7,680 research outputs found
Diffusion Adaptation Strategies for Distributed Estimation over Gaussian Markov Random Fields
The aim of this paper is to propose diffusion strategies for distributed
estimation over adaptive networks, assuming the presence of spatially
correlated measurements distributed according to a Gaussian Markov random field
(GMRF) model. The proposed methods incorporate prior information about the
statistical dependency among observations, while at the same time processing
data in real-time and in a fully decentralized manner. A detailed mean-square
analysis is carried out in order to prove stability and evaluate the
steady-state performance of the proposed strategies. Finally, we also
illustrate how the proposed techniques can be easily extended in order to
incorporate thresholding operators for sparsity recovery applications.
Numerical results show the potential advantages of using such techniques for
distributed learning in adaptive networks deployed over GMRF.Comment: Submitted to IEEE Transactions on Signal Processing. arXiv admin
note: text overlap with arXiv:1206.309
Distributed Adaptive Learning of Graph Signals
The aim of this paper is to propose distributed strategies for adaptive
learning of signals defined over graphs. Assuming the graph signal to be
bandlimited, the method enables distributed reconstruction, with guaranteed
performance in terms of mean-square error, and tracking from a limited number
of sampled observations taken from a subset of vertices. A detailed mean square
analysis is carried out and illustrates the role played by the sampling
strategy on the performance of the proposed method. Finally, some useful
strategies for distributed selection of the sampling set are provided. Several
numerical results validate our theoretical findings, and illustrate the
performance of the proposed method for distributed adaptive learning of signals
defined over graphs.Comment: To appear in IEEE Transactions on Signal Processing, 201
Distributed Diffusion-Based LMS for Node-Specific Adaptive Parameter Estimation
A distributed adaptive algorithm is proposed to solve a node-specific
parameter estimation problem where nodes are interested in estimating
parameters of local interest, parameters of common interest to a subset of
nodes and parameters of global interest to the whole network. To address the
different node-specific parameter estimation problems, this novel algorithm
relies on a diffusion-based implementation of different Least Mean Squares
(LMS) algorithms, each associated with the estimation of a specific set of
local, common or global parameters. Coupled with the estimation of the
different sets of parameters, the implementation of each LMS algorithm is only
undertaken by the nodes of the network interested in a specific set of local,
common or global parameters. The study of convergence in the mean sense reveals
that the proposed algorithm is asymptotically unbiased. Moreover, a
spatial-temporal energy conservation relation is provided to evaluate the
steady-state performance at each node in the mean-square sense. Finally, the
theoretical results and the effectiveness of the proposed technique are
validated through computer simulations in the context of cooperative spectrum
sensing in Cognitive Radio networks.Comment: 13 pages, 6 figure
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