12,586 research outputs found
Multitask Diffusion Adaptation over Networks
Adaptive networks are suitable for decentralized inference tasks, e.g., to
monitor complex natural phenomena. Recent research works have intensively
studied distributed optimization problems in the case where the nodes have to
estimate a single optimum parameter vector collaboratively. However, there are
many important applications that are multitask-oriented in the sense that there
are multiple optimum parameter vectors to be inferred simultaneously, in a
collaborative manner, over the area covered by the network. In this paper, we
employ diffusion strategies to develop distributed algorithms that address
multitask problems by minimizing an appropriate mean-square error criterion
with -regularization. The stability and convergence of the algorithm in
the mean and in the mean-square sense is analyzed. Simulations are conducted to
verify the theoretical findings, and to illustrate how the distributed strategy
can be used in several useful applications related to spectral sensing, target
localization, and hyperspectral data unmixing.Comment: 29 pages, 11 figures, submitted for publicatio
Distributed Estimation and Control of Algebraic Connectivity over Random Graphs
In this paper we propose a distributed algorithm for the estimation and
control of the connectivity of ad-hoc networks in the presence of a random
topology. First, given a generic random graph, we introduce a novel stochastic
power iteration method that allows each node to estimate and track the
algebraic connectivity of the underlying expected graph. Using results from
stochastic approximation theory, we prove that the proposed method converges
almost surely (a.s.) to the desired value of connectivity even in the presence
of imperfect communication scenarios. The estimation strategy is then used as a
basic tool to adapt the power transmitted by each node of a wireless network,
in order to maximize the network connectivity in the presence of realistic
Medium Access Control (MAC) protocols or simply to drive the connectivity
toward a desired target value. Numerical results corroborate our theoretical
findings, thus illustrating the main features of the algorithm and its
robustness to fluctuations of the network graph due to the presence of random
link failures.Comment: To appear in IEEE Transactions on Signal Processin
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