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arXiv · 1408.5845

Analysis of a Reduced-Communication Diffusion LMS Algorithm

Reza Arablouei·Stefan Werner·Kutluyıl Doğançay·Yih-Fang Huang
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Abstract

In diffusion-based algorithms for adaptive distributed estimation, each node of an adaptive network estimates a target parameter vector by creating an intermediate estimate and then combining the intermediate estimates available within its closed neighborhood. We analyze the performance of a reduced-communication diffusion least mean-square (RC-DLMS) algorithm, which allows each node to receive the intermediate estimates of only a subset of its neighbors at each iteration. This algorithm eases the usage of network communication resources and delivers a trade-off between estimation performance and communication cost. We show analytically that the RC-DLMS algorithm is stable and convergent in both mean and mean-square senses. We also calculate its theoretical steady-state mean-square deviation. Simulation results demonstrate a good match between theory and experiment.

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Reza Arablouei, Stefan Werner, Kutluyıl Doğançay, Yih-Fang Huang. 2014-08-25. Analysis of a Reduced-Communication Diffusion LMS Algorithm. https://arxiv.org/abs/1408.5845

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