1,272 research outputs found
Distributed Diffusion-based LMS for Node-Specific Parameter Estimation over Adaptive Networks
A distributed adaptive algorithm is proposed to solve a node-specific
parameter estimation problem where nodes are interested in estimating
parameters of local interest 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 or global parameters. Although all the different LMS
algorithms are coupled, the diffusion-based implementation of each LMS
algorithm is exclusively undertaken by the nodes of the network interested in a
specific set of local or global parameters. To illustrate the effectiveness of
the proposed technique we provide simulation results in the context of
cooperative spectrum sensing in cognitive radio networks.Comment: 5 pages, 2 figures, Published in Proc. IEEE ICASSP, Florence, Italy,
May 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
Proximal Multitask Learning over Networks with Sparsity-inducing Coregularization
In this work, we consider multitask learning problems where clusters of nodes
are interested in estimating their own parameter vector. Cooperation among
clusters is beneficial when the optimal models of adjacent clusters have a good
number of similar entries. We propose a fully distributed algorithm for solving
this problem. The approach relies on minimizing a global mean-square error
criterion regularized by non-differentiable terms to promote cooperation among
neighboring clusters. A general diffusion forward-backward splitting strategy
is introduced. Then, it is specialized to the case of sparsity promoting
regularizers. A closed-form expression for the proximal operator of a weighted
sum of -norms is derived to achieve higher efficiency. We also provide
conditions on the step-sizes that ensure convergence of the algorithm in the
mean and mean-square error sense. Simulations are conducted to illustrate the
effectiveness of the strategy
A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation
We consider a multitask estimation problem where nodes in a network are
divided into several connected clusters, with each cluster performing a
least-mean-squares estimation of a different random parameter vector. Inspired
by the adapt-then-combine diffusion strategy, we propose a multitask diffusion
strategy whose mean stability can be ensured whenever individual nodes are
stable in the mean, regardless of the inter-cluster cooperation weights. In
addition, the proposed strategy is able to achieve an asymptotically unbiased
estimation, when the parameters have same mean. We also develop an
inter-cluster cooperation weights selection scheme that allows each node in the
network to locally optimize its inter-cluster cooperation weights. Numerical
results demonstrate that our approach leads to a lower average steady-state
network mean-square deviation, compared with using weights selected by various
other commonly adopted methods in the literature.Comment: 30 pages, 8 figures, submitted to IEEE Journal of Selected Topics in
Signal Processin
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