lobe Interference Suppression for DFT Filter Banks base...

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International Conference on Graphic and Image Processing (ICGIP)

作者： Li Guo-lin Li Tao Lu Cui-hua Naval Aeronaut & Astronaut Univ Yantai Peoples R China

ISBN: (纸本)9780819489326

In this paper Generalized Side-Lobe Canceller(GSC) is introduced to cancel strong jamming signals from side-lobes of DFT filter banks. For the ith channel, the canceller uses other channels' output to cancel interferefering signals, and places nulls at stop band adaptively, but the length of the canceller' coefficient vector is restricted to 1, so the canceller just cancels strong interferefering signals, with little influence on desired signals in pass band. The algorithms is based on lms algorithms, but a simplifying method is devised to control the length of coefficient vector, which need no root square and division operations. This method realizes the function of high-order filter banks and especially suited for weak signal detection. Simulations indicate that it reduces computation quantity relative to high-order filter banks but maintains the same performance.

关键词： Electronic warfare receiver Filter banks Interference suppression Generalized Side-Lobe Canceller lms algorithms

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A Brief Survey on Hardware Realization of Two-Dimensional Adaptive Filters

A Brief Survey on Hardware Realization of Two-Dimensional Ad...

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International Conference on VLSI, Communications, and Signal Processing (VCAS)

作者： Shrivastava, Prabhat Chandra Kumar, Prashant Tiwari, Manish Dhawan, Amit MNNIT Dept Elect & Commun Engn Allahabad 211004 Uttar Pradesh India

ISBN: (纸本)9789813297753;9789813297746

The efficient recognition of hardware of two-dimensional (2-D) adaptive filters is an immense problem of present state of art. The concept of the adaptive filter is given by Widrow in the decade of sixty and the mathematical expression of 2-D adaptive filters is introduced by Hadhoud in the decade of ninety. Further, several researchers give the different type of adaptive algorithms for the hardware realization of 2-D adaptive filters. The least mean square (lms) algorithms are too renowned due to its accomplished convergence properties and simplicity to implement in hardware. In this paper, we present a concise compendium of the efficient hardware structure of 2-D adaptive filters.

关键词： 1-D and 2-D adaptive filter lms algorithms Mean square error Normalized lms 2-D lms algorithm

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Dec :: Tech Reports :: Crl-94-7

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2016年

[Auto Generated] Contents 1 Introduction 1 2 Identi cation, Model and Criterion 1 2.1 Identi cation 1 2.2 Model 2 2.3 Criterion 3 3 The Mean-Squared-Error Criterion 4 3.1 The Gradient or Steepest-Descent Method 4 3.2 The Gradient Algorithm 5 3.3 The lms algorithms 6 3.4 Computational Complexity 8 4 Experiments 9 4.1 Evaluation: ERLE Measurements 9 4.2 The Echo-Cancelation Software Lab 9 4.3 Results from the Echo-Cancelation Software Lab 11 4.3.1 Filter Calibration 11 4.3.2 The HA Algorithm 12 4.

关键词： algorithm algorithms computational complexity convergence factor crl-94-7 cycles digital digital equipment echo echo cancelation echo path filter filter coefficients gradient lms lms algorithms model output path impulse reports total number unknown system

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Design/analysis of GEGS4-1 time integration framework with improved stability and solution accuracy for first-order transient systems

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JOURNAL OF COMPUTATIONAL PHYSICS 2020年 422卷 109763-109763页

作者： Wang, Yazhou Maxam, Dean Tamma, Kumar K. Qin, Guoliang Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Peoples R China Univ Minnesota Dept Mech Engn Minneapolis MN 55455 USA

In this work, the fundamental design procedure, termed as algorithms by Design, is exploited to establish novel explicit algorithms under the umbrella of linear multi-step (lms) methods for first-order linear and/or nonlinear transient systems with second-/third-/fourth-order accuracy features. To this end, we focus on developing and designing General Explicit time integration algorithms in an advanced algorithmic fashion typical of the Generalized Single-Step Single-Solve framework for the first-order transient system (GEGS4-1), in which the original GS4-1 has been acknowledged to encompass a wide variety of implicit lms algorithms of second-order accuracy developed over the past few decades. In contrast to the existing explicit lms family of algorithms (specifically, second-/third-/fourth-order Adams-Bashforth methods), the proposed algorithmic framework is a single-step formulation and is proved to significantly improve stability and solution accuracy with rigor via mathematical derivations and numerical demonstrations;Moreover, it does not need any additional numerical techniques, such as Runge-Kutta method, for the starting procedure. New/Optimized algorithms can be generated in the proposed framework to circumvent the stability and accuracy limitation with respect to the classical lms family (not multi-stage method), which is most useful for practical applications. Most significantly, the proposed method readily provides a promising and controllable trade off between stability and accuracy. Specifically, (i) with different selections of free algorithmic parameters, one can recover second-order Adams-Bashforth and Taylor-Galerkin algorithms with critical stability frequency Omega(s) = Delta t(Cr) = 1, third-order Adams-Bashforth algorithm with Omega(s) = 6/11 approximate to 0.5455, and fourth-order Adams-Bashforth algorithm with Omega(s) = 0.3;(ii) new algorithms are originated from the proposed method with improved stability (such as second-order GEGS4-1 with

关键词： General explicit algorithms GS4-1 framework lms algorithms Time integration Improved stability and solution accuracy First-order systems

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