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Analysis of a reduced-communication diffusion LMS algorithm
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DOI：10.1016/j.sigpro.2015.06.006.png)
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Abstract
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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 from 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. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Adaptive networks
Communication reduction
Diffusion adaptation
Distributed estimation
Least mean-square
Performance analysis
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