Distributed estimation over complex networks

  • Authors:

  • Ying Liu;Chunguang Li;Wallace K. S. Tang;Zhaoyang Zhang

  • Affiliations:

  • Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, PR China;Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, PR China;Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, PR China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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Abstract

Distributed estimation is an appealing technique for in-network signal processing. In this paper, we investigate the impacts of network topology on the performance of a distributed estimation algorithm, namely adaptive-then-combine diffusion LMS, based on the data with or without the temporal and spatial independence assumptions. The study covers different network models, including the regular, the small-world, the random and the scale-free, while the performance is analyzed according to the mean stability, mean-square errors, communication cost and robustness. Simulation results show that the estimation performance is largely dependent on the topological properties of the networks, such as the average path length, the clustering coefficient and the degree distribution, indicating that the network topology indeed plays an important role in distributed estimation. From the design point of view, this study also provides some guidelines on how to design a network such that the qualities of estimates are optimized.