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Distributed low-rank adaptive estimation algorithms based on alternating optimization

机译：基于交替优化的分布式低秩自适应估计算法

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摘要

AbstractThis 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.
机译： 摘要 本文提出了一种新颖的分布式低秩方案和自适应算法，用于无线网络上的分布式估计。所提出的分布式方案基于一种变换，该变换在网络的每个代理处执行降维，然后将减少的参数集传输到其他代理并进行降维的参数估计。开发了基于交替优化策略的分布式低秩联合迭代估计算法，与现有技术相比，该算法可显着降低通信开销并提高性能。提出了所提出的和现有的低秩算法的计算复杂性分析，以及所提出的技术的收敛性分析。仿真结果表明了所提出策略在无线传感器网络和智能电网应用中的性能。

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