Analysis of a reduced-communication diffusion LMS algorithm

被引：40

作者：

Arablouei, Reza ^{[1
]}

Werner, Stefan ^{[2
]}

Dogancay, Kutluyil ^{[3
]}

Huang, Yih-Fang ^{[4
]}

机构：

[1] CSIRO, Pullenvale, Qld, Australia

[2] Aalto Univ, Sch Elect Engn, Dept Signal Proc & Acoust, Espoo, Finland

[3] Univ S Australia, Sch Engn, Mawson Lakes, SA, Australia

[4] Univ Notre Dame, Dept Elect Engn, Notre Dame, IN 46556 USA

来源：

SIGNAL PROCESSING | 2015年 / 117卷

关键词：

Adaptive networks; Communication reduction; Diffusion adaptation; Distributed estimation; Least mean-square; Performance analysis; STRATEGIES; NETWORKS;

D O I：

10.1016/j.sigpro.2015.06.006

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In diffusion-based algorithms for adaptive distributed estimation, each node of an adaptive network estimates a target parameter vector by creating an intermediate estimate and then combining the intermediate estimates available within its closed neighborhood. We analyze the performance of a reduced-communication diffusion least mean-square (RC-DLMS) algorithm, which allows each node to receive the intermediate estimates from only a subset of its neighbors at each iteration. This algorithm eases the usage of network communication resources and delivers a trade-off between estimation performance and communication cost. We show analytically that the RC-DLMS algorithm is stable and convergent in both mean and mean-square senses. We also calculate its theoretical steady-state mean-square deviation. Simulation results demonstrate a good match between theory and experiment. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：355 / 361

页数：7

共 19 条

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Arablouei, Reza ;

Dogancay, Kutluyil ;

Werner, Stefan ;

Huang, Yih-Fang .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62 (14) :3510-3522

[3]

Arablouei R, 2013, 2013 IEEE 5TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP 2013), P89, DOI 10.1109/CAMSAP.2013.6714014

[4] Distributed Least Mean-Square Estimation With Partial Diffusion [J].