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Distributed low-rank adaptive estimation algorithms based on alternating optimization

Author(s): Xu, Songcen; de Lamare, Rodrigo C; Vincent Poor, H

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Abstract: This paper presents a novel distributed low-rank scheme and adaptive algorithms for distributed estimation over wireless networks. The proposed distributed scheme is based on a transformation that performs dimensionality reduction at each agent of the network followed by transmission of a reduced set of parameters to other agents and reduced-dimension parameter estimation. Distributed low-rank joint iterative estimation algorithms based on alternating optimization strategies are developed, which can achieve significantly reduced communication overhead and improved performance when compared with existing techniques. A computational complexity analysis of the proposed and existing low-rank algorithms is presented along with an analysis of the convergence of the proposed techniques. Simulations illustrate the performance of the proposed strategies in applications of wireless sensor networks and smart grids.
Publication Date: 2018
Citation: Xu, Songcen, de Lamare, Rodrigo C, Vincent Poor, H. (2018). Distributed low-rank adaptive estimation algorithms based on alternating optimization. Signal Processing, 144 (41 - 51. doi:10.1016/j.sigpro.2017.09.023
DOI: doi:10.1016/j.sigpro.2017.09.023
ISSN: 0165-1684
Pages: 41 - 51
Language: en
Type of Material: Journal Article
Journal/Proceeding Title: Signal Processing
Version: Author's manuscript

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