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Distributed Signal Processing Algorithms for Wireless Networks

This project will investigate novel distributed cooperative algorithms for inference in ad hoc and wireless sensor networks. The goal is to devise low-complexity and effective algorithms for performing inference about the environment in a distributed way. We will consider a number of innovative approaches for dealing with node failures, compression of data and exchange of information. The activities will involve the development of system models, simulation tools and analytical approaches.

We develop dynamic topology strategies based on 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 computational complexity, steady–state and tracking performances. In comparison with existing centralized or distributed estimation strategies, the novelty of the proposed algorithms lies in that: 1) the proposed algorithms have more accurate estimates and a faster convergence speed; and 2) the network is equipped with the ability of dynamic topology adaptation that can circumvent link failures and improve the estimation performance. The performance of the proposed adaptive link selection algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.

Members

  • Songcen Xu
  • Rodrigo C. de Lamare

Dates

  • Start: October 2011

Research