Partial-diffusion recursive least-squares estimation over adaptive networks
Date
2013
Authors
Arablouei, R.
Werner, S.
Dogancay, K.
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Conference paper
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2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013, iss.6714014, pp.89-92
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IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (15 Dec 2013 - 18 Dec 2013 : Saint Martin, France)
Abstract
In the diffusion strategies for distributed estimation over adaptive networks, each node calculates a weighted average of the intermediate parameter estimates of its neighboring nodes. Thus, all the nodes should continuously share their intermediate estimates with their neighbors. In this paper, we consider exchanging a predetermined number of elements of each intermediate estimate vector at each iteration rather than the entire vectors. We examine two different schemes, i.e., stochastic and sequential partial-diffusion schemes, for selecting the to-be-diffused elements at each iteration. Accordingly, we propose a partial-diffusion recursive least-squares (PDRLS) algorithm that can alleviate internode communications at the expense of estimation performance. Simulation results show that the communication-performance trade-off offered by the proposed algorithm is indeed lucrative.
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Copyright 2013 IEEE