Abstract
Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a combination framework that aggregates the operation of multiple diffusion strategies for enhanced performance. By assigning a combination coefficient to each node, and using an adaptation mechanism to minimize the network error, we obtain a combined diffusion strategy that benefits from the best characteristics of all component strategies simultaneously in terms of excess-mean-square error (EMSE). Analyses of the universality are provided to show the superior performance of affine combination scheme and to characterize its behavior in the mean and mean-square sense. Simulation results are presented to demonstrate the effectiveness of the proposed strategies, as well as the accuracy of theoretical findings.
Original language | English |
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Article number | 9005223 |
Pages (from-to) | 2087-2104 |
Number of pages | 18 |
Journal | IEEE Transactions on Signal Processing |
Volume | 68 |
DOIs | |
State | Published - 2020 |
Keywords
- adaptive fusion
- affine combination
- diffusion strategy
- Distributed optimization
- stochastic performance