Frequency-Domain Diffusion Adaptation Over Networks

被引：14

作者：

Zhang, Sheng ^{[1
,2
]}

Zhang, Fenglian ^{[1
,2
]}

Chen, Hongyang ^{[3
]}

Merched, Ricardo ^{[4
]}

Sayed, Ali H. ^{[5
]}

机构：

[1] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Sichuan, Peoples R China

[2] State Key Lab Cryptol, Beijing 100878, Peoples R China

[3] Zhejiang Lab, Res Ctr Intelligent Network, Hangzhou 311121, Peoples R China

[4] Univ Fed Rio de Janeiro, Dept Elect & Comp Engn, BR-21941901 Rio De Janeiro, RJ, Brazil

[5] Ecole Polytech Fed Lausanne, Sch Engn, CH-1015 Lausanne, Switzerland

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2021年 / 69卷

基金：

中国国家自然科学基金;

关键词：

Signal processing algorithms; Frequency-domain analysis; Noise measurement; Convergence; Estimation; Discrete Fourier transforms; Adaptive systems; Adaptive networks; frequency domain; average estimation; steady-state analysis; RECURSIVE LEAST-SQUARES; STEADY-STATE ANALYSIS; LMS ALGORITHM; ADAPTIVE NETWORKS; DISTRIBUTED ESTIMATION; UNIFIED APPROACH; SENSOR NETWORKS; MEAN SQUARES; PERFORMANCE; STRATEGIES;

D O I：

10.1109/TSP.2021.3107622

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：

This paper analyzes the implementation of least-mean-squares (LMS)-based, adaptive diffusion algorithms over networks in the frequency-domain (FD). We focus on a scenario of noisy links and include a moving-average step for denoising after self-learning to enhance performance. The mean-square-error convergence behavior of the resulting algorithm is investigated and the theoretical results are illustrated through simulations. In particular, the proposed denoised recursions are shown to perform favorably when compared with partial diffusion LMS (PD-LMS) and diffusion LMS algorithms, in terms of both complexity and performance.

引用

页码：5419 / 5430

页数：12

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