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This paper presents a novel distributed low-rank scheme and adaptivealgorithms for distributed estimation over wireless networks. The proposeddistributed scheme is based on a transformation that performs dimensionalityreduction at each agent of the network followed by transmission of a reducedset of parameters to other agents and reduced-dimension parameter estimation.Distributed low-rank joint iterative estimation algorithms based on alternatingoptimization strategies are developed, which can achieve significantly reducedcommunication overhead and improved performance when compared with existingtechniques. A computational complexity analysis of the proposed and existinglow-rank algorithms is presented along with an analysis of the convergence ofthe proposed techniques. Simulations illustrate the performance of the proposedstrategies in applications of wireless sensor networks and smart grids.

Distributed Low-Rank Adaptive Algorithms Based on Alternating Optimization and Applications