A Sampling Algorithm for Diffusion Networks

被引：0
作者：
Tiglea, Daniel G. [1 ]
Candido, Renato [1 ]
Silva, Magno T. M. [1 ]
机构：
[1] Univ Sao Paulo, Escola Politecn, Sao Paulo, Brazil
来源：
28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020) | 2021年
基金：
巴西圣保罗研究基金会;
关键词：
Diffusion strategies; energy efficiency; adaptive networks; distributed estimation; convex combination; LEAST-MEAN SQUARES; FORMULATION;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
In this paper, we propose a sampling mechanism for adaptive diffusion networks that adaptively changes the amount of sampled nodes based on mean-squared error in the neighborhood of each node. It presents fast convergence during transient and a significant reduction in the number of sampled nodes in steady state. Besides reducing the computational cost, the proposed mechanism can also be used as a censoring technique, thus saving energy by reducing the amount of communication between nodes. We also present a theoretical analysis to obtain lower and upper bounds for the number of network nodes sampled in steady state.
引用
收藏
页码：2175 / 2179
页数：5
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