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Diffusion LMS with component-wise variable step-size over sensor networks

机译:在传感器网络上具有分量可变步长的扩散LMS

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In this study, the authors propose a novel - 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.
机译:在这项研究中,作者提出了一种新颖的-可变步长(CVSS)扩散分布式算法,用于估计传感器网络上的特定参数。 CVSS算法的新颖之处在于步长在每次迭代时在不同的组件上彼此不同。他们得出全局均方差(MSD)和相对MSD(RMSD)的稳态值。在数值模拟中,他们将提出的CVSS算法与其他几种最小均方(LMS)算法进行了比较。结果表明,与其他算法相比,CVSS算法可以有效地减小稳态值,加快RMSD的收敛速度,同时又不牺牲MSD的收敛速度。结果还表明,所提出的CVSS算法可以减小各个组件上相对估计误差的稳态值差异。

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