1,761 research outputs found
Sparse Distributed Learning Based on Diffusion Adaptation
This article proposes diffusion LMS strategies for distributed estimation
over adaptive networks that are able to exploit sparsity in the underlying
system model. The approach relies on convex regularization, common in
compressive sensing, to enhance the detection of sparsity via a diffusive
process over the network. The resulting algorithms endow networks with learning
abilities and allow them to learn the sparse structure from the incoming data
in real-time, and also to track variations in the sparsity of the model. We
provide convergence and mean-square performance analysis of the proposed method
and show under what conditions it outperforms the unregularized diffusion
version. We also show how to adaptively select the regularization parameter.
Simulation results illustrate the advantage of the proposed filters for sparse
data recovery.Comment: to appear in IEEE Trans. on Signal Processing, 201
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
Distributed Coupled Multi-Agent Stochastic Optimization
This work develops effective distributed strategies for the solution of
constrained multi-agent stochastic optimization problems with coupled
parameters across the agents. In this formulation, each agent is influenced by
only a subset of the entries of a global parameter vector or model, and is
subject to convex constraints that are only known locally. Problems of this
type arise in several applications, most notably in disease propagation models,
minimum-cost flow problems, distributed control formulations, and distributed
power system monitoring. This work focuses on stochastic settings, where a
stochastic risk function is associated with each agent and the objective is to
seek the minimizer of the aggregate sum of all risks subject to a set of
constraints. Agents are not aware of the statistical distribution of the data
and, therefore, can only rely on stochastic approximations in their learning
strategies. We derive an effective distributed learning strategy that is able
to track drifts in the underlying parameter model. A detailed performance and
stability analysis is carried out showing that the resulting coupled diffusion
strategy converges at a linear rate to an neighborhood of the true
penalized optimizer
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
Adjustable dynamic range for paper reduction schemes in large-scale MIMO-OFDM systems
In a multi-input-multi-output (MIMO) communication system there is a necessity to limit the power that the output antenna amplifiers can deliver. Their signal is a
combination of many independent channels, so the demanded amplitude can peak to many times the average value. The orthogonal frequency division multiplexing
(OFDM) system causes high peak signals to occur because many subcarrier components are added by an inverse discrete Fourier transformation process at the base station. This causes out-of-band spectral regrowth. If simple clipping of the input signal is used, there will be in-band distortions in the transmitted signals and the bit error rate will increase substantially.
This work presents a novel technique that reduces the peak-to-average power ratio (PAPR). It is a combination of two main stages, a variable clipping level and an
Adaptive Optimizer that takes advantage of the channel state information sent from all users in the cell.
Simulation results show that the proposed method achieves a better overall system performance than that of conventional peak reduction systems in terms of the symbol
error rate. As a result, the linear output of the power amplifiers can be minimized with a great saving in cost
Diffusion Adaptation Strategies for Distributed Optimization and Learning over Networks
We propose an adaptive diffusion mechanism to optimize a global cost function
in a distributed manner over a network of nodes. The cost function is assumed
to consist of a collection of individual components. Diffusion adaptation
allows the nodes to cooperate and diffuse information in real-time; it also
helps alleviate the effects of stochastic gradient noise and measurement noise
through a continuous learning process. We analyze the mean-square-error
performance of the algorithm in some detail, including its transient and
steady-state behavior. We also apply the diffusion algorithm to two problems:
distributed estimation with sparse parameters and distributed localization.
Compared to well-studied incremental methods, diffusion methods do not require
the use of a cyclic path over the nodes and are robust to node and link
failure. Diffusion methods also endow networks with adaptation abilities that
enable the individual nodes to continue learning even when the cost function
changes with time. Examples involving such dynamic cost functions with moving
targets are common in the context of biological networks.Comment: 34 pages, 6 figures, to appear in IEEE Transactions on Signal
Processing, 201
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