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A variable step-size diffusion lms algorithm over networks with noisy links

SIGNAL PROCESSING 2018年 148卷 205-213页

作者： Xia, Wei Wang, Yanyan Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 611731 Sichuan Peoples R China

The variable step-size (VSS) diffusion least mean square (lms) algorithms for wireless sensor networks (WSNs) exhibit accelerated initial convergence rate and decreased steady-state deviation compared with the conventional constant step-size counterparts. However, the performance of the VSS diffusion lms algorithms would be deteriorated if the network links are contaminated by noise. By explicitly considering the link noise impact, we develop a locally sub-optimal step-size scheme for the diffusion lms algorithm, originated from which we propose an asymptotically locally sub-optimal (ALSO) step-size scheme as well as a VSS diffusion lms algorithm. The proposed VSS diffusion lms algorithm is verified through simulations to significantly outperform the current VSS diffusion algorithm in the networks with moderate and high link noise profiles. Simulation results further validate that the proposed VSS diffusion lms algorithm is superior to the current VSS algorithm in the networks with correlated link noise. (C) 2018 Published by Elsevier B.V.

关键词： Adaptive networks Distributed estimation diffusion lms algorithm Variable step-size Noisy links

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Robust competitive diffusion lms algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2020年第2期14卷 343-349页

作者： Wen, Pengwei Zhang, Jiashu Zhang, Sheng Southwest Jiaotong Univ Sichuan Prov Key Lab Signal & Informat Proc Chengdu 610031 Peoples R China

The main drawback of the diffusion least mean square algorithm (Dlms) is its performance will degrade when the impulsive noise (IN) occurs in the system. To improve the performance, a robust competitive Dlms (RCDlms) algorithm based on two types of effective IN detection method is proposed. Because both methods estimate noise variance to determine whether impulse noise occurs or not. The first method is using the shrinkage denoising method to obtain the variance of noise, and the second method is by estimating cross-correlation of the input and error signals to acquire the variance of noise. Finally, simulation results demonstrate that the proposed algorithm has a good tracking capability and robust performance against impulsive noise.

关键词： diffusion lms algorithm IN detection method Robust

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A new robust variable weighting coefficients diffusion lms algorithm

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SIGNAL PROCESSING 2017年第0期131卷 300-306页

作者： Ahn, Do-Chang Lee, Jae-Woo Shin, Seung-Jun Song, Woo-Jin Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang South Korea

We introduce a new robust algorithm that is insensitive to impulsive noise (IN) for distributed estimation problem over adaptive networks. Motivated by the fact that each node can access to multiple spatial data, we propose to discard IN-contaminated data. Under the assumption that IN is successfully detected, we propose a cost function that considers only the uncontaminated data. The derived algorithm is the ATC diffusion lms algorithm that has variable weighting coefficients depending on IN detection, which leads both to insensitivity to IN and to good estimation performance. A method to detect IN is also presented. Simulation results show that the proposed algorithm has good estimation performance in an environment that is subject to IN, and outperforms the conventional robust algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Adaptive networks Distributed estimation Impulsive noise Robust algorithm diffusion lms algorithm

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A Variable Step-Size diffusion lms algorithm for Distributed Estimation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第7期63卷 1808-1820页

作者： Lee, Han-Sol Kim, Seong-Eun Lee, Jae-Woo Song, Woo-Jin Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang 790784 South Korea

We propose a new variable step-size diffusion least mean square algorithm for distributed estimation that adaptively adjusts the step-size in every iteration. For a network application, the proposed method determines a suboptimal step-size at each node to minimize the mean square deviation for the intermediate estimate. The algorithm thus adapts the different node environments and profiles across the networks, and requires relatively less user interaction than existing algorithms. In experiments, the algorithm achieves both fast convergence speed and low misadjustment by remarkable improvement in an adaptation stage. We analyze the mean square performance of the proposed algorithm. Also, the proposed algorithm works well even in non-stationary environments.

关键词： Distributed estimation adaptive networks diffusion lms algorithm variable step-size

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diffusion Robust Variable Step-Size lms algorithm Over Distributed Networks

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IEEE ACCESS 2018年 6卷 47511-47520页

作者： Huang, Wei Li, Lindong Li, Qiang Yao, Xinwei Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China

In this paper, we propose a novel diffusion robust variable step-size least mean square (DRVSS-lms) algorithm that is insensitive to impulsive noise for distributed estimation in the network. Conventional diffusion least mean square algorithms are based on the assumption that the background noise obeys Gaussian distribution. However, the performances of these algorithms are severely degraded when impulsive noises occur in the network. By introducing the Huber objective function which can significantly suppress the effect of impulsive noise on estimation performances, we introduce a novel method to respectively deal with the abnormal nodes carrying data contaminated by impulsive noise and the normal nodes without being contaminated by impulsive noise. In addition, the proposed algorithm is assigned with variable step-sizes to further improve the performances of distributed estimation. Simulation results show that the proposed DRVSS-lms algorithm can achieve both higher convergence rate and lower steady-state misadjustment than several existing robust diffusion lms algorithms in the presence of impulsive noise.

关键词： Distributed estimation diffusion lms algorithm impulsive noise Huber objective function robust algorithm

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A new diffusion variable spatial regularized lms algorithm

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SIGNAL PROCESSING 2021年 188卷 108207-108207页

作者： Chu, Y. J. Chan, S. C. Zhou, Y. Wu, M. South China Univ Technol State Key Lab Subtrop Bldg Sci Guangzhou Guangdong Peoples R China Uinivers Hong Kong Dept Elect & Elect Engn Pok Fu Lam Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China Inst Acoust Key Lab Noise & Vibrat Res Beijing Peoples R China

This paper develops a new diffusion (Diff) least mean squares (lms) algorithm for the identification of a network of systems that have distinct parameters at each node. The mean and mean squares behavior of the Diff-lms algorithm in the so called multitask environment is studied in order to obtain an explicit expression of the estimation bias and variance in terms of the spatial regularization (SR) parameter. An optimal SR formula for the Diff lms algorithm is then derived via minimizing the estimation error. An approximation is made to the formula such that a new practical Diff variable SR lms (Diff-VSR-lms) algorithm is obtained. This paper also provides a framework for the design of other lms-like algorithms that incorporate diffusion technology to solve multitask problems. The theoretical analysis is evaluated via computer simulations and the performance of the proposed algorithm is compared with conventional Diff lms algorithms under the multitask environment. (c) 2021 Elsevier B.V. All rights reserved.

关键词： diffusion lms algorithm Variable spatial regularization Performance analysis

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diffusion lms with component-wise variable step-size over sensor networks

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IET SIGNAL PROCESSING 2016年第1期10卷 37-45页

作者： Huang, Wei Yang, Xi Liu, Duanyang Chen, Shengyong Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China

In this study, the authors propose a novel component-wise variable step-size (CVSS) diffusion distributed algorithm for estimating a specific parameter over sensor networks. The novelty of the CVSS algorithm is that step-sizes vary from each other on different components at each iteration. They derive the steady-state value of global mean-square deviation (MSD) and relative MSD (RMSD). In the numerical simulations, they compare the proposed CVSS algorithm with several other least mean square (lms) algorithms. Results show that, when compared with these other algorithms, the CVSS algorithm can effectively reduce steady-state value and speed up convergence rate of RMSD while not sacrificing the convergence rate of MSD. Results also reveal that the proposed CVSS algorithm can achieve reduced difference of steady-state values of relative estimation error on various components.

关键词： least mean squares methods wireless sensor networks distributed algorithms convergence of numerical methods component wise variable step size diffusion lms algorithm CVSS diffusion distributed algorithm parameter estimation sensor network mean square deviation global MSD relative MSD RMSD steady-state value convergence rate relative estimation error

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Distributed diffusion bias-compensated lms for node-specific networks

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SIGNAL PROCESSING 2019年 160卷 21-31页

作者： Jia, Lijuan Zheng, Chenxue Katerega, Albert Yang, Zi-Jiang Beijing Inst Technol Dept Elect Engn 5 South Zhongguancun St Beijing 100081 Peoples R China Ibaraki Univ Dept Intelligent Syst Engn Ibaraki Japan

In this paper, we study the problem of node-specific parameter estimation(NSPE) over distributed multi-agent networks, whose nodes have noise-corrupted regressor vectors. When the classic diffusion least mean square(lms) algorithm is used in this situation, it results biased estimates of the nodal objectives. Therefore, we propose an online bias-compensated method to remove the bias introduced on the diffusion lms results. Moreover, we investigate performance analysis in the mean and mean-square sense. Furthermore, we provide numerical experiments to illustrate and compare the robustness of our method under various distributed strategies and different network topologies. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Bias-compensated Node-specific parameter estimation diffusion lms algorithm Regressor noise variance Performance analysis

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A noise-constrained algorithm for estimation over distributed networks

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2013年第10期27卷 827-845页

作者： Bin Saeed, Muhammad O. Zerguine, Azzedine Zummo, Salam A. King Fahd Univ Petr & Minerals Dhahran 31261 Saudi Arabia

Much research has been devoted recently to the development of algorithms to utilize the distributed structure of an ad hoc wireless sensor network for the estimation of a certain parameter of interest. A successful solution is the algorithm called the diffusion least mean squares algorithm. The algorithm estimates the parameter of interest by employing cooperation between neighboring sensor nodes within the network. The present work derives a new algorithm by using the noise constraint that is based on and improves the diffusion least mean squares algorithm. In this work, first the derivation of the noise constraint-based algorithm is given. Second, detailed convergence and steady-state analyses are carried out, including analyses for the case where there is mismatch in the noise variance estimate. Finally, extensive simulations are carried out to test the robustness of the proposed algorithm under different scenarios, especially the mismatch scenario. Moreover, the simulation results are found to corroborate the theoretical results very well. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： noise-constrained lms algorithm diffusion lms algorithm distributed networks

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diffusion Least-Mean Squares over Distributed Networks in the Presence of MAC Errors

Diffusion Least-Mean Squares over Distributed Networks in th...

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46th Asilomar Conference on Signals, Systems and Computers

作者： Ghazanfari-Rad, Saeed Labeau, Fabrice McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0E9 Canada

ISBN: (纸本)9781467350518

This paper presents the formulation and steady-state analysis of the distributed estimation algorithms based on diffusion cooperation scheme, in which all nodes in the network communicate employing a non-ideal channel access mechanism. We formulate and study a two-node network and derive the closed-form expressions of the steady-state mean-square deviation (MSD). We also assess the mean performance and stability condition. The proposed analytical framework enables us to investigate the effects of the medium access control (MAC) layer performance on the behavior of the diffusion least-mean squares (lms) algorithm in terms of the convergence speed and the steady-state error that is validated by performing Monte Carlo simulations. Simulation and analysis confirm that a high probability of collision at the MAC level, results in lower convergence speed and higher steady-state MSD for distributed estimation algorithm.

关键词： Adaptive networks diffusion lms algorithm MAC layer distributed estimation distributed signal processing

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