Adaptive link selection algorithms for distributed estimation

Songcen Xu, Rodrigo C. de Lamare, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search-based least mean squares (LMS) / recursive least squares (RLS) link selection algorithms and sparsity-inspired LMS / RLS link selection algorithms that can exploit the topology of networks with poor-quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady-state, and tracking performance and computational complexity. In comparison with the existing centralized or distributed estimation strategies, the key features of the proposed algorithms are as follows: (1) more accurate estimates and faster convergence speed can be obtained and (2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.

Original languageEnglish (US)
Article number86
JournalEurasip Journal on Advances in Signal Processing
Issue number1
StatePublished - Dec 1 2015

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Electrical and Electronic Engineering


  • Adaptive link selection
  • Distributed estimation
  • Smart grids
  • Wireless sensor networks


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