One-Step Asynchronous Data Fusion DLMS Algorithm

Yi Hua, Fangyi Wan, Hongping Gan, Bin Liao

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

In recent years distributed estimation has attracted much attention. In traditional distributed algorithms, each node performs data fusion over synchronous data, which causes lots of time consumptions in the actual situations and estimation performance degradation. To deal with this problem, we propose a new one-step asynchronous data fusion strategy in distributed estimation algorithms. Moreover, the proposed algorithms with or without measurement data sharing are studied to provide different asynchronous cooperation strategies. In particular, the convergence behavior of the proposed asynchronous fusion algorithms is analyzed, and why asynchronous fusion can improve estimation performance and reduce time consumptions are also analyzed. The effectiveness of the proposed algorithms is demonstrated through some illustrative examples. Simulation results show that the proposed algorithms considerably outperform the traditional DLMS algorithms and LMS algorithm.

Original languageEnglish
Article number9316763
Pages (from-to)1660-1664
Number of pages5
JournalIEEE Communications Letters
Volume25
Issue number5
DOIs
StatePublished - May 2021

Keywords

  • asynchronous fusion
  • Distribution estimation
  • DLMS algorithm
  • mean performance
  • mean-square performance

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